Neural Comput - Replicating receptive fields of simple and complex cells in primary visual cortex in a neuronal network model with temporal and population sparseness and reliability.

Tópicos

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Resumo

We propose a new principle for replicating receptive field properties of neurons in the primary visual cortex. We derive a learning rule for a feedforward network, which maintains a low firing rate for the output neurons (resulting in temporal sparseness) and allows only a small subset of the neurons in the network to fire at any given time (resulting in population sparseness). Our learning rule also sets the firing rates of the output neurons at each time step to near-maximum or near-minimum levels, resulting in neuronal reliability. The learning rule is simple enough to be written in spatially and temporally local forms. After the learning stage is performed using input image patches of natural scenes, output neurons in the model network are found to exhibit simple-cell-like receptive field properties. When the output of these simple-cell-like neurons are input to another model layer using the same learning rule, the second-layer output neurons after learning become less sensitive to the phase of gratings than the simple-cell-like input neurons. In particular, some of the second-layer output neurons become completely phase invariant, owing to the convergence of the connections from first-layer neurons with similar orientation selectivity to second-layer neurons in the model network. We examine the parameter dependencies of the receptive field properties of the model neurons after learning and discuss their biological implications. We also show that the localized learning rule is consistent with experimental results concerning neuronal plasticity and can replicate the receptive fields of simple and complex cells.

Resumo Limpo

propos new principl replic recept field properti neuron primari visual cortex deriv learn rule feedforward network maintain low fire rate output neuron result tempor spars allow small subset neuron network fire given time result popul spars learn rule also set fire rate output neuron time step nearmaximum nearminimum level result neuron reliabl learn rule simpl enough written spatial tempor local form learn stage perform use input imag patch natur scene output neuron model network found exhibit simplecelllik recept field properti output simplecelllik neuron input anoth model layer use learn rule secondlay output neuron learn becom less sensit phase grate simplecelllik input neuron particular secondlay output neuron becom complet phase invari owe converg connect firstlay neuron similar orient select secondlay neuron model network examin paramet depend recept field properti model neuron learn discuss biolog implic also show local learn rule consist experiment result concern neuron plastic can replic recept field simpl complex cell

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