# Predicting Performance using Approximate State Space Model for Liquid State Machines

## TL;DR

This paper introduces a linear state space approximation for Liquid State Machines, enabling efficient performance prediction and parameter tuning by correlating a new memory metric with accuracy, surpassing traditional non-linear measures.

## Contribution

The paper proposes a linear approximation of LSM dynamics that provides a computationally efficient memory metric, improving performance prediction and parameter exploration over existing non-linear measures.

## Key findings

- tau_M correlates strongly with LSM performance
- tau_M is 1800x faster to compute than LSM simulations
- tau_M outperforms Lyapunov exponent in high-performance regimes

## Abstract

Liquid State Machine (LSM) is a brain-inspired architecture used for solving problems like speech recognition and time series prediction. LSM comprises of a randomly connected recurrent network of spiking neurons. This network propagates the non-linear neuronal and synaptic dynamics. Maass et al. have argued that the non-linear dynamics of LSMs is essential for its performance as a universal computer. Lyapunov exponent (mu), used to characterize the "non-linearity" of the network, correlates well with LSM performance. We propose a complementary approach of approximating the LSM dynamics with a linear state space representation. The spike rates from this model are well correlated to the spike rates from LSM. Such equivalence allows the extraction of a "memory" metric (tau_M) from the state transition matrix. tau_M displays high correlation with performance. Further, high tau_M system require lesser epochs to achieve a given accuracy. Being computationally cheap (1800x time efficient compared to LSM), the tau_M metric enables exploration of the vast parameter design space. We observe that the performance correlation of the tau_M surpasses the Lyapunov exponent (mu), (2-4x improvement) in the high-performance regime over multiple datasets. In fact, while mu increases monotonically with network activity, the performance reaches a maxima at a specific activity described in literature as the "edge of chaos". On the other hand, tau_M remains correlated with LSM performance even as mu increases monotonically. Hence, tau_M captures the useful memory of network activity that enables LSM performance. It also enables rapid design space exploration and fine-tuning of LSM parameters for high performance.