Volume 12 Supplement 1
A convolutional neural network model of the neural responses of inferotemporal cortex to complex visual objects
© Rohit and Chakravarthy; licensee BioMed Central Ltd. 2011
Published: 18 July 2011
We present a neural network model that replicates the response properties of the neurons in monkey inferior temporal cortex described in the studies of Tanaka and colleagues [1, 2]. A convolutional neural network (CNN) known for its visual pattern recognition capabilities is used for this purpose. The present work consists of two studies.
In the first study, we simulate the “image reduction method” of  in order to study the responses of tuned neurons to complex visual patterns. The CNN used in this study consists of 4 hidden layers, 12 output neurons, and accepts a input image of size 50 X 50. The first hidden layer has 5 sub layers, each of size 46 X 46, and the third hidden layer has 12 sub layers, each of size 20 X 20. The network is trained on 12 images selected from the original study . Neurons of the penultimate layer that exhibit a distinct response to one image, as opposed to all other images, are selected as tuned neurons. When reduced version of an image is presented, the corresponding tuned neurons preferentially show a drastic reduction in response; no such change is seen in the responses of a non-tuned neuron (fig. 1).
- Tanaka K: Mechanisms of visual object recognition studied in monkeys. Spatial Vision. 2000, 13: 147-163. 10.1163/156856800741171.View ArticlePubMedGoogle Scholar
- Kiani R, Esteky H, Mirpour K, Tanaka K: Object Category Structure in Response Patterns of Neuronal Population in Monkey Inferior Temporal Cortex. Journal of Neurophysiology. 2007, 97: 4296-4309. 10.1152/jn.00024.2007.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.