# A neuronal dynamics study on a neuromorphic chip

**Authors:** Wenyuan Li, Igor V. Ovchinnikov, Honglin Chen, Zhe Wang, Albert Lee,, Hochul Lee, Carlos Cepeda, Robert N. Schwartz, Karlheinz Meier, Kang L., Wang

arXiv: 1703.03560 · 2017-03-13

## TL;DR

This study investigates neuronal firing dynamics on a neuromorphic chip, revealing different collective activities and phase behaviors that could inform neurological disease diagnostics and inspire new brain-inspired computing paradigms.

## Contribution

It demonstrates the existence of distinct neuronal firing phases on a neuromorphic chip and links these to brain health and computational efficiency, offering new insights into neural dynamics and applications.

## Key findings

- Identified three types of neuronal firing activities matching clinical data.
- Constructed a brain phase diagram showing operation in an N-phase.
- Normal brain states in the N-phase are optimal for computation.

## Abstract

Neuronal firing activities have attracted a lot of attention since a large population of spatiotemporal patterns in the brain is the basis for adaptive behavior and can also reveal the signs for various neurological disorders including Alzheimer's, schizophrenia, epilepsy and others. Here, we study the dynamics of a simple neuronal network using different sets of settings on a neuromorphic chip. We observed three different types of collective neuronal firing activities, which agree with the clinical data taken from the brain. We constructed a brain phase diagram and showed that within the weak noise region, the brain is operating in an expected noise-induced phase (N-phase) rather than at a so-called self-organized critical boundary. The significance of this study is twofold: first, the deviation of neuronal activities from the normal brain could be symptomatic of diseases of the central nervous system, thus paving the way for new diagnostics and treatments; second, the normal brain states in the N-phase are optimal for computation and information processing. The latter may provide a way to establish powerful new computing paradigm using collective behavior of networks of spiking neurons.

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Source: https://tomesphere.com/paper/1703.03560