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

Mario Pannunzi, Guido Gigante, Maurizio Mattia, Gustavo Deco, Stefano Fusi, Paolo Del Giudice

Research output: Contribution to journalArticleResearchpeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)3124-3137
Number of pages14
JournalJournal of Neurophysiology
Issue number11
Publication statusPublished - 1 Dec 2012
Externally publishedYes


  • Categorization learning
  • Feedback
  • Hebbian learning
  • Spiking neuronal network
  • Visual categorization

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