Access count of this item: 135

Files in This Item:
File Description SizeFormat 
neco_a_00341.pdf1.97 MBAdobe PDFView/Open
Title: Replicating receptive fields of simple and complex cells in primary visual cortex in a neuronal network model with temporal and population sparseness and reliability.
Authors: Tanaka, Takuma
Aoyagi, Toshio  kyouindb  KAKEN_id
Kaneko, Takeshi  kyouindb  KAKEN_id
Author's alias: 田中, 琢真
金子, 武嗣
Issue Date: Oct-2012
Publisher: Massachusetts Institute of Technology Press
Journal title: Neural computation
Volume: 24
Issue: 10
Start page: 2700
End page: 2725
Abstract: 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.
Rights: © 2012 Massachusetts Institute of Technology
DOI(Published Version): 10.1162/NECO_a_00341
PubMed ID: 22845820
Appears in Collections:Journal Articles

Show full item record

Export to RefWorks

Export Format: 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.