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Biologically plausible spatiotemporal adjustment helps train deep spiking neural networks

The ahead and backward means of spiking neural networks. The dotted traces of various colours point out the influence on the community at completely different time steps. The sooner spiking node can have extra affect on the parameter replace. Credit: Patterns (2022). DOI: 10.1016/j.patter.2022.100522

Spiking neural networks (SNNs) seize an important points of mind data processing. They’re thought-about a promising strategy for next-generation synthetic intelligence. Nevertheless, the most important drawback limiting the event of SNNs is the coaching algorithm.

To unravel this drawback, a analysis staff led by Prof. Zeng Yi from the Institute of Automation of the Chinese language Academy of Sciences has proposed backpropagation (BP) with biologically believable spatiotemporal adjustment for coaching deep spiking neural networks.

The related examine was revealed in Patterns on June 2.

Backpropagation-based coaching has prolonged SNNs to extra advanced community buildings and duties. Nevertheless, the normal design of BP ignores the dynamic traits of SNNs and isn’t biologically believable.

Impressed by neural mechanisms within the mind, the researchers proposed a biologically believable spatiotemporal adjustment to exchange the normal synthetic design.

“After rethinking the relationship between membrane potential and spikes, we proposed the biologically plausible spatial adjustment of gradients to different time steps. It precisely controls the backpropagation of the error along the spatial dimension,” mentioned Prof. Zeng Yi, corresponding creator of the examine.

To beat the issue of temporal dependency of conventional spiking neurons inside a single spike interval, the researchers proposed a biologically believable temporal adjustment to make the error propagate throughout the spikes within the temporal dimension, in line with Shen Guobin, first creator of the examine.

The adjustment improves the efficiency of the SNNs and reduces energy consumption and latency. In contrast with different surrogate gradient algorithms, the algorithm proposed on this examine achieves 4.34% and 6.36% enchancment in accuracy with solely about half the power consumption on DVS-Gesture and DVS-CIFAR10, typical datasets for temporal-sequential data processing.

“In theory, compared with artificial neural networks of the same structure, the proposed algorithm uses only about 3% of the energy to achieve competitive classification accuracy,” mentioned Assistant Professor Zhao Dongcheng.

This examine is a part of the Mind-inspired Cognitive Intelligence Engine (BrainCog) challenge initiated by Prof. Zeng Yi’s staff, an on-going scientific exploration of the infrastructure of brain-inspired synthetic intelligence.

Mesoscale neural plasticity helps in AI learning

Extra data:
Yi Zenga, Backpropagation with Biologically Believable Spatio-Temporal Adjustment For Coaching Deep Spiking Neural Networks, Patterns (2022). DOI: 10.1016/j.patter.2022.100522. … 2666-3899(22)00119-2

Biologically believable spatiotemporal adjustment helps practice deep spiking neural networks (2022, June 2)
retrieved 2 June 2022

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