Boris B. Vladimirskiy

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The unsupervised categorization of sensory stimuli is typically attributed to feedforward processing in a hierarchy of cortical areas. This purely sensory-driven view of cortical processing, however, ignores any internal modulation, e.g., by top-down attentional signals or neuromodulator release. To isolate the role of internal signaling on category(More)
Reinforcement learning in neural networks requires a mechanism for exploring new network states in response to a single, nonspecific reward signal. Existing models have introduced synaptic or neuronal noise to drive this exploration. However, those types of noise tend to almost average out—precluding or significantly hindering learning —when coding in(More)
Since top-down predictions cannot be independent of the bottom-up input, the interpretation of a visual scene must be an iterative process in which the initial activation pattern relaxes to a solution matching expectation with sensory experience. However, in models, such as the one of Rao and Ballard, where top-down effects propagate over all layers of the(More)
Predictive coding has been previously introduced as a hierarchical coding framework for the visual system. At each level, activity predicted by the higher level is dynamically subtracted from the input, while the difference in activity continuously propagates further. Here we introduce modular predictive coding as a feedforward hierarchy of prediction(More)
Here we first present the framework of the classical ‘cross-level’ predictive coding (Section S.I). We show that for a single level, with a readout at the next level but without top-down connectivity, predictive coding performs principal component analysis (Section S.II). We next argue that cross-level predictive coding across 2 levels can essentially be(More)
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