Mapping and Validating a Point Neuron Model on Intel's Neuromorphic Hardware Loihi

TL;DR

This paper demonstrates that Intel's Loihi neuromorphic chip can efficiently simulate Leaky Integrate and Fire neuron models based on mouse visual cortex data, offering scalable and energy-efficient performance compared to classical hardware.

Contribution

It validates the use of Loihi for brain-inspired neural simulations, showing its efficiency and scalability in emulating biologically realistic neuron models.

Findings

Loihi replicates classical simulations accurately.

The neuromorphic hardware scales well with network size.

Loihi offers significant improvements in time and energy efficiency.

Abstract

Neuromorphic hardware is based on emulating the natural biological structure of the brain. Since its computational model is similar to standard neural models, it could serve as a computational acceleration for research projects in the field of neuroscience and artificial intelligence, including biomedical applications. However, in order to exploit this new generation of computer chips, rigorous simulation and consequent validation of brain-based experimental data is imperative. In this work, we investigate the potential of Intel's fifth generation neuromorphic chip - `Loihi', which is based on the novel idea of Spiking Neural Networks (SNNs) emulating the neurons in the brain. The work is implemented in context of simulating the Leaky Integrate and Fire (LIF) models based on the mouse primary visual cortex matched to a rich data set of anatomical, physiological and behavioral constraints. Simulations on the classical hardware serve as the validation platform for the neuromorphic implementation. We find that Loihi replicates classical simulations very efficiently and scales notably well in terms of both time and energy performance as the networks get larger.