# An Efficient Method for online Detection of Polychronous Patterns in Spiking Neural Network

## TL;DR

This paper introduces a novel online detection method for polychronous patterns in spiking neural networks, significantly improving efficiency and enabling real-time recognition of spike-timing sequences.

## Contribution

The work presents a new model that captures polychronous patterns directly during neural simulation using randomized codes and hash tables, enabling online detection.

## Key findings

- Method outperforms existing detection techniques in computational efficiency.
- Successfully recognizes spike-timing patterns in a visual task.
- Enables real-time pattern detection in neural simulations.

## Abstract

Polychronous neural groups are effective structures for the recognition of precise spike-timing patterns but the detection method is an inefficient multi-stage brute force process that works off-line on pre-recorded simulation data. This work presents a new model of polychronous patterns that can capture precise sequences of spikes directly in the neural simulation. In this scheme, each neuron is assigned a randomized code that is used to tag the post-synaptic neurons whenever a spike is transmitted. This creates a polychronous code that preserves the order of pre-synaptic activity and can be registered in a hash table when the post-synaptic neuron spikes. A polychronous code is a sub-component of a polychronous group that will occur, along with others, when the group is active. We demonstrate the representational and pattern recognition ability of polychronous codes on a direction selective visual task involving moving bars that is typical of a computation performed by simple cells in the cortex. The computational efficiency of the proposed algorithm far exceeds existing polychronous group detection methods and is well suited for online detection.