Classifying neuromorphic data using a deep learning framework for image classification

by   Roshan Gopalakrishnan, et al.

In the field of artificial intelligence, neuromorphic computing has been around for several decades. Deep learning has however made much recent progress such that it consistently outperforms neuromorphic learning algorithms in classification tasks in terms of accuracy. Specifically in the field of image classification, neuromorphic computing has been traditionally using either the temporal or rate code for encoding static images in datasets into spike trains. It is only till recently, that neuromorphic vision sensors are widely used by the neuromorphic research community, and provides an alternative to such encoding methods. Since then, several neuromorphic datasets as obtained by applying such sensors on image datasets (e.g. the neuromorphic CALTECH 101) have been introduced. These data are encoded in spike trains and hence seem ideal for benchmarking of neuromorphic learning algorithms. Specifically, we train a deep learning framework used for image classification on the CALTECH 101 and a collapsed version of the neuromorphic CALTECH 101 datasets. We obtained an accuracy of 91.66 CALTECH 101 datasets respectively. For CALTECH 101, our accuracy is close to the best reported accuracy, while for neuromorphic CALTECH 101, it outperforms the last best reported accuracy by over 10 suitability of such datasets as benchmarks for neuromorphic learning algorithms.


page 3

page 4


Design and Mathematical Modelling of Inter Spike Interval of Temporal Neuromorphic Encoder for Image Recognition

Neuromorphic computing systems emulate the electrophysiological behavior...

Deep Versus Wide Convolutional Neural Networks for Object Recognition on Neuromorphic System

In the last decade, special purpose computing systems, such as Neuromorp...

NengoDL: Combining deep learning and neuromorphic modelling methods

NengoDL is a software framework designed to combine the strengths of neu...

Limitations in odour recognition and generalisation in a neuromorphic olfactory circuit

Neuromorphic computing is one of the few current approaches that have th...

SPAIC: A Spike-based Artificial Intelligence Computing Framework

Neuromorphic computing is an emerging research field that aims to develo...

You Only Spike Once: Improving Energy-Efficient Neuromorphic Inference to ANN-Level Accuracy

In the past decade, advances in Artificial Neural Networks (ANNs) have a...

Neko: a Library for Exploring Neuromorphic Learning Rules

The field of neuromorphic computing is in a period of active exploration...

Please sign up or login with your details

Forgot password? Click here to reset