Solid State Neuroscience: Spiking Neural Networks as Time Matter

TL;DR

This paper introduces a novel analogy between neural spike events and matter, defining concepts like phase transitions and equations of state for neural systems, with potential applications in medical diagnostics.

Contribution

It establishes a new framework linking neuroscience and material science by modeling neural spiking activity as 'time-matter' with thermodynamic-like properties.

Findings

Neural spike events can be modeled with equations analogous to physical states.

The concept of neuro-compressibility predicts transitions in neural activity.

Potential for early diagnosis of neurological diseases using these analogies.

Abstract

We aim at building a bridge between to {\it a priori} disconnected fields: Neuroscience and Material Science. We construct an analogy based on identifying spikes events in time with the positions of particles of matter. We show that one may think of the dynamical states of spiking neurons and spiking neural networks as {\it time-matter}. Namely, a structure of spike-events in time having analogue properties to that of ordinary matter. We can define for neural systems notions equivalent to the equations of state, phase diagrams and their phase transitions. For instance, the familiar Ideal Gas Law relation (P$v$ = constant) emerges as analogue of the Ideal Integrate and Fire neuron model relation ($I_{in}$ISI = constant). We define the neural analogue of the spatial structure correlation function, that can characterize spiking states with temporal long-range order, such as regular tonic spiking. We also define the ``neuro-compressibility'' response function in analogy to the lattice compressibility. We show that similarly to the case of ordinary matter, the anomalous behavior of the neuro-compressibility is a precursor effect that signals the onset of changes in spiking states. We propose that the notion of neuro-compressibility may open the way to develop novel medical tools for the early diagnose of diseases. It may allow to predict impending anomalous neural states, such as Parkinson's tremors, epileptic seizures, electric cardiopathies, and perhaps may even serve as a predictor of the likelihood of regaining consciousness.