Attention-gated reinforcement learning of internal representations for classification

Learning selective top-down control enhances performance in a visual categorization task

We model the putative neuronal and synaptic mechanisms involved in learning a visual categorization task, taking inspiration from single-cell recordings in inferior temporal cortex (ITC). Our working hypothesis is that learning the categorization task involves both bottom-up, ITC to prefrontal cortex (PFC), and top-down (PFC to ITC) synaptic plasticity and that the latter enhances the selectivity of the ITC neurons encoding the task-relevant features of the stimuli, thereby improving the signal-to-noise ratio. We test this hypothesis by modeling both areas and their connections with spiking neurons and plastic synapses, ITC acting as a feature-selective layer and PFC as a category coding layer. This minimal model gives interesting clues as to properties and function of the selective feedback signal from PFC to ITC that help solving a categorization task. In particular, we show that, when the stimuli are very noisy because of a large number of nonrelevant features, the feedback structure helps getting better categorization performance and decreasing the reaction time. It also affects the speed and stability of the learning process and sharpens tuning curves of ITC neurons. Furthermore, the model predicts a modulation of neural activities during error trials, by which the differential selectivity of ITC neurons to task-relevant and task-irrelevant features diminishes or is even reversed, and modulations in the time course of neural activities that appear when, after learning, corrupted versions of the stimuli are input to the network.

View-invariance learning in object recognition by pigeons depends on error-driven associative learning processes

A model hypothesizing that basic mechanisms of associative learning and generalization underlie object categorization in vertebrates can account for a large body of animal and human data. Here, we report two experiments which implicate error-driven associative learning in pigeons' recognition of objects across changes in viewpoint. Experiment 1 found that object recognition across changes in viewpoint depends on how well each view predicts reward. Analyses of generalization performance, spatial position of pecks to images, and learning curves all showed behavioral patterns analogous to those found in prior studies of relative validity in associative learning. In Experiment 2, pigeons were trained to recognize objects from multiple viewpoints, which usually promotes robust performance at novel views of the trained objects. However, when the objects possessed a salient, informative metric property for solving the task, the pigeons did not show view-invariant recognition of the training objects, a result analogous to the overshadowing effect in associative learning.

A neuromorphic architecture for object recognition and motion anticipation using burst-STDP

In this work we investigate the possibilities offered by a minimal framework of artificial spiking neurons to be deployed in silico. Here we introduce a hierarchical network architecture of spiking neurons which learns to recognize moving objects in a visual environment and determine the correct motor output for each object. These tasks are learned through both supervised and unsupervised spike timing dependent plasticity (STDP). STDP is responsible for the strengthening (or weakening) of synapses in relation to pre- and post-synaptic spike times and has been described as a Hebbian paradigm taking place both in vitro and in vivo. We utilize a variation of STDP learning, called burst-STDP, which is based on the notion that, since spikes are expensive in terms of energy consumption, then strong bursting activity carries more information than single (sparse) spikes. Furthermore, this learning algorithm takes advantage of homeostatic renormalization, which has been hypothesized to promote memory consolidation during NREM sleep. Using this learning rule, we design a spiking neural network architecture capable of object recognition, motion detection, attention towards important objects, and motor control outputs. We demonstrate the abilities of our design in a simple environment with distractor objects, multiple objects moving concurrently, and in the presence of noise. Most importantly, we show how this neural network is capable of performing these tasks using a simple leaky-integrate-and-fire (LIF) neuron model with binary synapses, making it fully compatible with state-of-the-art digital neuromorphic hardware designs. As such, the building blocks and learning rules presented in this paper appear promising for scalable fully neuromorphic systems to be implemented in hardware chips.

Learning acts on distinct processes for visual form perception in the human brain

Learning is known to facilitate our ability to detect targets in clutter and optimize brain processes for successful visual recognition. Previous brain-imaging studies have focused on identifying spatial patterns (i.e., brain areas) that change with learning, implicating occipitotemporal and frontoparietal areas. However, little is known about the interactions within this network that mediate learning-dependent improvement in complex perceptual tasks (i.e., discrimination of visual forms in clutter). Here we take advantage of the complementary high spatial and temporal resolution of simultaneous EEG-fMRI to identify the learning-dependent changes in spatiotemporal brain patterns that mediate enhanced behavioral sensitivity in the discrimination of global forms after training. We measured the observers' choices when discriminating between concentric and radial patterns presented in noise before and after training. Similarly, we measured the choices of a pattern classifier when predicting each stimulus from EEG-fMRI signals. By comparing the performance of human observers and classifiers, we demonstrated that learning alters sensitivity to visual forms and EEG-fMRI activation patterns related to distinct visual recognition processes. In particular, behavioral improvement after training was associated with changes in (1) early processes involved in the integration of global forms in higher occipitotemporal and parietal areas, and (2) later processes related to categorical judgments in frontal circuits. Thus, our findings provide evidence that learning acts on distinct visual recognition processes and shapes feedforward interactions across brain areas to support performance in complex perceptual tasks.

Learning shapes spatiotemporal brain patterns for flexible categorical decisions

Learning is thought to facilitate our ability to perform complex perceptual tasks and optimize brain circuits involved in decision making. However, little is known about the experience-dependent mechanisms in the human brain that support our ability to make fine categorical judgments. Previous work has focused on identifying spatial brain patterns (i.e., areas) that change with learning. Here, we take advantage of the complementary high spatial and temporal resolution of simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) to identify the spatiotemporal dynamics between cortical networks involved in flexible category learning. Observers were trained to use different decision criteria (i.e., category boundaries) when making fine categorical judgments on morphed stimuli (i.e., radial vs. concentric patterns). Our findings demonstrate that learning acts on a feedback-based circuit that supports fine categorical judgments. Experience-dependent changes in the behavioral decision criterion were associated with changes in later perceptual processes engaging higher occipitotemporal and frontoparietal circuits. In contrast, category learning did not modulate early processes in a medial frontotemporal network that are thought to support the coarse interpretation of visual scenes. These findings provide evidence that learning flexible criteria for fine categorical judgments acts on distinct spatiotemporal brain circuits and shapes the readout of sensory signals that provide evidence for categorical decisions.

Neural representations for object perception: structure, category, and adaptive coding

Object perception is one of the most remarkable capacities of the primate brain. Owing to the large and indeterminate dimensionality of object space, the neural basis of object perception has been difficult to study and remains controversial. Recent work has provided a more precise picture of how 2D and 3D object structure is encoded in intermediate and higher-level visual cortices. Yet, other studies suggest that higher-level visual cortex represents categorical identity rather than structure. Furthermore, object responses are surprisingly adaptive to changes in environmental statistics, implying that learning through evolution, development, and also shorter-term experience during adulthood may optimize the object code. Future progress in reconciling these findings will depend on more effective sampling of the object domain and direct comparison of these competing hypotheses.

Practicing coarse orientation discrimination improves orientation signals in macaque cortical area v4

Practice improves the performance in visual tasks, but mechanisms underlying this adult brain plasticity are unclear. Single-cell studies reported no [1], weak [2], or moderate [3, 4] perceptual learning-related changes in macaque visual areas V1 and V4, whereas none were found in middle temporal (MT) [5]. These conflicting results and modeling of human (e.g., [6, 7]) and monkey data [8] suggested that changes in the readout of visual cortical signals underlie perceptual learning, rather than changes in these signals. In the V4 learning studies, monkeys discriminated small differences in orientation, whereas in the MT study, the animals discriminated opponent motion directions. Analogous to the latter study, we trained monkeys to discriminate static orthogonal orientations masked by noise. V4 neurons showed robust increases in their capacity to discriminate the trained orientations during the course of the training. This effect was observed during discrimination and passive fixation but specifically for the trained orientations. The improvement in neural discrimination was due to decreased response variability and an increase of the difference between the mean responses for the two trained orientations. These findings demonstrate that perceptual learning in a coarse discrimination task indeed can change the response properties of a cortical sensory area.

Bayesian cue integration as a developmental outcome of reward mediated learning

Average human behavior in cue combination tasks is well predicted by bayesian inference models. As this capability is acquired over developmental timescales, the question arises, how it is learned. Here we investigated whether reward dependent learning, that is well established at the computational, behavioral, and neuronal levels, could contribute to this development. It is shown that a model free reinforcement learning algorithm can indeed learn to do cue integration, i.e. weight uncertain cues according to their respective reliabilities and even do so if reliabilities are changing. We also consider the case of causal inference where multimodal signals can originate from one or multiple separate objects and should not always be integrated. In this case, the learner is shown to develop a behavior that is closest to bayesian model averaging. We conclude that reward mediated learning could be a driving force for the development of cue integration and causal inference.

The human Turing machine: a neural framework for mental programs

In recent years much has been learned about how a single computational processing step is implemented in the brain. By contrast, we still have surprisingly little knowledge of the neuronal mechanisms by which multiple such operations are sequentially assembled into mental algorithms. We outline a theory of how individual neural processing steps might be combined into serial programs. We propose a hybrid neuronal device: each step involves massively parallel computation that feeds a slow and serial production system. Production selection is mediated by a system of competing accumulator neurons that extends the role of these neurons beyond the selection of a motor action. Productions change the state of sensory and mnemonic neurons and iteration of such cycles provides a basis for mental programs.

Learning alters the tuning of functional magnetic resonance imaging patterns for visual forms

Learning is thought to facilitate the recognition of objects by optimizing the tuning of visual neurons to behaviorally relevant features. However, the learning mechanisms that shape neural selectivity for visual forms in the human brain remain essentially unknown. Here, we combine behavioral and functional magnetic resonance imaging (fMRI) measurements to test the mechanisms that mediate enhanced behavioral sensitivity in the discrimination of visual forms after training. In particular, we used high-resolution fMRI and multivoxel pattern classification methods to investigate fine learning-dependent changes in neural preference for global forms. We measured the observers' choices when discriminating between concentric and radial patterns presented in noise before and after training. Similarly, we measured the choices of a pattern classifier when predicting each stimulus from fMRI activity. Comparing the performance of human observers and classifiers demonstrated that learning alters the observers' sensitivity to visual forms and the tuning of fMRI activation patterns in visual areas selective for task-relevant features. In particular, training on low-signal stimuli enhanced the amplitude but reduced the width of pattern-based tuning functions in higher dorsal and ventral visual areas. Thus, our findings suggest that learning of visual patterns is implemented by enhancing the response to the preferred stimulus category and reducing the response to nonpreferred stimuli in higher extrastriate visual cortex.

Spatial and non-spatial functions of the parietal cortex

Although the parietal cortex is traditionally associated with spatial attention and sensorimotor integration, recent evidence also implicates it in higher order cognitive functions. We review relevant results from neuron recording studies showing that inferior parietal neurons integrate information regarding target location with a variety of non-spatial signals. Some of these signals are modulatory and alter a stimulus-evoked response according to the action, category, or reward associated with the stimulus. Other non-spatial inputs act independently, encoding the context or rules of a task even before the presentation of a specific target. Despite the ubiquity of non-spatial information in individual neurons, reversible inactivation of the parietal lobe affects only spatial orienting of attention and gaze, but not non-spatial aspects of performance. This suggests that non-spatial signals contribute to an underlying spatial computation, possibly allowing the brain to determine which targets are worthy of attention or action in a given task context.

Learning-dependent plasticity with and without training in the human brain

Long-term experience through development and evolution and shorter-term training in adulthood have both been suggested to contribute to the optimization of visual functions that mediate our ability to interpret complex scenes. However, the brain plasticity mechanisms that mediate the detection of objects in cluttered scenes remain largely unknown. Here, we combine behavioral and functional MRI (fMRI) measurements to investigate the human-brain mechanisms that mediate our ability to learn statistical regularities and detect targets in clutter. We show two different routes to visual learning in clutter with discrete brain plasticity signatures. Specifically, opportunistic learning of regularities typical in natural contours (i.e., collinearity) can occur simply through frequent exposure, generalize across untrained stimulus features, and shape processing in occipitotemporal regions implicated in the representation of global forms. In contrast, learning to integrate discontinuities (i.e., elements orthogonal to contour paths) requires task-specific training (bootstrap-based learning), is stimulus-dependent, and enhances processing in intraparietal regions implicated in attention-gated learning. We propose that long-term experience with statistical regularities may facilitate opportunistic learning of collinear contours, whereas learning to integrate discontinuities entails bootstrap-based training for the detection of contours in clutter. These findings provide insights in understanding how long-term experience and short-term training interact to shape the optimization of visual recognition processes.

Dissociable Neural Effects of Long-term Stimulus–Reward Pairing in Macaque Visual Cortex

It has been proposed that perceptual learning may occur through a reinforcement process, in which consistently pairing stimuli with reward is sufficient for learning. We tested whether stimulus–reward pairing is sufficient to increase the sensorial representation of a stimulus by recording local field potentials (LFPs) in macaque extrastriate area V4 with chronically implanted electrodes. Two oriented gratings were repeatedly presented; one was paired with a fluid reward, whereas no reward was given at any other time. During the course of conditioning the LFP increased for the rewarded compared to the unrewarded orientation. The time course of the effect of stimulus–reward pairing and its reversal differed between an early and late interval of the LFP response: a fast change in the later part of the neural response that was dissociated from a slower change in the early part of the response. The fast change of the late interval LFP suggests that this late LFP change is related to enhanced attention during the presentation of the rewarded stimulus. The slower time course of the early interval response suggests an effect of sensorial learning. Thus, simple stimulus–reward pairing is sufficient to strengthen stimulus representations in visual cortex and does this by means of two dissociable mechanisms.

Perceptual learning rules based on reinforcers and attention

How does the brain learn those visual features that are relevant for behavior? In this article, we focus on two factors that guide plasticity of visual representations. First, reinforcers cause the global release of diffusive neuromodulatory signals that gate plasticity. Second, attentional feedback signals highlight the chain of neurons between sensory and motor cortex responsible for the selected action. We here propose that the attentional feedback signals guide learning by suppressing plasticity of irrelevant features while permitting the learning of relevant ones. By hypothesizing that sensory signals that are too weak to be perceived can escape from this inhibitory feedback, we bring attentional learning theories and theories that emphasized the importance of neuromodulatory signals into a single, unified framework.

Advances in visual perceptual learning and plasticity

Visual perceptual learning (VPL) is defined as a long-term improvement in performance on a visual task. In recent years, the idea that conscious effort is necessary for VPL to occur has been challenged by research suggesting the involvement of more implicit processing mechanisms, such as reinforcement-driven processing and consolidation. In addition, we have learnt much about the neural substrates of VPL and it has become evident that changes in visual areas and regions beyond the visual cortex can take place during VPL.

Mechanisms of visual perceptual learning in macaque visual cortex

The neural mechanisms underlying behavioral improvement in the detection or discrimination of visual stimuli following learning are still ill understood. Studies in nonhuman primates have shown relatively small and, across studies, variable effects of fine discrimination learning in primary visual cortex when tested outside the context of the learned task. At later stages, such as extrastriate area V4, extensive practice in fine discrimination produces more consistent effects upon responses and neural tuning. In V1 and V4, the effects of learning were most prominent in those neurons that can contribute the most reliable information about the trained stimuli. I suggest that, depending on the particulars of the task demands, neurons at various stages of stimulus and task processing can change their tuning and responses, so that execution of the task will produce a higher frequency of reward. I speculate that the sort of changes that will occur depend on the task and on stimulus analysis requirements, and they may vary from changes in bottom-up stimulus processing/tuning within early visual areas or more efficient readout of early visual areas to top-down driven changes in response properties of these areas.

Visual learning for perceptual and categorical decisions in the human brain

Successful actions and interactions in the complex environments we inhabit entail making fast and optimal perceptual decisions. Extracting the key features from our sensory experiences and deciding how to interpret them is a computationally challenging task that is far from understood. Accumulating evidence suggests that the brain may solve this challenge by combining sensory information and previous knowledge about the environment acquired through evolution, development, and everyday experience. Here, we review the role of visual learning and experience-dependent plasticity in shaping decisions. We propose that learning plays an important role in translating sensory experiences to decisions and actions by shaping neural representations across cortical circuits in a task-dependent manner.

Flexible learning of natural statistics in the human brain

The ability to detect and identify targets in cluttered scenes is a critical skill for survival and interactions. To solve this challenge the brain has optimized mechanisms for capitalizing on frequently occurring regularities in the environment. Although evolution and development have been suggested to shape the brain's architecture in a manner that resembles these natural statistics, we provide novel evidence that short-term experience in adulthood may modify the brain's functional organization to support integration of signals atypical of shape contours in natural scenes. Although collinearity is a prevalent principle for perceptual integration in natural scenes, we show that observers learn to exploit other image regularities (i.e., orthogonal alignments of segments at an angle to the contour path) that typically signify discontinuities. Combining behavioral and functional MRI measurements, we demonstrate that this flexible learning is mediated by changes in the neural representations of behaviorally relevant image regularities primarily in dorsal visual areas. These changes in neural sensitivity are in line with changes in perceptual sensitivity for the detection of orthogonal contours and are evident only in observers that show significant performance improvement. In contrast, changes in the activation extent in frontoparietal regions are evident independent of performance changes, may support the detection of salient regions, and modulate perceptual integration in occipitotemporal areas in a top-down manner. Thus experience at shorter timescales in adulthood supports the adaptive functional optimization of visual circuits for flexible interpretation of natural scenes.

Cholinergic pairing with visual activation results in long-term enhancement of visual evoked potentials

Acetylcholine (ACh) contributes to learning processes by modulating cortical plasticity in terms of intensity of neuronal activity and selectivity properties of cortical neurons. However, it is not known if ACh induces long term effects within the primary visual cortex (V1) that could sustain visual learning mechanisms. In the present study we analyzed visual evoked potentials (VEPs) in V1 of rats during a 4–8 h period after coupling visual stimulation to an intracortical injection of ACh analog carbachol or stimulation of basal forebrain. To clarify the action of ACh on VEP activity in V1, we individually pre-injected muscarinic (scopolamine), nicotinic (mecamylamine), α7 (methyllycaconitine), and NMDA (CPP) receptor antagonists before carbachol infusion. Stimulation of the cholinergic system paired with visual stimulation significantly increased VEP amplitude (56%) during a 6 h period. Pre-treatment with scopolamine, mecamylamine and CPP completely abolished this long-term enhancement, while α7 inhibition induced an instant increase of VEP amplitude. This suggests a role of ACh in facilitating visual stimuli responsiveness through mechanisms comparable to LTP which involve nicotinic and muscarinic receptors with an interaction of NMDA transmission in the visual cortex.

Rewards evoke learning of unconsciously processed visual stimuli in adult humans

The study of human learning is complicated by the myriad of processing elements involved in conducting any behavioral task. In the case of visual perceptual learning, there has been significant controversy regarding the task processes that guide the formation of this learning. However, there is a developing consensus that top-down, task-related factors are required for such learning to take place. Here we challenge this idea by use of a novel procedure in which human participants, who were deprived of food and water, passively viewed visual stimuli while receiving occasional drops of water as rewards. Visual orientation stimuli, which were temporally paired with the liquid rewards, were viewed monocularly and rendered imperceptible by continuously flashing contour-rich patterns to the other eye. Results show that visual learning can be formed in human adults through stimulus-reward pairing in the absence of a task and without awareness of the stimulus presentation or reward contingencies.

Functional connections between visual areas in extracting object features critical for a visual categorization task

The ability to group visual stimuli into meaningful categories is a fundamental cognitive process. Several experiments have been made to investigate the neural mechanism of visual categorization task. Although experimental evidence is known that prefrontal cortex (PFC) and inferior temporal cortex (ITC) sensitively respond in categorization task, little is known about the functional role of interaction between PFC and ITC in categorization task. To address this issue, we present a model, which performs categorization via an interaction between ITC, PFC, and posterior parietal (PP). Using the model, we show here that the functional connections of synapses between neurons in these areas are organized by the learning depending on a reward that is given only by correct behaviors for the task. We also show that the feedback from PFC to ITC allows the sensitivity enhancement of the ITC neurons encoding the object features critical for the task, and the feedback from PFC to PP works as a spatial attention required for finding object feature critical for the task. The model seems to be comparable with experimental data about categorization.

Parietal control of attentional guidance: the significance of sensory, motivational and motor factors

The lateral intraparietal area (LIP), a portion of monkey posterior parietal cortex, has been implicated in spatial attention. We review recent evidence showing that LIP encodes a priority map of the external environment that specifies the momentary locus of attention and is activated in a variety of behavioral tasks. The priority map in LIP is shaped by task-specific motor, cognitive and motivational variables, the functional significance of which is not entirely understood. We suggest that these modulations represent teaching signals by which the brain learns to identify attentional priority of various stimuli based on the task-specific associations between these stimuli, the required action and expected outcome.

Learning to link visual contours

In complex visual scenes, linking related contour elements is important for object recognition. This process, thought to be stimulus driven and hard wired, has substrates in primary visual cortex (V1). Here, however, we find contour integration in V1 to depend strongly on perceptual learning and top-down influences that are specific to contour detection. In naive monkeys, the information about contours embedded in complex backgrounds is absent in V1 neuronal responses and is independent of the locus of spatial attention. Training animals to find embedded contours induces strong contour-related responses specific to the trained retinotopic region. These responses are most robust when animals perform the contour detection task but disappear under anesthesia. Our findings suggest that top-down influences dynamically adapt neural circuits according to specific perceptual tasks. This may serve as a general neuronal mechanism of perceptual learning and reflect top-down mediated changes in cortical states.

Elman Backpropagation as Reinforcement for Simple Recurrent Networks

Simple recurrent networks (SRNs) in symbolic time-series prediction (e.g., language processing models) are frequently trained with gradient descent--based learning algorithms, notably with variants of backpropagation (BP). A major drawback for the cognitive plausibility of BP is that it is a supervised scheme in which a teacher has to provide a fully specified target answer. Yet agents in natural environments often receive summary feedback about the degree of success or failure only, a view adopted in reinforcement learning schemes.

In this work, we show that for SRNs in prediction tasks for which there is a probability interpretation of the network's output vector, Elman BP can be reimplemented as a reinforcement learning scheme for which the expected weight updates agree with the ones from traditional Elman BP. Network simulations on formal languages corroborate this result and show that the learning behaviors of Elman backpropagation and its reinforcement variant are very similar also in online learning tasks.

Attention alters spatial integration in macaque V1 in an eccentricity-dependent manner

Attention can selectively enhance neuronal responses and exclude external noise, but the neuronal computations that underlie these effects remain unknown. At the neuronal level, noise exclusion might result in altered spatial integration properties. We tested this proposal by recording neuronal activity and length tuning in neurons of the primary visual cortex of the macaque when attention was directed toward or away from stimuli presented in each neuron's classical receptive field. For cells with central-parafoveal receptive fields, attention reduced spatial integration, as demonstrated by a reduction in preferred stimulus length and in the size of the spatial summation area. Conversely, in cells that represented more peripheral locations, attention increased spatial integration by increasing the cell's summation area. This previously unknown dichotomy between central and peripheral vision could support accurate analysis of attended foveal objects and target selection for impending eye movements to peripheral objects.

Synergies between intrinsic and synaptic plasticity mechanisms

We propose a model of intrinsic plasticity for a continuous activation model neuron based on information theory. We then show how intrinsic and synaptic plasticity mechanisms interact and allow the neuron to discover heavy-tailed directions in the input. We also demonstrate that intrinsic plasticity may be an alternative explanation for the sliding threshold postulated in the BCM theory of synaptic plasticity. We present a theoretical analysis of the interaction of intrinsic plasticity with different Hebbian learning rules for the case of clustered inputs. Finally, we perform experiments on the "bars" problem, a popular nonlinear independent component analysis problem.

Learning to see the difference specifically alters the most informative V4 neurons

Perceptual learning is an instance of adult plasticity whereby training in a sensory (e.g., a visual task) results in neuronal changes leading to an improved ability to perform the task. Yet studies in primary visual cortex have found that changes in neuronal response properties were relatively modest. The present study examines the effects of training in an orientation discrimination task on the response properties of V4 neurons in awake rhesus monkeys. Results indicate that the changes induced in V4 are indeed larger than those in V1. Nonspecific effects of training included a decrease in response variance, and an increase in overall orientation selectivity in V4. The orientation-specific changes involved a local steepening in the orientation tuning curve around the trained orientation that selectively improved orientation discriminability at the trained orientation. Moreover, these changes were largely confined to the population of neurons whose orientation tuning was optimal for signaling small differences in orientation at the trained orientation. Finally, the modifications were restricted to the part of the tuning curve close to the trained orientation. Thus, we conclude that it is the most informative V4 neurons, those most directly involved in the discrimination, that are specifically modified by perceptual learning.

Envisioning the reward

The primary visual cortex (area V1) is for vision. At least, that is what most researchers believe. However, in a recent issue of Science, Shuler and Bear demonstrate a correlate of reward timing in area V1. This surprising result indicates that brain circuits for reward processing are more extensive than expected and that area V1 has more functionality than previously thought.

Learning to attend: modeling the shaping of selectivity in infero-temporal cortex in a categorization task

Recent experiments on behaving monkeys have shown that learning a visual categorization task makes the neurons in infero-temporal cortex (ITC) more selective to the task-relevant features of the stimuli (Sigala and Logothetis in Nature 415 318-320, 2002). We hypothesize that such a selectivity modulation emerges from the interaction between ITC and other cortical area, presumably the prefrontal cortex (PFC), where the previously learned stimulus categories are encoded. We propose a biologically inspired model of excitatory and inhibitory spiking neurons with plastic synapses, modified according to a reward based Hebbian learning rule, to explain the experimental results and test the validity of our hypothesis. We assume that the ITC neurons, receiving feature selective inputs, form stronger connections with the category specific neurons to which they are consistently associated in rewarded trials. After learning, the top-down influence of PFC neurons enhances the selectivity of the ITC neurons encoding the behaviorally relevant features of the stimuli, as observed in the experiments. We conclude that the perceptual representation in visual areas like ITC can be strongly affected by the interaction with other areas which are devoted to higher cognitive functions.