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Sunday, July 19 • 9:00pm - 10:00pm
P176: Using Deep Convolutional Neural Networks to Visualise the Receptive Fields of High Level Visual Cortical Neurons

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Brett Schmerl
, Declan Rowley, Elizabeth Zavitz, Hsin-Hao Yu, Nicholas Price, Marcello Rosa

Understanding the image features that are encoded by neurons throughout the hierarchy of visual cortical areas, particularly in areas higher in the hierarchy that have more complex response properties than in V1, is a challenging yet fundamental goal in visual neuroscience that is often achieved by visualising their pattern of responses [1]. Visualising image features responsible for driving activity of individual units in a hierarchical system used for visual processing for the purposes of understanding the system’s functioning and information representation is also encountered in the study of deep convolutional neural networks.

In this study we train deep convolutional neural networks on spiking data recorded from individual neurons in a mid-tier visual area (the dorsomedial area, DM) of the anaesthetised marmoset monkey whilst the animal is presented with changing patterns of spatiotemporally white noise [2]. We show that convolutional neural networks are capable of learning statistically significant input-output relationships of these neurons and are thus able to perform classification of the spiking behaviour of the neuron given the stimuli. Furthermore, we applied deconvolutional techniques [3] used to visualise image features encoded by the convolutional model, thus allowing visualisation of input image features that are significant to determining spiking behaviour, by proxy, of the neuron. A comparison between the features recovered using this technique and those recovered by traditional methods of analysis is presented.

1. Jones, J. P., & Palmer, L. A. (1987). The two-dimensional spatial structure of simple receptive fields in cat striate cortex. Journal of neurophysiology, 58(6), 1187-1211.

2. Lui, L. L., Bourne, J. A., & Rosa, M. G. (2005). Functional response properties of neurons in the dorsomedial visual area of New World monkeys (Callithrix jacchus). Cerebral Cortex, 16(2), 162-177.

3. Zeiler, M. D. & Fergus R. Visualizing and understanding convolutional networks. In European conference on computer vision, 818–833. Springer, 2014.


Brett Schmerl

School of Information Technology and Mathematical Sciences, University of South Australia

Sunday July 19, 2020 9:00pm - 10:00pm CEST
Slot 18