# Transient sequences in a hypernetwork generated by an adaptive network of spiking neurons

## TL;DR

This paper presents a model of an adaptive spiking neuron network that generates transient sequences and hypernetwork dynamics, demonstrating stimulus-specific reproducible patterns and FPGA implementation.

## Contribution

It introduces a novel adaptive spiking neuron network model that produces hypernetwork-based transient sequences with FPGA realization.

## Key findings

- Network exhibits transient clustering and hypernetwork traffic as a random walk.
- Input stimuli induce reproducible, stimulus-specific sequences.
- Model is validated through FPGA implementation.

## Abstract

We propose a model of an adaptive network of spiking neurons that gives rise to a hypernetwork of its dynamic states at the upper level of description. Left to itself, the network exhibits a sequence of transient clustering which relates to a traffic in the hypernetwork in the form of a random walk. Receiving inputs the system is able to generate reproducible sequences corresponding to stimulus-specific paths in the hypernetwork. We illustrate these basic notions by a simple network of discrete-time spiking neurons together with its FPGA realization and analyze their properties.