Object similarity affects the perceptual strategy underlying invariant visual object recognition in rats

In recent years, a number of studies have explored the possible use of rats as models of high-level visual functions. One central question at the root of such an investigation is to understand whether rat object vision relies on the processing of visual shape features or, rather, on lower-order image properties (e.g., overall brightness). In a recent study, we have shown that rats are capable of extracting multiple features of an object that are diagnostic of its identity, at least when those features are, structure-wise, distinct enough to be parsed by the rat visual system. In the present study, we have assessed the impact of object structure on rat perceptual strategy. We trained rats to discriminate between two structurally similar objects, and compared their recognition strategies with those reported in our previous study. We found that, under conditions of lower stimulus discriminability, rat visual discrimination strategy becomes more view-dependent and subject-dependent. Rats were still able to recognize the target objects, in a way that was largely tolerant (i.e., invariant) to object transformation; however, the larger structural and pixel-wise similarity affected the way objects were processed. Compared to the findings of our previous study, the patterns of diagnostic features were: (i) smaller and more scattered; (ii) only partially preserved across object views; and (iii) only partially reproducible across rats. On the other hand, rats were still found to adopt a multi-featural processing strategy and to make use of part of the optimal discriminatory information afforded by the two objects. Our findings suggest that, as in humans, rat invariant recognition can flexibly rely on either view-invariant representations of distinctive object features or view-specific object representations, acquired through learning

Shape similarity, better than semantic membership, accounts for the structure of visual object representations in a population of monkey inferotemporal neurons

The anterior inferotemporal cortex (IT) is the highest stage along the hierarchy of visual areas that, in primates, processes visual objects. Although several lines of evidence suggest that IT primarily represents visual shape information, some recent studies have argued that neuronal ensembles in IT code the semantic membership of visual objects (i.e., represent conceptual classes such as animate and inanimate objects). In this study, we investigated to what extent semantic, rather than purely visual information, is represented in IT by performing a multivariate analysis of IT responses to a set of visual objects. By relying on a variety of machine-learning approaches (including a cutting-edge clustering algorithm that has been recently developed in the domain of statistical physics), we found that, in most instances, IT representation of visual objects is accounted for by their similarity at the level of shape or, more surprisingly, low-level visual properties. Only in a few cases we observed IT representations of semantic classes that were not explainable by the visual similarity of their members. Overall, these findings reassert the primary function of IT as a conveyor of explicit visual shape information, and reveal that low-level visual properties are represented in IT to a greater extent than previously appreciated. In addition, our work demonstrates how combining a variety of state-of-the-art multivariate approaches, and carefully estimating the contribution of shape similarity to the representation of object categories, can substantially advance our understanding of neuronal coding of visual objects in cortex

Input-driven unsupervised learning in recurrent neural networks

Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is an attractor neural network with Hebbian learning (e.g. the Hopfield model). The model simplicity and the locality of the synaptic update rules come at the cost of a limited storage capacity, compared with the capacity achieved with supervised learning algorithms, whose biological plausibility is questionable. Here, we present an on-line learning rule for a recurrent neural network that achieves near-optimal performance without an explicit supervisory error signal and using only locally accessible information, and which is therefore biologically plausible. The fully connected network consists of excitatory units with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the patterns to be memorized are presented on-line as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs (’local fields’). Synapses corresponding to active inputs are modified as a function of the position of the local field with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. An additional parameter of the model allows to trade storage capacity for robustness, i.e. increased size of the basins of attraction. We simulated a network of 1001 excitatory neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction: our results show that, for any given basin size, our network more than doubles the storage capacity, compared with a standard Hopfield network. Our learning rule is consistent with available experimental data documenting how plasticity depends on firing rate. It predicts that at high enough firing rates, no potentiation should occur

From luminance to semantics: how natural objects are represented in monkey inferotemporal cortex.

In primates, visual object information is processed through a hierarchy of cortico-cortical stages that culminates with the inferotemporal cortex (IT). Although the nature of visual processing in IT is still poorly understood, several lines of evidence suggest that IT conveys an explicit object representation that can directly serve as a basis for decision, action and memory - e.g., it can support flexible formation of semantic categories in downstream areas, such as prefrontal and perirhinal cortex. However, some recent studies (Kiani et al, 2007; Kriegeskorte et al, 2008) have argued that IT neuronal ensembles may themselves code the semantic membership of visual objects (i.e., represent such abstract conceptual classes such as animate and inanimate objects, animals, etc). In this study, we have applied an array of multi-variate computational approaches to investigate the nature of visual objects' representation in IT. Our results show that IT neuronal ensembles represent a surprisingly broad spectrum of visual features complexity, ranging from low-level visual properties (e.g., brightness), to visual patterns of intermediate complexity (e.g., star-like shapes), to complex objects (e.g., four-leg animals) that appear to be coded so invariantly that their clustering in the IT neuronal space is not easily accountable by any similarity metric we used. On the one hand, these findings show that IT supports recognition of low-level properties of the visual input that are typically though to be extracted by lower-level visual areas. On the other hand, IT appears to convey such an explicit representation of some object classes that coding of semantic membership in IT (at least for a few categories) cannot be excluded. Overall, these results shed new light on IT amazing pluripotency in supporting recognition tasks as diverse as detection of brightness and categorization of complex shapes

From luminance to semantics: how natural objects are represented in monkey inferotemporal cortex.

In primates, visual object information is processed through a hierarchy of cortico-cortical stages that culminates with the inferotemporal cortex (IT). Although the nature of visual processing in IT is still poorly understood, several lines of evidence suggest that IT conveys an explicit object representation that can directly serve as a basis for decision, action and memory - e.g., it can support flexible formation of semantic categories in downstream areas, such as prefrontal and perirhinal cortex. However, some recent studies (Kiani et al, 2007; Kriegeskorte et al, 2008) have argued that IT neuronal ensembles may themselves code the semantic membership of visual objects (i.e., represent such abstract conceptual classes such as animate and inanimate objects, animals, etc). In this study, we have applied an array of multi-variate computational approaches to investigate the nature of visual objects' representation in IT. Our results show that IT neuronal ensembles represent a surprisingly broad spectrum of visual features complexity, ranging from low-level visual properties (e.g., brightness), to visual patterns of intermediate complexity (e.g., star-like shapes), to complex objects (e.g., four-leg animals) that appear to be coded so invariantly that their clustering in the IT neuronal space is not easily accountable by any similarity metric we used. On the one hand, these findings show that IT supports recognition of low-level properties of the visual input that are typically though to be extracted by lower-level visual areas. On the other hand, IT appears to convey such an explicit representation of some object classes that coding of semantic membership in IT (at least for a few categories) cannot be excluded. Overall, these results shed new light on IT amazing pluripotency in supporting recognition tasks as diverse as detection of brightness and categorization of complex shapes

Similarity matrix, hierarchical clustering and PCA of IT population responses to visual objects.

<p>(A) Each pixel in the matrix color-codes the correlation (i.e., similarity) between the neuronal population vectors representing a pair of visual objects. The order of the objects along the axes is defined by the dendrogram produced by hierarchical clustering of the population vectors (to avoid crowding, one every three objects is shown; the complete object set is shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003167#pcbi-1003167-g002" target="_blank">Fig. 2</a>). The first two branches of the dendrogram (shown at the top) are colored in cyan and magenta. (B) The fraction of animate and inanimate objects is not significantly different in the first two branches of the dendrogram (NS, <i>p</i>>0.1, <i>χ</i>² test). (C) The proportion of large and small objects is significantly different in the first two branches of the dendrogram (**, <i>p</i><0.001, <i>χ</i>² test), (D) Layout of visual objects in the two-dimensional space defined by the first two principal components of the IT population responses (to avoid crowding, only some of the objects are shown). (E) Object area and object ranking along the first principal component are linearly related (<i>r</i> = −0.69, <i>p</i><0.001, <i>t</i>-test).</p

Overlapping between low-level categories and D-MST neuronal-based clusters.

<p>The table reports the overlap (fifth column) between each low-level category (first column) and the D-MST neuronal-based cluster (second column) containing the best matching sub-tree of contiguous objects. Same table structure and symbols as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003167#pcbi-1003167-t001" target="_blank">Table 1</a>.</p

Overlapping between shape-based categories and D-MST neuronal-based clusters.

<p>The table reports the overlap (fifth column) between each shape-based category (first column) and the D-MST neuronal-based cluster (second column) containing the best matching sub-tree of contiguous objects. Same table structure and symbols as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003167#pcbi-1003167-t001" target="_blank">Table 1</a>.</p

Overlap between <i>k</i>-means clusters in the IT neuronal space and object categories of the clustering hypotheses.

<p>(A) Fifteen object clusters obtained by a typical run of the <i>k</i>-means algorithm over the IT neuronal representation space. The clusters' arrangement was determined by applying a hierarchical clustering algorithm to their centroids (see the dendrogram on the top; the same approach was used to arrange the shape-based categories shown in C, which resulted from the <i>k</i>-means object clustering in the output layer of an object recognition model <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003167#pcbi.1003167-Mutch1" target="_blank">[44]</a>). (B–D) The semantic (B), shape-based (C) and low-level (D) categories that significantly overlapped with some of the neuronal-based clusters shown in A. Overlapping neuronal-based clusters and categories are indicated by matching names (e.g., <i>faces</i>) in A and B–D, with the objects in common between a cluster and a category enclosed by either a yellow (semantic), a red (shape-based) or a cyan (low-level) frame. (E) Average number of significant overlaps between neuronal-based clusters and semantic (first bar), shape-based (second bar) and low-level (third bar) categories across 1,000 runs of the <i>k</i>-means algorithm over both the neuronal representation space and the model representation space. The yellow, red and cyan striped portion of the first bar indicates the number of neuronal-based clusters that significantly overlapped with both a semantic category and either a shape-based or a low-level category.</p