Implementing a foveal-pit inspired filter in a Spiking Convolutional
Neural Network: a preliminary study
- URL: http://arxiv.org/abs/2105.14326v1
- Date: Sat, 29 May 2021 15:28:30 GMT
- Title: Implementing a foveal-pit inspired filter in a Spiking Convolutional
Neural Network: a preliminary study
- Authors: Shriya T.P. Gupta, Basabdatta Sen Bhattacharya
- Abstract summary: We have presented a Spiking Convolutional Neural Network (SCNN) that incorporates retinal foveal-pit inspired Difference of Gaussian filters and rank-order encoding.
The model is trained using a variant of the backpropagation algorithm adapted to work with spiking neurons, as implemented in the Nengo library.
The network has achieved up to 90% accuracy, where loss is calculated using the cross-entropy function.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We have presented a Spiking Convolutional Neural Network (SCNN) that
incorporates retinal foveal-pit inspired Difference of Gaussian filters and
rank-order encoding. The model is trained using a variant of the
backpropagation algorithm adapted to work with spiking neurons, as implemented
in the Nengo library. We have evaluated the performance of our model on two
publicly available datasets - one for digit recognition task, and the other for
vehicle recognition task. The network has achieved up to 90% accuracy, where
loss is calculated using the cross-entropy function. This is an improvement
over around 57% accuracy obtained with the alternate approach of performing the
classification without any kind of neural filtering. Overall, our
proof-of-concept study indicates that introducing biologically plausible
filtering in existing SCNN architecture will work well with noisy input images
such as those in our vehicle recognition task. Based on our results, we plan to
enhance our SCNN by integrating lateral inhibition-based redundancy reduction
prior to rank-ordering, which will further improve the classification accuracy
by the network.
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