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A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses

Implementing compact, low-power artificial neural processing systems with real-time on-line learning abilities is still an open challenge. In this paper we present a full-custom mixed-signal VLSI device with neuromorphic learning circuits that emulate the biophysics of real spiking neurons and dynamic synapses for exploring the properties of computational neuroscience models and for building brain-inspired computing systems. The proposed architecture allows the on-chip configuration of a wide range of network connectivities, including recurrent and deep networks, with short-term and long-term plasticity. The device comprises 128 K analog synapse and 256 neuron circuits with biologically plausible dynamics and bi-stable spike-based plasticity mechanisms that endow it with on-line learning abilities. In addition to the analog circuits, the device comprises also asynchronous digital logic circuits for setting different synapse and neuron properties as well as different network configurations. This prototype device, fabricated using a 180 nm 1P6M CMOS process, occupies an area of 51.4 mm2, and consumes approximately 4 mW for typical experiments, for example involving attractor networks. Here we describe the details of the overall architecture and of the individual circuits and present experimental results that showcase its potential. By supporting a wide range of cortical-like computational modules comprising plasticity mechanisms, this device will enable the realization of intelligent autonomous systems with on-line learning capabilities.

Introduction
Recent advances in neural network modeling and theory, combined with advances in technology and computing power, are producing impressive results in a wide range of application domains. For example, large-scale deep-belief neural networks and convolutional networks now represent the state-of-the-art for speech recognition and image segmentation applications (Mohamed et al., 2012;Farabet et al., 2013). However, the mostly sequential and synchronous clocked nature of conventional computing platforms is not optimally suited for the implementation of these types of massively parallel neural network architectures. For this reason a new generation of custom neuro-computing hardware systems started to emerge. These systems are typically composed of custom Very Large Scale Integration (VLSI) chips that either contain digital processing cores with dedicated memory structures and communication schemes optimized for spiking neural networks architectures (Wang et al., 2013;Furber et al., 2014;Neil and Liu, 2014), or full-custom digital circuit solutions that implement large arrays of spiking neurons with programmable synaptic connections (Merolla et al., 2014). While these devices and systems have high potential for solving machine learning tasks and applied research problems, they do not emulate directly the dynamics of real neural systems.
At the other end of the spectrum, neuromorphic engineering researchers have been developing hardware implementations of detailed neural models, using mixed signal analog-digital circuits to reproduce faithfully neural and synaptic dynamics, in a basic research effort to understand the principles of neural computation in physical hardware systems (Douglas et al., 1995;Liu et al., 2002;Chicca et al., 2014). By studying the physics of computation of neural systems, and reproducing it through the physics of transistors biased in the subthreshold regime (Liu et al., 2002), neuromorphic engineering seeks to emulate biological neural computing systems efficiently, using the least amount of power and silicon real-estate possible. Examples of biophysically realistic neural electronic circuits built following this approach range from models of single neurons (Mahowald and Douglas, 1991;Farquhar and Hasler, 2005;Hynna and Boahen, 2007;van Schaik et al., 2010), to models of synaptic dynamics (Liu, 2003;Bartolozzi and Indiveri, 2007a;Xu et al., 2007), to auditory/visual sensory systems (Sarpeshkar et al., 1996;van Schaik and Meddis, 1999;Zaghloul and Boahen, 2004;Costas-Santos et al., 2007;Liu and Delbruck, 2010), to reconfigurable spiking neural network architectures with learning and plasticity (Giulioni et al., 2008;Hsieh and Tang, 2012;Ramakrishnan et al., 2012;Yu et al., 2012;Chicca et al., 2014).
In this paper we propose to combine the basic research efforts with the applied research ones, by presenting a VLSI architecture that can be used to both carry out research experiments in computational neuroscience, and to develop application solutions for practical tasks. The architecture proposed comprises electronic neuromorphic circuits that directly emulate the physics of real neurons and synapses to faithfully reproduce their adaptive and dynamic behavior, together with digital logic circuits that can set both the properties of the individual synapse and neuron elements as well as the topology of the neural network. In particular, this architecture has been developed to implement spike-based adaptation and plasticity mechanisms, and to carry out on-chip on-line learning for tasks that require the system to adapt to the changes in the environment it interacts with. Given these characteristics, including the ability to arbitrarily reconfigure the network topology also at run-time, we named this device the Reconfigurable On-line Learning Spiking Neuromorphic Processor (ROLLS neuromorphic processor).
The main novelty of the work proposed, compared to previous analogous approaches (Indiveri et al., 2006;Giulioni et al., 2008;Ramakrishnan et al., 2012;Yu et al., 2012) consists in the integration of analog bi-stable learning synapse circuits with asynchronous digital logic cells and in the embedding of these mixed-signal blocks in a large multi-neuron architecture. The combination of analog and digital circuits, with both analog and digital memory elements, within the same block provides the device with an important set of programmable features, including the ability to configure arbitrary network connectivity schemes. At the analog circuit design level, we present improvements in the neuron and spike-based learning synapses over previously proposed ones (Indiveri et al., 2011;Chicca et al., 2014), which extend their range of behaviors and significantly reduce device mismatch effects. At the system application level we demonstrate, for the first time, both computational neuroscience models of attractor networks and image classification neural networks implemented exclusively on custom mixed-signal analog-digital neuromorphic hardware, with no extra pre-or post-processing done in software. In the next section we describe the ROLLS neuromorphic processor system-level block diagram, highlighting its dynamic and spike-based learning features. In Section 2.2 we describe in detail the circuits that are present in each building block, and in Section 3 we present system level experimental results showcasing examples of both computational neuroscience models and machine vision pattern recognition tasks. Finally, in Sections 4, 5 we discuss the results obtained and summarize our contribution with concluding remarks.

The Neuromorphic Processor Architecture
The block-diagram of the ROLLS neuromorphic processor architecture is shown in Figure 1. The device comprises a configurable array of synapse circuits that produce biologically realistic response properties and spiking neurons that can exhibit a wide range of realistic behaviors. Specifically, this device comprises a row of 256×1 silicon neuron circuits, an array of 256×256 learning synapse circuits for modeling long-term plasticity mechanisms, an array of 256×256 programmable synapses with short-term plasticity circuits, a 256×2 row of linear integrator filters denoted as "virtual synapses" for modeling excitatory and inhibitory synapses that have shared synaptic weights and time constants, and additional peripheral analog/digital Input/Output (I/O) circuits for both receiving and transmitting spikes in real-time off-chip.
The ROLLS neuromorphic processor was fabricated using a standard 180 nm Complementary Metal-Oxide-Semiconductor (CMOS) 1P6M process. It occupies an areas of 51.4 mm 2 and has approximately 12.2 million transistors. The die photo of the chip is shown in Figure 2. The area distribution of main circuit blocks is shown in Table 1. The silicon neurons contain circuits that implement a model of the adaptive exponential Integrate-and-Fire (I&F) neuron (Brette and Gerstner, 2005), post-synaptic learning circuits used to implement the spike-based weight-update/plasticity mechanism in the array of long-term plasticity synapses, and analog circuits that model homeostatic synaptic scaling mechanisms operating on very long time scales (Rovere et al., 2014). The array of long-term plasticity synapses comprises pre-synaptic spike-based learning circuits FIGURE 1 | Architecture of ROLLS neuromorphic processor. (A) Block diagram of the architecture, showing two distinct synapse arrays (short-term plasticity and long-term plasticity synapses), an additional row of synapses (virtual synapses) and a row of neurons (somas). A synapse de-multiplexer block is used to connect the rows from the synapse arrays to the neurons (see main text for details). Peripheral circuits include asynchronous digital AER logic blocks, an Analog-to-Digital converter, and a programmable on-chip bias-generator. (B) Block-diagram legend.
FIGURE 2 | Micro-photograph of the ROLLS neuromorphic processor. The chip was fabricated using a 180 nm CMOS process and occupies an area of 51.4 mm 2 , comprising 12.2 million transistors.
with bi-stable synaptic weights, that can undergo either Long-Term Potentiation (LTP) or Long-Term Depression (LTD), (see Section 2.1.2 for details). The array of Short-Term Plasticity (STP) synapses comprises synapses with programmable weights and STP circuits that reproduce short-term adaptation dynamics. Both arrays contain analog integrator circuits that implement faithful models of synaptic temporal dynamics (see Section 2.1.1). Digital configuration logic in each of the synapse and neuron circuits allows the user to program the properties of the synapses, the topology of the network, and the properties of the neurons.
The architecture comprises also a "synapse de-multiplexer" static logic circuit, which allows the user to choose how many rows of plastic synapses should be connected to the neurons. It is a programmable switch-matrix that configures the connectivity between the synapse rows and the neuron columns. By default, each of the 256 rows of 1×512 synapses is connected to its corresponding neuron. By changing the circuit control bits it is possible to allocate multiple synapse rows to the neurons, thereby disconnecting and sacrificing the unused neurons. In the extreme case all 256×512 synapses are assigned to a single neuron, and the remaining 255 neurons remain unused. An on-chip programmable bias generator, optimized for subthreshold circuits  is used to set all of the bias currents that control the parameters of the synapses and neurons (such as time constants, leak currents, etc.).
An Analog to Digital Converter (ADC) circuit converts the subthreshold currents produced by selected synapse and neuron circuits into a stream of voltage pulses, using a linear pulsefrequency-modulation scheme, and transmits them off-chip as digital signals.
Finally, peripheral asynchronous I/O logic circuits are used for receiving input spikes and transmitting output ones, using the Address-Event Representation (AER) communication protocol (Deiss et al., 1998;Boahen, 2000).

Synapse Temporal Dynamics
In the ROLLS neuromorphic processor all synapses process input spikes in real-time, as they arrive. Similarly the neurons transmit the spikes they produce immediately, as they are generated. In these types of architectures time represents itself and input data is processed instantaneously. There is no virtualization of time and no mechanism for storing partial results in memory banks. As a consequence, the circuits must operate with time-constants that are well-matched to those of the signals they are designed to process. Since this device is intended to be used in behaving systems that interact with the environment in natural real-world scenarios, it is important to design circuits that can implement a wide range of time constants, including very slow, biologically plausible, ones. To achieve this, and to model neural dynamics with biologically plausible time constants, we used the Differential Pair Integrator (DPI) (Bartolozzi and Indiveri, 2007b). This is a current-mode log-domain integrator. When biased in the subthreshold regime, this circuit can obtain long time constants, even with relatively small and compact capacitors. For example, in the 180 nm technology used, with a capacitor of 1 pF, we could obtain time constants of the order of tens of milliseconds without resorting to any advanced design techniques. However, to realize even longer time constants (e.g., of the order of hundreds of milliseconds), we used a shifted-source biasing technique, as described in Linares- Barranco and Serrano-Gotarredona (2003).
The synapse circuits in the two synapse arrays of the ROLLS neuromorphic processor convert input voltage spikes into output currents which have non-linear dynamics, due to their adaptation or learning features. In addition, to model the synapse temporal dynamics, the currents produced by the circuit elements in the array are further integrated by a linear temporal filter. If we assume that all the synapses in an array have the same temporal dynamics (i.e., share the same time constants), then we can exploit Kirchhoff 's current law and sum the output currents of all synapses in a row into a single DPI circuit. This allows us to save a significant amount of silicon realestate, as we can use only one DPI per row, in each array. In particular, we use one excitatory DPI in the long-term plasticity array configured to produce time constants of the order of hundreds of milliseconds, to model the dynamics of N-Methyl-D-Aspartate (NMDA) receptors, and two DPI circuits (one for excitatory and one for inhibitory synaptic dynamics) in the STP array, configured with time constants of the order of tens of milliseconds, to model the dynamics of AMPA and GABA receptors, respectively.
We use the same principle for the 256×2 "virtual synapse" integrators in the architecture. These circuits comprise two DPI integrators per row (one for the excitatory synapse and one for the inhibitory one) with fixed sets of weights and shared timeconstant parameters, biased to operate in their linear operating range. By time-multiplexing input spikes to a single virtual synapse we can model the effect of multiple independent inputs to the targeted neuron. For example, by stimulating the DPI with a single 10 KHz spike train, we can model the effect of 1000 synapses receiving a 10 Hz input spike train.

The Spike-Based Learning Algorithm
Many models of Spike-Timing Dependent Plasticity (STDP) have been proposed in the computational neuroscience literature (Abbott and Nelson, 2000;Markram et al., 2012). However, a growing body of evidence is revealing that learning algorithms based on spike-timing alone cannot account for all of the phenomenology observed neurophysiological experiments (Lisman and Spruston, 2010), have poor memory retention performance (Billings and van Rossum, 2009), and require additional mechanisms to learn both spike-time correlations and mean firing rates in the input patterns (Senn, 2002).
For this reason, we chose to implement the spike-driven synaptic plasticity rule proposed by Brader et al. (2007), which has been shown to reproduce many of the behaviors observed in biology, and has performance characteristics that make it competitive with the state-of-the-art machine learning methods (Brader et al., 2007). This algorithm does not rely on spike-timing alone. It updates the synaptic weights according to the timing of the pre-synaptic spike, the state of the post-synaptic neuron's membrane potential, and its recent spiking activity. It assumes that the synaptic weights are bounded, and that, on long time-scales, they converge to either a high state, or a low one. However, in order to avoid updating all synapses in exactly the same way, this algorithm requires a stochastic weight update mechanism (see Brader et al., 2007 for details).
The requirements and features of this algorithm make it particularly well-suited for neuromorphic hardware implementation: the bi-stability feature removes the problematic need of storing precise analog variables on long-time scales, while the probabilistic weight update requirement can be obtained by simply exploiting the variability in the input spike trains (typically produced by a Poisson process) and the variability in the post-synaptic neuron's membrane potential (typically driven by noisy sensory inputs).
The weight-update rule for a given synapse i is governed by the following equations, which are evaluated upon the arrival of each pre-synaptic spike: where w i represents an internal variable that encodes the bistale synaptic weight; the terms w + and w − determine the amplitude of the variable instantaneous increases and decreases; V mem (t pre ) represents the post-synaptic neuron's membrane potential at the time of arrival of the pre-synaptic spike, and θ mem is a threshold term that determines whether the weight should be increased or decreased; the term Ca(t pre ) represents the postsynaptic neuron's Calcium concentration, which is proportional to the neuron's recent spiking activity, at the time of the presynaptic spike, while the terms θ 1 , θ 2 , and θ 3 are three thresholds that determine in which conditions the weights are allowed to be increased, decreased, or should not be updated. These "stoplearning" conditions are useful for normalizing the weights of all synapses afferent to the same neuron. They have been shown to be effective in extending the memory lifetime of recurrent spiking neural networks, and in increasing their capacity (Senn and Fusi, 2005). In parallel to the instantaneous weight updates, the internal variable of the synapse w i is constantly being driven toward one of two stable states, depending whether it is above or below a given threshold θ w : where C drift represents the rate at which the synapse is driven to its bounds, and w max and w min represent the high and low bounds, respectively. The actual weight J i of the synapse i is a thresholded version of the internal variable w i that is used to produce the Excitatory Post-Synaptic Current (EPSC) upon the arrival of the pre-synaptic spike: where f (x, θ J ) can be a sigmoidal or hard-threshold function with threshold θ J , and J max is the maximum synaptic efficacy. We will show in Section 2.2.3 experimental results that demonstrate how the circuits integrated in the ROLLS neuromorphic processor chip faithfully implement this learning algorithm.

The Neuromorphic Processor Building Blocks
Here we present the main building blocks used in the ROLLS neuromorphic processor chip, describing the circuit schematics and explaining their behavior.

The Silicon Neuron Block
The neuron circuit integrated in this chip is derived from the adaptive exponential I&F circuit proposed in Indiveri et al. (2011), which can exhibit a wide range of neural behaviors, such as spike-frequency adaptation properties, refractory period mechanism and adjustable spiking threshold mechanism. The circuit schematic is shown in Figure 3. It comprises an NMDA block (M N1,N2 ), which implements the NMDA voltage gating function, a LEAK DPI circuit (M L1−L7 ) which models the neuron's leak conductance, an AHP DPI circuit (M A1−A7 ) in negative feedback mode, which implements a spike-frequency adaptation behavior, an Na + positive feedback block (M Na1−Na5 ) which models the effect of Sodium activation and inactivation channels for producing the spike, and a K + block (M K1−K7 ) which models the effect of the Potassium conductance, resetting the neuron and implementing a refractory period mechanism. The negative feedback mechanism of the AHP block, and the tunable reset potential of the K + block introduce two extra variables in the dynamic equation of the neuron that can endow it with a wide variety of dynamical behaviors (Izhikevich, 2003). As the neuron circuit equations are essentially the same of the adaptive I&F neuron model, we refer to the work of Brette and FIGURE 3 | Silicon neuron schematics. The NMDA block implements a voltage gating mechanism; the LEAK block models the neuron's leak conductance; the spike-frequency adaptation block AHP models the after-hyper-polarizing current effect; the positive-feedback block Na + models the effect of the Sodium activation and inactivation channels; reset block K + models the Potassium conductance functionality.
Frontiers in Neuroscience | www.frontiersin.org Gerstner (2005) for an extensive analysis of the repertoire of behaviors that this neuron model can reproduce, in comparison to, e.g., the Izhikevich neuron model.
All voltage bias variables in Figure 3 ending with an exclamation mark represent global tunable parameters which can be precisely set by the on chip Bias Generator (BG). There are a total of 13 tunable parameters, which provide the user with high flexibility for configuring all neurons to produce different sets of behaviors. In addition, by setting the appropriate bits of the relative latches in each neuron, it is possible to configure two different leak time constants ( if_tau1! / if_tau2!) and refractory period settings ( if_rfr1! / if_rfr2!). This gives the user the opportunity to model up to four different types/populations of neurons within the same chip, that have different leak conductances and/or refractory periods.
An example of the possible behaviors that can be expressed by the silicon neuron are shown in Figure 4. The top-left quadrant shows measured data from the chip representing the neuron membrane potential in response to a constant current injection for different values of reset voltage. The top-right quadrant shows the neuron response to a constant current injection for different settings of its refractory period. The bottom-left quadrant demonstrates the spike-frequency adaptation behavior, obtained by appropriately tuning the relevant parameters in the AHP block of Figure 3 and stimulating the neuron with a constant injection current. By further increasing the gain of the AHP negative feedback block the neuron can produce bursting behavior (see bottom-right quadrant of Figure 4). Figure 5 shows the F-I curve of all neurons in the ROLLS neuromorphic processor (i.e., their firing rate as a function of the input injection current). The plot shows their average firing rate in solid line, and their standard deviation in the shaded area. The overall mismatch in the circuit, responsible for these deviations, is extremely small, if compared to other analog VLSI implementations of neural systems (Indiveri et al., 2006;Petrovici et al., 2014;Schmuker et al., 2014). The average value obtained from the measurement results of Figure 5 is only 9.4%. The reason for this improvement lies in the increased size of some critical transistors in the soma circuit-major contributor to neuron's mismatch. For example, the M L4 and M L5 Field-Effect Transistors (FETs) that set the neuron's leak time constants are of (W/L) size of (2 µm/4 µm) , while M Na3 and M Na4 , responsible for the firing threshold are of size (4 µm/0.4 µm) and (1 µm/4 µm), respectively.
In addition to the neuron soma circuit, this block contains also post-synaptic plasticity circuits that are necessary for evaluating the weight update and "stop-learning" conditions described in Section 2.1.2. In particular these circuits integrate  the spikes produced by the neuron into a current that models the neuron's Calcium concentration, and compare this current to three threshold currents that correspond to θ 1 , θ 2 , and θ 3 of Equation (1). In parallel, the neuron's membrane current (which is equivalent to the membrane potential in the theoretical model) is compared to an additional threshold equivalent to θ mem of Equation (1). The schematic diagram of this circuit is shown in Figure 6. The post-synaptic neuron's Calcium concentration is computed using the DPI M D1−D5 ; the comparisons with the fixed thresholds are made using three current-mode Winner-Take-All (WTA) circuits M W1−W9 , M WU1−WU12 , and M WD1−WD12 . The digital outcomes of these comparisons set the signals slnup and sldn which are then buffered and transmitted in parallel to all synapses afferent to this neuron belonging to the long-term plasticity array.

The Long-Term Plasticity Synapse Array
Each of the 256×256 synapse circuits in the long-term plasticity array comprises event-based programmable logic circuits for configuring both synapse and network properties, as well as analog/digital circuits for implementing the learning algorithm of Section 2.1.2. Figure 7 shows both digital and analog circuit blocks. The digital logic part, shown in Figure 7A has an pulse generator circuit that manages the handshaking signals required by the AER protocol, and three one-bit configurable latches: one latch sets/resets the MON_EN signal, which enables/disables the synapse monitor circuit, which buffers the synapse weight V w signal for off-chip reading. The remaining two latches are used to set the BC_EN and REC_EN signals, which control the activation modes of the synapse. There are three different activation modes can be configured: direct activation, broadcast activation and recurrent activation. Figure 7B shows a timing diagram in which the relative latches for enabling broadcast and recurrent activation modes are configured in a synapse, using a 4-phase handshaking protocol. In the direct activation mode the synapse is stimulated by an AER event that has the matching row and column address. In the broadcast activation mode the synapse is stimulated by an AER broadcast event (that has a dedicated address word) which targets the matching column address. All synapses belonging to the same column that have the BC_EN bit set high get stimulated in parallel, when the matching broadcast event is received. In the recurrent activation mode the synapse of column j is stimulated when the on-chip post-synaptic neuron of row j spikes. Therefore, it is possible to connect, internally, neuron i to neuron j by setting the REC_EN bit high of the synapse in row i and column j. In addition to these circuits, there is a pulse extender circuit which can increase the duration of the input pulse from nano-seconds to hundreds of micro-seconds.
The schematic diagram of the analog/digital weight update circuits is shown in Figure 7C. These circuits are subdivided into four sub-blocks: the SET block can be used to set/reset FIGURE 6 | Post-synaptic learning circuits for evaluating the algorithm's weight update and "stop-learning" conditions. The DPI circuit M D1−5 integrates the post-synaptic neuron spikes and produces a current proportional to the neuron's Calcium concentration. Three current-mode winner-take-all circuits WTA, WTAUP, and WTADN compare the Calcium concentration current to three set thresholds sl_thmin! , sl_thdn!, and sl_thup!, while the neuron's membrane current is compared to the threshold sl_memthr!.
Frontiers in Neuroscience | www.frontiersin.org the bistable state of the synaptic weight by sending an AER event with the matching address and properly asserting the configuration signals set_hi and set_low. The JUMP block increases or decreases the synaptic weight internal variable (i.e., the voltage V w ) depending on the digital signals up and dn, that are buffered copies of the ones generated in the silicon neuron stop-learning block (see Section 2.2.1). The heights of the up and down jumps can be set by changing the delta_up! and delta_dn! signals. The BIST block consists of a wide-range transconductance amplifier configured in positive feedback mode, to constantly compare the V w node with the threshold bi_thr!: if V w > bi_thr! then the amplifier slowly drives the V w node, drifting toward the positive rail, otherwise it actively drives it toward the ground. The drift rates to the two states can be tuned by biases drift_up! and drift_dn!, respectively. The current converter (CC) block converts the V w voltage into a thresholded EPSC with maximum amplitude set by pa_wht!. Figure 8 shows experimental results that highlight the features of both synapse and neuron learning circuits in action: weight updates are triggered when the pre-synaptic spikes arrive, and when the post-synaptic neuron's Calcium concentration is in the appropriate range. Depending on the value of the Calcium concentration signal, the digital up and dn signal turn on or off. The weight internal variable is increased or decreased depending on where the membrane potential is with respect to the membrane threshold (see highlighted weight updates at t = 273 and t = 405). This variable is actively driven to the low or high bounds, depending if it is below or above the weight hreshold.

The Short-Term Plasticity Synaptic Array
The array of STP synapses contains circuits that allow users to program the synaptic weights, rather than changing them with a fixed on-chip learning algorithm. Specifically, each synapse has a two-bit programmable latch that can be used to set one of four possible weight values. In addition, it has an extra latch that can set the type of synapse (excitatory or inhibitory). In the excitatory mode, the synapse has additional circuits for modeling Short-Term Depression (STD) dynamics (Rasche and Hahnloser, 2001;Boegerhausen et al., 2003) whereby the magnitude of the EPSC decreases with every input spike, and recovers slowly in absence of inputs. Figure 9 shows both a block diagram of all synapse components, and the schematic diagram of the synapse analog circuits. In addition to the latches for setting the weight, FIGURE 8 | Spike-based learning circuit measurements. The bottom trace represents the pre-synaptic input spikes; the second trace from the bottom represents the bi-stable internal variable (node V w of Figure 7); the third trace represents the post-synaptic neuron's membrane potential; and the top trace shows both a voltage trace proportional to the neuron's integrated spiking activity as well as the digital control signals that determine whether to increase (red shaded area), decrease (blue shaded area) or leave V w unchanged (no shaded area). The horizontal lines represent the thresholds used in the learning algorithm (see Section 2.1.2), while the vertical lines at t = 273 s (blue line) and t = 405 s (red line) are visual guides to show where the membrane potential is, with respect to its threshold, for down and up jumps in V w respectively. there are two extra latches for configuring the synapse activation mode. As for the long-term-plasticity synapses, there are three possible activation modes: direct, broadcast, and recurrent (see Section 2.2.2).
The left panel of Figure 9B shows the excitatory CC and the STD circuit. The CC at the top generates a current that is proportional to the 2-bit weight. The proportionality constant is controlled through analog biases. This current charges up the C STD capacitor through the diode connected p-FET M S3 so that at steady state, the gate voltages of M S1 and M W2 are equal. A pre-synaptic pulse on the PW port activates the I exc current branch, and produces a current that initially is proportional to the 2-bit weight original current. At the same time, the PW pulse activates also the STD branch through transistor M S5 and an amount of positive charge that is controlled by the bias STD is removed from the capacitor C STD . The gate voltage of M W2 is now momentarily lower than that of M S1 , and recovers slowly through the diode connected p-FET M S3 . Pulses that arrive before the capacitor voltage has recovered completely will generate a current that is smaller than the original one, and will further depress the effective synaptic weight through the STD branch. The excitatory block is only active if the E/I voltage is high. If E/I is low, the inhibitory current DAC in the right panel of Figure 9B is active and generates a weight-proportional inhibitory current on PW pulses. Figure 10 illustrates how the STD behavior in the synapse: a spike burst was used to activate a programmable synapse. This resulted in a drop in synaptic efficacy during the later part of the burst. During a period of no stimulation the synapse recovered and responded with large Excitatory Post-Synaptic Potentials (EPSPs) to the initial part of the following burst, before depressing again. The responses to the two bursts are not identical in Figure 10 as the state of the neuron, synapse, and DPI circuits are not exactly the same at the onset of each burst.

The Peripheral Input/Output Blocks
The peripheral digital circuits are used to transmit signals into and out of the chip. Given the real-time nature of our system, we use asynchronous digital circuits and quasi-delay-insensitive circuit design techniques (Manohar, 2006) to avoid discretization or virtualization of time. The AER communication protocol used encodes signals as the address of the destination synapse or as a control word for the input side, and as the address of the sender neuron in the output circuits.

AER input circuits
Input spike events as well as chip configuration events are sent through a common input interface that uses a 21-bit address space. Input addresses are decoded into a total of 1,249,553 possible patterns subdivided into three categories: Addressing, Local configuration, and Global configuration. Addressing inputs are decoded into a row and column address and are interpreted as a spike Address-Event (AE), which are sent to the desired target synapse of a target neuron. Local configuration AEs contain the row and column address of the target element as well as extra configuration bits that are written to the local latches of the addressed element. Local configuration patterns include commands for setting the type of synapse, programming its weight, or enabling broadcast or recurrent connections. Finally, the Global configuration inputs are decoded into configuration signals that represent global variables, stored onto registers in the periphery (rather than within the synapse or neuron elements). For example, the signals used to set the state of the synapse de-multiplexer are Global configuration signals. See the Supplementary Material for additional details on these circuits.

AER output
Each of the 256 neurons is assigned an 8-bit address for the output bus. When a neuron spikes, its address is instantaneously sent to the output AER circuits using the common four-phase handshaking scheme. Although neurons operate in a fully parallel fashion, their AEs can only access the shared output bus in a serial fashion. To manage possible simultaneous spike collisions the output AER circuits include an arbiter circuit that only grants access to the external bus to one neuron at a time. Details of these circuits are provided in the Supplementary Material.

Results
Here we demonstrate the capabilities of the ROLLS neuromorphic processor device with examples of hardware emulation of computational neuroscience models and pattern recognition in a machine vision task.

Attractor Networks
In this experiment we explored the collective dynamics of multiple populations of spiking silicon neurons that emulate FIGURE 10 | The effect of short-term depression on EPSC magnitudes. Two bursts separated by 100 ms were sent to a programmable synapse. Each burst has 5 spikes with an inter-spike interval of 5 ms. Within a burst, The jumps in the neuron V mem gradually get smaller as the synapse is depressed and the magnitude of the EPSCs it generates decreases. After the first burst, the synapse efficacy recovers as can be seen in the response to the second burst. The figure inset shows the derivative of the membrane potential which is equivalent to the synaptic EPSCs (minus the neuron leak).
the biophysics of cortical neurons organized in attractor networks (Amit, 1992). These types of networks are considered a basic computational primitive of neural processing systems. Their ability to exhibit self sustained activity is thought to be one of the basic requirements for exhibiting multiple types of cognitive processes and functions. Their collective dynamics represents the neural correlates of processes involved in working memory, perceptual decision making and attention.
We implemented the hardware attractor networks following the theories and methods proposed in Amit (1992); Wang (1999); Amit and Mongillo (2003); Del Giudice et al. (2003); Giulioni et al. (2012). We constructed an architecture comprising six pools of neurons recurrently connected. Specifically, there are three pools of 64 excitatory neurons and three pools of 10 inhibitory neurons. Neurons in each pool receive local excitation via recurrent connections implemented via the on-chip long-term synaptic plasticity circuits. In Figure 11 each point represents a synaptic contact (i.e., an active synapse in the corresponding STP or LTP synaptic matrix). The recurrent connectivity via the LTP synapses is set to have a probability of 70% for the excitatory connections and 40% for the inhibitory ones, i.e., they have connectivity parameters c e ee = 0.7, c e ii = 0.4, respectively (see dots in Figure 11A). We further configured the connectivity matrix of the STP synapses such that every excitatory pools of neurons is homogeneously connected with all other excitatory pools with excitatory connectivity parameter c e ee = 0.2 and inhibitory connectivity parameter c i ee = 0.2. Inhibitory pools of neurons are connected to their corresponding excitatory pools (e.g., inhibitory pool #1 is connected to excitatory pool #1) via inhibitory synapses, with a connectivity parameter c i ei = 0.4. Excitatory pools of neurons are connected to their respective inhibitory pools of neurons via the STP excitatory synapses, with a connectivity parameter c e ie = 0.7. The behavior of the network when stimulated by a external transient stimuli is shown in Figure 11B. The profile of the external stimuli is depicted by the square waves below the plot of Figure 11B. The different colors indicate inputs to the different corresponding populations. The input stimuli are a series of Poisson spike trains, generated artificially and sent via the AER protocol to the chip virtual synapses. The mean rate of the input spike trains is v in = 100 Hz and their duration is t = 0.5 s. When the attractor networks are being driven by external stimuli their activity reaches a mean rate of approximately 50 Hz and, after the removal of these stimuli, the pools of neurons relax to a sustained state of activity of about 15 Hz, indicating that the neurons settled into their attractor states. This persistent activity is the neural correlate of working memory and can be exploited as an asynchronous distributed memory state that has peculiar dynamical properties of error correction, pattern completion, and stability against distractors (Amit, 1992).
If a population is in an attractor state, a transient stimulus to a different pool of neurons shuts down its activity via direct inhibitory connections (on the STP synaptic matrix), and brings the newly stimulated pool of neurons into a new attractor state. If we inhibit an active pool of neurons directly, with an external stimulus the population is reset and becomes inactive. This is evident in Figure 11B at t = 3 s, when a Poisson stimulus of mean rate ν = 200 Hz is used to inhibit all attractor networks. This experiment demonstrates how it is possible to implement robust state dependent computation and reliable memory storage using sets of 64 slow and imprecise silicon neurons. A similar, but more elaborate experiment showing how these types of circuits can be used to synthesize context-dependent behavior in neuromorphic agents, in the context of cognitive computation was recently presented in Neftci et al. (2013), using the same types of circuits and principles. The implementation of plausible neural collective dynamics in neuromorphic substrates is an important step also for future nano-technologies that are likely to be affected by device mismatch and unreliability characteristics.

Multi-Perceptron Network
Neuromorphic systems are an ideal electronic substrate for realtime, low-latency machine vision (Serrano-Gotarredona et al., 2008;Delbruck and Lang, 2013;O'Connor et al., 2013). Here we present a feasibility study which demonstrates how the ROLLS neuromorphic processor can be used in conjunction with a spiking vision sensor for learning to solve an image classification task. In this experiment (see Figure 12), we used a DVS, interfaced to our chip via a commercially available digital board, used to route signals from the vision sensor to the chip. We implemented a two-layer spiking neural network which processes the visual stimuli by extracting sparse random features in realtime. The network is composed of 128 VLSI hidden neurons and 128 VLSI output neurons on the ROLLS neuromorphic processor. We trained 64 of the VLSI output neurons of the network to become selective to one of two image classes, and the other 64 to become selective to the other class, via supervised learning protocol.
The experimental protocol consists of showing a sequence of static images of objects from the Caltech 101 dataset coupled with a teacher signal to steer the activity of the output neurons. The DVS is put in front of a screen where the images are displayed. During the presentation, the images are flashed with a small jitter around the center of the visual field to simulate microsaccadic eye movements. The movement causes the DVS retina to continuously stream spike trains corresponding to the edges of the objects in the image. The spike trains are then routed to the STP synapse array, stimulating a population of neurons corresponding to the hidden layer of the neural network. The spikes from the hidden layer neurons are internally routed to the LTP plastic synapse array, thus activating the neurons of the output layer. With every training image, a corresponding teacher signal is provided to one of the two subgroups of the output layer neurons, depending on the image class, to associate stimulus with class. To remove artifacts generated during the transition from one presentation of an image to the next, we gated the DVS spikes, simulating a saccadic suppression mechanism analogous to the one observed in biology (Ross et al., 1996).
The performance of this experiment strongly depends on the right choice of parameters for the neural and synaptic dynamics. For this particular demonstration we chose to disable most of the complex aspects of the neural dynamics and optimized neuron and synapse parameters to obtain reasonable activity patterns in the hidden layer neurons. The activity in this layer is indeed the most important since it drives the plastic synapses that belong to the output layer neurons.
After training, our classification system was able to respond selectively to natural images of cars and motorbikes taken from the Caltech 101 database. Although an extensive characterization of the system's ability to perform object recognition is out of the scope of this work, we draw the following conclusions from our experiment: • The choice of fixed, random projections from the input layer was surprisingly effective, though certainly not optimal for the task at hand. • A better solution would be to include an unsupervised learning stage in the training protocol to optimize the weights of the convolution layer as in traditional machine learning approaches (LeCun et al., 1998;Le et al., 2012) and in neural systems (Olshausen and Field, 1997;Masquelier et al., 2009;Nessler et al., 2009). However, this stage would require the presentation of a large number of patterns and sophisticated synaptic plasticity rules.
Our network of randomly connected neurons projects the input stimuli into a high-dimensional space where they can be classified by linear models but with far less parameter optimization (Barak and Rigotti, 2011). This strategy is related to some of the state-of-the-art machine learning algorithms for pattern classifications, such as Support Vector Machines (SVMs) (Vapnik, 1995). Clearly, the generalization properties of our system are not comparable to standard machine learning approaches but they are also expected to scale with the number of randomly connected neurons in the hidden layer (Rigotti et al., 2010;Barak et al., 2013). Notice also that we haven't exploited any temporal structure of the input data, though we recently demonstrated that our hardware supports this functionality (Sheik et al., 2012a(Sheik et al., ,b, 2013. For cases in which the temporal structure of the input stimuli is relevant, it would be possible to follow alternative approaches, for example by interconnecting the neurons in the hidden layer to form a Liquid State Machine (LSM) (Maass et al., 2002). This solution would be particularly interesting in situations where information hidden in the fine temporal structure is expected to impact the performance of the recognition system. Also for this approach, it would be sufficient to provide an output layer analogous to the one used in our experiment, that could be trained in an analogous way. In our example we used multiple neurons clustered into two distinct pools in the output layer for our simple two-class discrimination problem, (e.g., instead of using just two output neuron units).
The rationale behind this choice is that, given the many sources of noise in the system (the micro-saccadic movements, the DVS spiking output, the stochastic plasticity mechanism, the hardware mismatch), each neuron taken singularly is not expected to perform well on the task (i.e., it will implement a "weak" classifier, showing low class specificity). However, the performance of the overall system improves as responses aggregated from multiple neurons are considered. This can be visually appreciated from the raster plots of Figure 13 where only population-level firing rates are selective for the input classes, but not the single neuron activities. This phenomenon is directly related to a notorious machine learning technique that uses "boosting" to improve the Middle column: heat map of the DVS spiking activity, where each pixel color represents the pixel's mean firing rate. Right column: raster plots of the ROLLS neuromorphic processor neurons. The star on the top panel label indicates that during the training phase an additional excitatory "teacher" signal was used to stimulate the "car" output neurons to induce plasticity. During testing no teacher signal is provided and only the excitatory currents from the input synapses drive the classifier activities. The average firing rates of the output layer for "motorbike" and "car" neuron pools are 6.0 Hz and 80.9 Hz during training. During testing with a "car" image they are 7.1 Hz and 11.1 Hz. During testing with a "motorbike" image they are 7.4 Hz and 4.9 Hz.

Discussion
Unlike conventional von Neumann processors that carry out bit-precise processing and access and store data in a physically separate memory block, the ROLLS neuromorphic processor uses elements in which memory and computation are colocalized. The computing paradigm implemented by these types of neuromorphic processors does not allow for the virtualization of time, with the transfer of partial results back and forth between the computing units and physically separate memory banks at high speeds. Instead, their synapse and neuron circuits process input spikes on demand as they arrive, and produce their output responses in real-time. Consequently, the time constants of the synapses and neurons present in these devices need to be wellmatched to the signals the system is designed to process. For the case of real-time behaving systems that must interact with the environment, while processing natural signals in real-time, these time constants turn out to be compatible with the biologically plausible ones that we designed into the ROLLS neuromorphic processor. As we implemented non-linear operations in each synapse (such as short-term depression or long-term plasticity), it is not possible to time-multiplex linear circuits to reduce the area occupied by the synaptic matrix array. As a consequence, our device is essentially a large memory chip with dedicated circuits for each synapse that act both as memory elements and computing ones. This approach is complementary to other recent ones that focus on accelerated neural simulations (Bruederle et al., 2011), or that target the real-time emulation of large populations of neurons but with no on-chip learning or adaptive behaviors at the synapse level (Benjamin et al., 2014). The device we describe here is ideal for processing sensory signals produced by neuromorphic sensors (Liu and Delbruck, 2010) and building autonomous behaving agents. The system level examples demonstrated in Section 3 show how this can be achieved in practice: the hardware attractor network experiment focuses on the idea that the functional units of the cortex are subset of neurons that are repeatedly active together and shows that such units have the capability of storing state-dependent information; the pattern classification experiment demonstrates how it is possible to implement relatively complex sensory processing tasks using event-based neuromorphic sensors.
Our results demonstrate the high-degree of programmability of our device as well as its usability in typical application domains. Its properties make it an ideal tool for exploring computational principles of spiking systems consisting of both spiking sensors and cortical-like processing units. This type of tools are an essential resource for understanding how to leverage the physical properties of the electronic substrate as well as the most robust theories of neural computation in light of the design of a new generation of cortex-like processors for real-world applications. The multi-chip system is supported by the use of a newly developed software front-end, PyNCS, which allows rapid integration of heterogeneous spiking neuromorphic devices in unique hardware infrastructure and continuous online monitoring and interaction with the system during execution . In order to integrate the DVS and ROLLS in the existing software and hardware infrastructure, it was necessary to list the address specifications for the spiking events and for the configuration events in Neuromorphic Hardware Mark-up Language (NHML) files, the neuromorphic mark-up language used by PyNCS to control the neuromorphic system.
The potential of the approach proposed in this work for building intelligent autonomous systems is extremely high, as we develop brain-inspired computing devices embedded with learning capabilities that can interact with the environment in real time. Substantial progress has already been made in the theoretical domain (Schöner, 2007;Rutishauser and Douglas, 2009), and preliminary results have already been demonstrated also with neuromorphic cognitive systems (Neftci et al., 2013) synthesized by the user. The ROLLS neuromorphic processor described in this work can therefore contribute to extending the current state-of-the-art by providing also adaptation and learning mechanisms that could allow these systems to learn the appropriate network properties to implement autonomous cognitive systems.

Conclusions
We presented a mixed-signal analog/digital VLSI device for implementing on-line learning spiking neural network architectures with biophysically realistic neuromorphic circuits such as STP synapses, LTP synapses and low-power, lowmismatch adaptive I&F silicon neurons. The proposed architecture exploits digital configuration latches in each synapse and neuron element to guarantee a highly flexible infrastructure for programming, with the same device, diverse spiking neural network architectures.
All the operations of the chip are achieved via asynchronous AE streams. These operations include sending events to the chip, configuring the topology of the neuron network, probing internal variables, as well as programming internal properties of synapse and neurons. The parameters for different synapse and neuron behaviors can be fine tuned by programming the temperature-compensated on-chip BG.
The ROLLS neuromorphic processor can be used to carry out basic research in computational neuroscience and can be exploited for developing application solutions for practical tasks. In particular, this architecture has been developed to study spikebased adaptation and plasticity mechanism and to use its ability to carry out on-chip on-line learning for solving tasks that require the system to adapt to the changes in its input signals and in the environment it interacts with.

Funding
This work was funded by the EU ERC Grant neuroP (257219), the EU FET Grant SI-CODE (284553) and the Samsung Advanced Institute of Technology (SAIT)'s Global Research Outreach (GRO) Program.


What Users Prefer and Why: A User Study on Effective Presentation Styles of Opinion Summarization

. Opinion Summarization research addresses how to help people in making appropriate decisions in an effective way. This paper aims to help users in their decision-making by providing them effective opinion presentation styles. We carried out two phases of experiments to systematically compare usefulness of different types of opinion summarization techniques. In the first crowd-sourced study, we recruited 46 turkers to generate high quality summary information. This first phase generated four styles of summaries: Tag Clouds, Aspect Oriented Sentiments, Paragraph Summary and Group Sample. In the follow-up second phase, 34 participants tested the four styles in a card sorting experiment. Each participant was given 32 cards with 8 per presentation styles and completed the task of grouping the cards into five categories in terms of the usefulness of the cards. Results indicated that participants preferred Aspect Oriented Sentiments the most and Tag cloud the least. Implications and hypotheses are discussed. on three review types which are related to perceived usefulness, perceived ease of use, and behavioral intention: A (regular), B (comparative), and C (suggestive). Suggestive reviews, the speech acts which are used to direct someone to do something in the form of a suggestion, were newly identified by the researchers as a third innovative review type. To examine user perspectives, researchers used a closed card sorting approach to analyze reviews from Amazon, blogs, and a self-deployed website. The results of their work indicated that the review types play a significant role in developing user perception regarding a new product or system, with suggestive reviews more significant for both customers and designers to find more usefulness that ultimately improves their satisfaction level.

Introduction
The widespread use of the Internet in many aspects of human activities has resulted in an abundance of publicly-accessible opinions. People can find opin ions on a variety of topics in venues such as Twitter, Weibo, foru ms, e-co mmerce sites and specialized opinion-hosting sites such as Yelp. While most of these opinions are intended to be helpful for others, the sheer quantity of them often makes most of the opinions underutilized, as the information overload overwhelms many potential consumers. For example, A mazon.com has more than 18,000 reviews for Kindle Fire, a single pro duct alone. Summarizing these reviews in some concise form could bring enormous benefits to consumers and business alike. Not surprisingly, research on opinion su mmarizat ion is gaining increased attention [14, 18, 19, 22, 24,]. Ho wever, most of the research emphasizes technical advances in underlying algorithms, wh ile paying less attention to the presentation of the results, which is the focus of this work. Correspondingly, evaluation of opin ion summarization research is normally based on ce rtain notions of precision and recall calculat ion co mmonly used in information retrieval [28] and data mining [29]. Studies have only begun to investigate the effectiveness of opinion summarization in term o f usability (e.g. [ 31]). Such studies focus on tes ting the newly-proposed techniques. A systematic comparison of the usefulness of different types of opinion summarizat ion is still lacking. Th is paper reports our effort in addressing this deficiency.
One major difficulty with studying the effectiveness of opinion summarizat ion is a confounding effect between content effectiveness and presentation effectiveness. It is often not clear whether a technique's empirical superiority can be attributed to its superior text analytics quality or its effective informat ion presentation style. We plan to isolate the two factors and focus on studying the effect of presentation styles. This goal is achieved by using human-generated summarization as the content, so as to ensure the content has consistent high quality regardless of the presentation styles. We can then vary the presentation styles of the summaries to investigate their effect on the usefulness ratings of the summaries. Any differences found between the usefu lness ratings of the summaries can be safely attributed to the differences in present ation styles. We identified four types of presentation styles of opinion summarization through a crowd-sourcing study on Amazon Mechanical Turk, and then conducted a lab user-centered experiment to compare the effectiveness of the four styles.

Previous work
Although not abundant, studies investigating the effectiven ess of opinion summarization fro m a perspective of both usability and user preference are emerg ing. Several recent studies explore feedback fro m users regarding their preferences for certain opinion summarization styles and approaches. Most recently, Qazi et al. [26] addressed a gap in existing studies examining the determination of useful opinion review types from customers and designers perspectives. Specifically, the researchers used the Technology Acceptance Model (TAM) as a lens to analyze users' perce ptions toward different opinion review types and online review systems. The study, a pilot study, focused on three review types which are related to perceived usefulness, perceived ease of use, and behavioral intention: A (regular), B (co mparative), and C (suggestive). Suggestive reviews, the speech acts which are used to direct someone to do something in the form of a suggestion, were newly identified by the researchers as a third innovative review type. To examine user perspectives, researchers used a closed card sorting approach to analyze reviews from A mazon, blogs, and a selfdeployed website. The results of their work indicated that the review types play a significant ro le in developing user perception regarding a new product or system, with suggestive reviews more significant for both customers and designers to find mo re usefulness that ultimately improves their satisfaction level.
Further, in another work [31], researchers conducted a user study of a review summarization interface they created called "Review Spotlight." Review Spotlight is based on a tag cloud and uses adjective-noun word pairs to provide an overview of online restaurant reviews. Findings indicated that study participants could form d etailed imp ressions about restaurants and make faster decisions between two options with Review Spotlight versus traditional review webpages. In a large-scale, co mprehensive human evaluation of three opinion-based summarization models -Sentiment Match (SM), Sentiment Match + Aspect Coverage (SMAC), and Sen timent-Aspect Match (SAM) -Lerman, Blair-Go ldensohn and McDonald [15] found that users have a strong preference for sentiment-info rmed summaries over simp le, non-sentiment baselines. This finding reinforces the usefulness of modeling sentiments and aspects in opinion summarization. In another study, Lerman and McDonald [16] investigated contrastive versus single-product summarization of consumer electronics and found a significant improvement in the usefulness of contrastive summaries versus summaries generated by single-product opinion summarizers. To find out which visual properties influence people viewing tag clouds, Bateman, Gutwin and Nacenta [2] conducted an exploratory study that asked participants to select tags from clouds that manipulated nine visual propert ies (font size, tag area, nu mber of characters, tag width, font weight, color, intensity, and number of pixels). Participants were asked to choose tags they felt were "v isually important" and results were used to determine wh ich visual properties most captured people's attention. Study results indicated that font size and font weight have stronger effects than intensity, number of characters or tag area. Ho wever, when several visual properties were manipulated at one time, no one visual property stood out among the others. Carenin i, Ng and Pauls [4] also employed a user study as part of their wider co mparison of a sentence extract ion -based versus a language generation-based summarizer for summarizing evaluative text . In their quantitative data analysis, the researchers found that both approaches performed equally well. Qualitative data analysis also indicated that both approaches performed well, however, for d ifferent, co mplementary reasons. In a related work, Carenin i, Ng and Pauls [5] examined the use of an interactive mult imedia interface, called "Treemaps," for summarizing evaluative text o f online reviews of consumer electro nics. Treemaps presents the opinion summarizations as an interactive visualization along with a natural language summary. Results of their user study showed that pa rticipants were generally satisfied with the interface and found the Treemap summar ization approach intuitive and informative.
In more recent work, [12] researchers presented a novel interactive visual text analytic system called, "Opin ionBlocks." Opinion Blocks had two key design goals: (1) automated creation of an aspect-based visual summary to support users' real-world opinion and analysis tasks, and (2) support of user corrections of system text analytic errors to imp rove system quality over time. To demonstrate OpinionBlock's s uccess in addressing the design goals, researchers employed two crowd -sourced studies on Amazon Mechanical Turk, According to their results, over 70% of users successfully accomplished non-trivial opin ion analysis tasks using OpinionBlocks. Additionally, the study revealed that users are not only willing to use Opinion Blocks to correct text classification mistakes, but that their corrections also produce high quality results. For example, study participants successfully identified numerous errors and their aggregated corrections achieved 89% accuracy.
Additionally, Duan et al. [7], introduced the opinion mining system, "VISA" (VIsual Sentiment Analysis), (derived fro m an earlier system called TIA RA). The VISA system employs a novel sentiment data model to support finer-grained sentiment analysis, at the core of which is the "sentiment tuple," composed of four elements: feature, aspect, opinion, and polarity. Researchers conducted a user experiment to explore how efficiently people could learn to use VISA an d demonstrate its effectiveness. Study results indicated that VISA performed significantly better than the two comparison tools (TripAdvisor and a text edit tool) due to its key features, namely mash-up visualizations and rich interaction features.
In the current study, investigation of user perspectives on opinion summarization styles is taken further with the evaluation and comparison of four distinct, popular summarization styles focused on textual opinions; namely, Tag Clouds, Aspect -Oriented Sentiments, Paragraph Summaries, and Group Samples .
Following we introduce the four presentation styles used in our study, the metho dology, results of the experiment, discussion and conclusions.

Opinion summarization presentation styles
Some opinion hosting sites allow opinion writers to give nu merical rat ings in addition to the textual opinions. Since the visualization of numerical values is a well -studied problem, we focus instead on the summarizat ion of textual opin ions. Similarly, we do not compare visualizat ion systems that emphasize statistics rather than the textual content of the text co llect ions (e.g. [6]). Opin ion summarizations studied here are of the kind that could potentially be used in place of the full documents.
Based on our survey of the literature, we have categorized the presentation styles of such opinion summarization into four major types.

Tag clouds (TAG)
Tag clouds are perhaps the most popular form of summarization on the Internet today [3]. This type of text presentation has also been used extensively in research (e.g. [25,28]). They consist of enumerations of the most common words or phrases in a colle ction of opinions, laid out on a 2D canvas. The size of the words or phrases often indicates how frequently they were mentioned. The larger the word or phrase, the more frequently it received mentions. The effect of various visual features of the tag clouds on their effectiveness have been investigated [2], but the comparison with other styles of summarization has yet to be done. See Figure 1.

Aspect oriented sentiments (ASP)
Aspect oriented sentiment summarization is an active area of research in text mining [11,13,14,19,23]. In this approach, some important aspects or topics (also known as features) of opinions are ext racted fro m an opin ion collection. Sentiment orientation of the text snippets containing the aspects are then estimated and summary statistics reported. A typical summarizat ion for one aspect might look like this: for a collection of reviews on Kindle Fire, "screen" is identified as an aspect, and 100 text snippets in the collection are found to be about this aspect, 60 of them have positive sentiment, 30 of them are negative, and the rest are neutral. Representative text snippets for each sentiment orientation may also be provided. See Figure 2.

Paragraph summaries (PRG)
Automatic text summarization systems traditionally produce short passages of text as summary [10,27]. The summarizat ion is called ext ractive when the sentences are selected from original documents; abstractive when the sentences are generated by the system [8]. Regard less of the approach, the output could be a readable abstract that resembles what hu mans would write for generic purposes in order to emphasize intrinsic properties such as fluency and coverage [21]. See Figure 3.

Group samples (GRP)
Clustering algorith ms are typically used in post-query user interfaces as a way of organizing retrieved documents [9]. It has also been used in summarization, where similar documents are grouped together and the representatives of the groups are displayed [1,20]. This approach has shown to be effective in interactive information retrieval [17,30]. See Figure 4.

Summary of the four presentation styles
Tag clouds may perform most differently fro m the other three presentation styles. Because Tag clouds do not show full sentences, they lack the context needed to make accurate judgments about the value of the informat ion. On the other hand, Tag clouds have the best support for fast perception as they package the most important words or phrases efficiently in space, and visually highlight their relat ive importance. Aspect oriented sentiments are similar to Tag clouds in that they lack a prose structure. On the other hand, they are also similar to Paragraph summaries because they include text snippets that are reader friendly and provide the context missing in Tag clouds. Paragraph summaries are close to Aspect oriented sentiments because they may cover similar amounts of informat ion, as paragraphs often list pros and cons in a form that resembles aspect oriented sentiments. Ho wever, the prose structure in a well-written paragraph summary affords deep analysis and accurate assessment of the context. Group samples are similar to paragraph summaries in form. However, unlike paragraph summaries that are written anew, group samples are directly drawn fro m the original document collection, and retain the most amount of contextual information.

Research questions
We hypothesize that hu mans respond differently to different presentation styles of opinion summaries, and some styles would be more effective in terms of hu man acceptance.
In this two-phase study, we are interested in investigating the following research questions: 1. Will users prefer (or not prefer) a particu lar opin ion summarizat ion style in making judgments about product reviews?
2. What are the reasons that users may p refer (or not to prefer) a part icular opinion summarization style in making judgments about product reviews?

Phase I: crowd sourcing opinion summarization
As mentioned earlier, in order to study the effect of presentation styles alone, we want to ensure the consistently high quality of the summaries. We achieved the goal through leveraging the wisdom of the crowd. Essentially, we elicited four styles of the opinion summaries with the help of Amazon's Mechanical Turk.
Procedure. First, we collected the top 50 reviews for one model of the iRoomba cleaning robot from Amazon. We chose this collection of opinion text because of the relative novelty of the product and the ability for the general public to relate to it. Using a within-subject design, we recruited 46 turkers located in the USA to answer a survey we developed to gather information for generating the opinion summaries from the text collection. In the survey, turkers were first directed to the raw text of the 50 reviews, and asked to read them in full. Then, questions about the reviews were asked. These questions were directly mapped from the information need for the four presentation styles of summaries. All questions were mandatory and were individually validated to ensure the quality of the answers. On average, turkers spent 56.3 minutes on the survey, and each was paid 4 US dollars.
Generating opi nion summaries using Turkers. For Tag clouds, turkers were asked to list five short phrases to characterize the clean ing robot. They were also asked to estimate what percentage of the 50 reviews had opinions consistent with each phrase. The phrases turkers came up with were remarkably consistent and converged to 38 phrases (phrases with minor variat ions were grouped as one). All 38 phrases were used in the subsequent lab study. The average percentages of turkers' estimations were used in the subsequent lab study to determine the font size of the phrases. A total of 8 Tag clouds were drawn by hand, with each cloud containing 4 or 5 phrases.
Turkers were asked to list three important aspects of the product according to the reviews they read. For each aspect, they were asked to give an estimate of how many reviews mentioned the aspect, as well as the estimated percentage for positive, neutral and negative sentiment towards the aspect. The top 8 most -frequently listed aspects were used in the subsequent study. Again, the averages of the estimations were used in the display of the aspects.
Each turker was also asked to write a summary of all the rev iews so that "consumers who read your summary can make an informed decision on the product, without having to read the reviews themselves". Among the 46 summaries, the top 8 most readable summaries, as agreed by two judges, were used in the lab study.
We asked turkers to identify similar reviews and group them together. They were required to list 3 groups of similar reviews.
When two reviews appeared in the same group once, their similarity measure increase by one. This way, we were able to generate a similarity matrix among the 50 reviews.
Using the matrix as input, we used a hierarchical clustering algorithm to cluster the 50 reviews. Four clusters produced the optimal fit, and the four cluster prototypes were used in the lab study as group samples. In addition, for each cluster, the closest summary to the prototype was also selected, so that there were 8 group samples in total.

Phase II: comparing presentation styles of summary using card sorting technique
The goal of this phase was to co mpare the four presentation styles of opin ion su mmary in a consumer decision making context to respond to the two research questions.
Experi ment design. The co mparison of the opinion summaries was conducted as a lab card-sorting task. For each of the four presentation styles (experiment conditions), eight opinion summaries were prepared according to the procedure described in the previous section.
Each opin ion summary was put on a single image of 960 x 720 pixels. Figure 1, Figure 2, Figure 3 and Figure 4 show a sample display for each of the conditions. In total, 4*8=32 image items were placed in the preparation bin in a random order.
The lab experiment was a within-subject design. Each participant's task was to take all 32 image items and place each of them in one of the five category bins. These category bins were defined as "Not at all useful", "So mewhat useful", "Useful", "Very useful" and "Extremely useful". Participants were told to ignore the card order within each category box. Essentially, we asked participants to give a usefulness rating for each opin ion summary. We use such a card-sorting setting in order to record participants' thought process, as they were asked to th ink-aloud wh ile placing the cards.
Partici pants. Thirty-four participants were recruited fro m Un iversity at Albany, half of them were males. All o f the participants stated that they read online reviews reg ularly for making purchase decisions.
Procedure. Each participant was tested individually in a human computer interaction lab in a University campus in the USA. The subjects first filled out a consent form. Next, the subjects completed an entry questionnaire. The participants were then d irected to http://websort.net/ to do the card sorting. After they comp leted the exper iment, they were asked to answer several questions regarding the four presentation styles and their thoughts about the experiment. The whole experimental process was logged by Techsmith Morae 3 software.

Content analysis scheme
To address the earlier mentioned research question(What are the reasons that users may prefer (or not prefer) a part icular opinion su mmarization style in making jud gments about product reviews?) we emp loyed a qualitative content analysis using an open coding approach to analyze the exit interview data of the Phase II experiment. The content analysis began with a co mprehensive read -through and evaluation of all 34 part icipants' interview transcripts by each of the first three authors , the primary investigator and two doctoral students. In terms of the initial review and several discussions between the authors, a number of themes emerged fro m the interviews that pertained to the reasons of preference towards the presentation styles. Th emes included: Co mprehensiveness (Co mprehensiveness of Information), Time (Time required to read the summary), Organizat ion/categorizat ion (Organizat ion/categorization of the summary's content), Length/Amount (Length/amount of informat ion), Appearance (Appearance of summary content), and Ease of use (Ease of use of summarization style). A coding scheme was designed according to these themes and is shown in the Table 1. The unit of analysis for the open coding was the individual interview docu ment. Each of the 34 interview documents were independently coded by the three researchers and data collected in an Excel spreadsheet. The average pairwise percent agreement among the 3 coders is 0.81. Along with each code, snippets of supporting text were extracted from the interview data.

User perception of the presentation styles
As initially noted, the goal of this paper is to better understand the relationship between opinion summarization styles and user decision judgment and the underlying reasons. The results are aligned with this goal and are based on a qualitative content analysis of the participant exit interview data.
In the exit interview, the part icipants answered questions relevant to the four presentation styles, including, (1) most helpful/ least helpful, (2) easiest/most difficult, (3) fastest/slowest, (4) most informative/least informative, (5) sacrifice/keep the most, and (6) like/dislike. The six questions were the basis for the measurement of user perception of the four presentation styles. The results are displayed in Figure 5.
Participant responses also included their opinions on the usefulness of the summary in general and strategies they used to make the decision, as well as suggestions on system features they best liked, disliked, and thought could be added in the future.
Although "think-aloud" data and additional computer log data was collected using the Morae software, the current paper focuses exclusively on results fro m the exit interview data.
As can be seen from Figure 5, participants felt that paragraph summary was the most helpful, and the most informat ive presentation style while it was also the most difficult and the slowest one. Tag clouds were reported to be the easiest and the fas test to use, but accordingly they were the least informative and the least helpful style and the one disliked by most of the participants. On the other hand, though Aspect Oriented Sentiments didn't get the most votes, they were found to be generally helpful, easy to use, fast, and informat ive and the part icipants liked them the most. Group samples were relatively less helpful, more difficult, and slower.

Users' opinion on the presentation styles
We were interested in finding out the major reasons influencing users' preference of the various opinion summarizat ion styles. It appears that "Comprehensiveness", "Organization/Categorization" and "Ease of use" are equally important key reasons affecting users' preference o f the p resentation styles . The "Appearance", "Time", and "Length/Amount" were found to be less important reasons to the participants. Table 2 shows the distribution of reasons coded across participants. To generate the table, each unique code instance was counted for each participant.
We further investigated the distribution of the identified reasons across the four presentation styles. Table 3 shows the top 5 reasons for each style. Tag clouds received positive comments on "Appearance" and "Time" and a nearly equal amount of positive and negative comments on "ease of use." However, most of the participants agreed that they were negative regarding "co mprehensiveness." This finding can e xplain the above-mentioned finding that participants disliked tag clouds the most. As mentioned by participants, "They don't, they don't have details." All o f the top 5 reasons related to aspect oriented sentiments were positive and covered all of the main reason categories except "time." The participants liked them because they "contain negative and neutral and the positive opinions ," were "very convenient or easy to read," and "very clear, brief," among other reasons. This finding correlates with the findings in Figure 5 and also exp lains why the participants liked aspect oriented se ntiments the most. Most participants found the paragraph summary good in terms of

# Participants
"comprehensiveness" and some liked its organization and found the formatting was "…what's most normal for me." But on the other hand, the paragraph summary received negative comments regarding "time" and "length." The participants found it "too long to read" and they needed to "Spend time reading it." Co mpared with the previous three styles, the group sample received far fewer co mments. Some of the participants mentioned that it was good in terms o f "co mprehensiveness" and "organization," but, some d idn't like it because of the "length," "ease of use," and "organization." Typical user comments can be found in Table 3.
Unsurprisingly, in both paragraph summary and the group sample styles, comprehensiveness was the predominant reason in users' preference decision. Specifically, participants claimed that the paragraph summary is "getting a lot of information in a fairly simple package" and the group sample helps them "imagine what if I had that product." For tag clouds, though the participants liked them because they were "much faster" and "the font size was there for the words ," they agreed that "They don't, they don't give details" and were negative regarding "comprehensiveness." On the contrary, the paragraph summary was long and time-consuming, but most of the part icipants found it provided "a lot of information in a fairly simple package" and was positive in its "comprehensiveness." Overall, aspect oriented sentiments were the best among the four styles. They were brief and, at the same t ime, co mprehensive. The part icipants found them easy to use and liked their appearance and organization.  In those group samples, that I think was too large is the content was too large. E2UN E2U 5 because they tended to repeat the same information ORGN ORG 5 It was not categorized properly. And if it isn't organized properly, it's confusing.

Discussion
In this paper, we were interested in discovering users' preferences and the reasons affecting their preferences of the representation styles in an opinion summarization card-sorting task.
Results demonstrate that: (1) Aspect oriented sentiments are the most preferred presentation style; (2) co mprehensiveness, time, organization/categorizat ion, length/amount, appearance, and ease of use are the major reasons impacting users' preferences of a presentation style in making decisions in a product review task.
Our results supported the finding reported in [3] in that our participants disliked the tag clouds the most. As [3] pointed out, "tags are useful for grouping art icles into broad categories, but less effective in indicating the particular content of an article " In our study, the participants acknowledged that tag clouds were the easiest to use because "We can construe what the product was like in very short time,," and the fastest to use because "It's very fast; the tag clouds was much faster." But, in making the decision on the usefulness of the presentation styles, their first priority was the comprehensiveness of the information in the summary presentation. As mentioned by participants, they understood people liked the tag clouds because of the "font size" and "color," but, they disliked them because they don't "give details" and "drive my decision making." This finding raised an important issue here for the design of info rmat ion systems: Ho w can the user interface balance the need for co mprehensiveness of information and the need to provide key features enabling users to quickly grasp the desired information? On the other hand, [3] reported that, compared with humanassigned tags, automated tagging produces "more focused, topical clusters ." The tag clouds in our study were generated by the turkers. As a result, the co mprehensiveness of automatically generated tags might be our future research direction .
It was interesting to learn that participants liked the aspect oriented sentiments the mo st. Organization/ categorization is the most critical reason in users' decision ma king relevant to this presentation style. Most importantly, they liked them because they contain "negative and neutral, and the positive opinions," "color," "nu mber" and "percentages." Our results indicated that there may be a relationship between consumers' informat ion needs and their preference of an opinion summary presentation style. With regards to the opinion summary of a cleaning robot, it is within expectation that consumers may want to look for informat ion about system usability, performance, and reliability. This factor may contribute to the finding that participants prefer aspect oriented sentiments, not tag clouds.
It can also be noticed that there exist biases potentially introduced by manually generated summary presentations. Our summarizat ions were generated by human turkers, but, this generation could have been influenced by the instructions and contents distributed by the researchers.
Results of this study have practical imp licat ions for developers of text summarization. A few design considerations for imp roving usability and user experiences emerged based on participant responses and our observations. First, a deeper understanding of users' information behavior and their in formation needs in using info rmat ion systems supporting consumer decision-making is important. In this study, we made a step towards this understanding in using turkers to generate the summary reviews. Second, after having identified key features in the consumer decision -making system, a good design should well balance the nu mber of the features and the amount of information p rovided in the interface. Th ird, an appropriate and co mprehensive organization/categorizat ion scheme should be selected in terms of targeted user groups and task and design considerations. Many participants expressed their opinions about the importance of organization/categorization. We feel it should be given greater attention in the design process in the future experiments.

Conclusion
This paper reports a study comparing the effect iveness of four major styles of opinion summarization in a consumer purchase decision scenario. The study leverages the power of the crowd to bypass issues of text min ing quality in order to reach mo re meaningful conclusions.
Our next step is to design and implement an experimental system based on the findings of this study. Such an experimental system will provide customers with a better view in the system interface. Additionally, the experimental system will be compared with our baseline system in a user-centered lab experiment to test its effectiveness and efficiency. Our goal is to contribute to improving the user experience and usability of information systems that support consumer decision-making.
As a lab-based user-centered study, limitations exist. In this experiment, the generalizab ility of the findings was restricted by the limited types of tasks, the number of topics, and the sample pool. Additionally, the coding scheme we generated is a simple, init ial one. Deeper, more fine-tuned coding and analysis could be applied to the data in a subsequent analysis . Despite the limitations, the results of this type of research will have imp licat ions for the design of information systems that support consumer decision-making.