Performance-monitoring integrated reweighting model of perceptual learning

Perceptual learning (PL) has been traditionally thought of as highly specific to stimulus properties, task and retinotopic position. This view is being progressively challenged, with accumulating evidence that learning can generalize (transfer) across various parameters under certain conditions. For example, retinotopic specificity can be diminished when the proportion of easy to hard trials is high, such as when multiple short staircases, instead of a single long one, are used during training. To date, there is a paucity of mechanistic explanations of what conditions affect transfer of learning. Here we present a model based on the popular Integrated Reweighting Theory model of PL but departing from its one-layer architecture by including a novel key feature: dynamic weighting of retinotopic-location-specific vs location-independent representations based on internal performance estimates of these representations. This dynamic weighting is closely related to gating in a mixture-of-experts architecture. Our dynamic performance-monitoring model (DPMM) unifies a variety of psychophysical data on transfer of PL, such as the short-vs-long staircase effect, as well as several findings from the double-training literature. Furthermore, the DPMM makes testable predictions and ultimately helps understand the mechanisms of generalization of PL, with potential applications to vision rehabilitation and enhancement

The "silent" surround of V1 receptive fields: theory and experiments

International audienceThe spiking response of a primary visual cortical cell to a stimulus placed within its receptive field can be up- and down-regulated by the simultaneous presentation of objects or scenes placed in the "silent" regions which surround the receptive field. We here review recent progresses that have been made both at the experimental and theoretical levels in the description of these so-called "Center/Surround" modulations and in the understanding of their neural basis. Without denying the role of a modulatory feedback from higher cortical areas recent results support the view that some of these phenomena result from the dynamic interplay between feedforward projections and horizontal intracortical connectivity in V1. Uncovering the functional role of the contextual periphery of cortical receptive fields has become an area of active investigation. The detailed comparison of electrophysiological and psychophysical data reveals strong correlations between the integrative behavior of V1 cells and some aspects of "low-level" and "mid-level" conscious perception. These suggest that as early as the V1 stage the visual system is able to make use of contextual cues to recover local visual scene properties or correct their interpretation. Promising ideas have emerged on the importance of such a strategy for the coding of visual scenes and the processing of static and moving objects

Acquisition of visual priors and induced hallucinations in chronic schizophrenia

Prominent theories suggest that symptoms of schizophrenia stem from learning deficiencies resulting in distorted internal models of the world. To test these theories further, we used a visual statistical learning task known to induce rapid implicit learning of the stimulus statistics. In this task, participants are presented with a field of coherently moving dots and are asked to report the presented direction of the dots (estimation task), and whether they saw any dots or not (detection task). Two of the directions were more frequently presented than the others. In controls, the implicit acquisition of the stimuli statistics influences their perception in two ways: (i) motion directions are perceived as being more similar to the most frequently presented directions than they really are (estimation biases); and (ii) in the absence of stimuli, participants sometimes report perceiving the most frequently presented directions (a form of hallucinations). Such behaviour is consistent with probabilistic inference, i.e. combining learnt perceptual priors with sensory evidence. We investigated whether patients with chronic, stable, treated schizophrenia (n = 20) differ from controls (n = 23) in the acquisition of the perceptual priors and/or their influence on perception. We found that although patients were slower than controls, they showed comparable acquisition of perceptual priors, approximating the stimulus statistics. This suggests that patients have no statistical learning deficits in our task. This may reflect our patients’ relative wellbeing on antipsychotic medication. Intriguingly, however, patients experienced significantly fewer (P = 0.016) hallucinations of the most frequently presented directions than controls when the stimulus was absent or when it was very weak (prior-based lapse estimations). This suggests that prior expectations had less influence on patients’ perception than on controls when stimuli were absent or below perceptual threshold

Self-Organized Artificial Grammar Learning in Spiking Neural Networks

The Artificial Grammar Learning (AGL) paradigm provides a means to study the nature of syntactic processing and implicit sequence learning. With mere exposure and without perfor-mance feedback, human beings implicitly acquire knowledge about the structural regularities implemented by complex rule systems. We investigate to which extent a generic cortical mi-crocircuit model can support formally explicit symbolic com-putations, instantiated by the same grammars used in the hu-man AGL literature and how a functional network emerges, in a self-organized manner, from exposure to this type of data. We use a concrete implementation of an input-driven recurrent network composed of noisy, spiking neurons, built according to the reservoir computing framework and dynamically shaped by a variety of synaptic and intrinsic plasticity mechanisms operating concomitantly. We show that, when shaped by plas-ticity, these models are capable of acquiring the structure of a simple grammar. When asked to judge string legality (in a manner similar to human subjects), the networks perform at a qualitatively comparable level

Temporal sequence learning via adaptation in biologically plausible spiking neural networks

Ecologically relevant computations are carried out by a complex interaction of adaptive dynamics, through a variety of activity-dependent modifications of synaptic and intrinsic neuronal properties. Such modifications ought to be robust and reliable enough to endow neuro-nal circuits with the ability to learn from and operate upon complex, dynamic environmental variables. On the lower levels of the cortical processing hierar-chy, continuous data streams representing the environ-ment are parsed, in order to isolate and attend to salient and invariant features ('perceptual objects'), upon which higher order cortical networks will operate, by flexibly evaluating the dynamic relations between such structural elements [1]. The formation of stable representations of spatial/spectral environmental features (stimulus selec-tivity) along with the related ability to discriminate such features and their combinations is known to be continu-ously shaped and refined by synaptic plasticity mechan-isms, and it has been recently demonstrated that correlation-based inhibitory plasticity has an important role to play in such computations (see, for example, [2]). However, in order to adequately process information, neural circuits must not only develop stable internal representations of perceptual objects, but also reflect and represent the continuous unfolding structure of its input, which is poised with intricate temporal dependen-cies. Much less is currently known about the acquisition of complex temporal relations between stimuli and the (possibly specialized) role played by different adaptation mechanisms involved in this process. In this work, we study the properties of biologically realistic networks of LIF neurons, with differentially modulated, dynamic excitation and inhibition, combining well established as well as more recent phenomenological models of synaptic plasticity [2,3]. Explicitly embedded or entirely self-organized, input-specific neuronal assemblies are driven by stimulus sequences that contain complex temporal dependencies and signal propagation through-out these assemblies is gated by transient disruptions of E/I balance, in order to 'prime' the network to learn the underlying transitional probabilities and input statistics through targeted modifications of these 'gating' synapses. We explore the representational properties developed by these networks and the impact of the different plasticity rules in shaping the network's learning abilities while maintaining stable global dynamics. Furthermore, we assess the network's ability to extract complex temporal dependency rules between sequence elements and to use the acquired knowledge to make predictions about upcoming sequence elements

Étude théorique des modulations centre/pourtour des propriétés des champs récepteurs du cortex visuel primaire (circuits, dynamiques et corrélats perceptifs)

La réponse des neurones du cortex visuel primaire (V1) à un stimulus présenté dans le champ récepteur peut être modulée par la stimulation du pourtour du champ récepteur. L'origine et le rôle fonctionnel de ces modulations " centre/pourtour " restent peu compris. Par la modélisation, et en interaction avec des approches psychophysiques et physiologiques, nous cherchons à répondre à 2 questions : Quels sont les circuits responsables de la diversité de ces effets ? Nous fournissons des outils théoriques pour évaluer les modèles existants, les réconcilier au sein d'un même formalisme, et comprendre comment les diverses caractéristiques spatiales des modulations centre/ pourtour peuvent résulter des propriétés connues de V1. Quelles sont les conséquences des dynamiques de ces effets sur les réponses neuronales et sur la perception visuelle? Nos résultats suggèrent que les réponses de V1 et la perception des objets visuels dépendent non seulement du contexte spatial, mais aussi du contexte temporel dans lequel ces objets sont présentés. Nous discutons les implications fonctionnelles possibles de ce mécanisme pour l'analyse d'objets statiques ou en mouvement.PARIS-BIUSJ-Thèses (751052125) / SudocPARIS-BIUSJ-Physique recherche (751052113) / SudocSudocFranceF