Computational Effects of Free-Flowing Ion Concentrations in Spiking Neural Networks

TY - GEN

T1 - Computational Effects of Free-Flowing Ion Concentrations in Spiking Neural Networks

AU - Afonso Rodrigues, Rafael

AU - O'Keefe, Simon

PY - 2024/6/30

Y1 - 2024/6/30

N2 - Modern Spiking Neural Networks (SNNs) employ efficient neuron models to scale networks using Deep Learning techniques. However, as conventional artificial neural networks continue to outperform and outpace SNNs, research shifted towards efficiency, thereby overlooking the question of performance. This document explores the idea of finding a computational edge for SNNs in greater biophysical realism. The authors’ inspiration lies in the known dependency of biological spike activity on an interplay of continuous flows of ions. Current spiking neuron models adopt a simplified representation of this principle by either not considering ions (Integrate-and-Fire) or viewing their concentrations as static quantities (Hodgkin-Huxley). After presenting some of the complexity surrounding biological neurons, the authors investigate intricate molecular mechanisms as well as non-stationary flows, incorporated under the proposed Free-Flowing Ion Concentrations (FFIC) model framework. Their importance for neuronal function is studied from a computational lens. Supported by novel descriptive techniques tailored for spike activity, computational properties of FFIC models are examined first within individual neurons and then as part of cortical microcircuit-like SNNs. Results indicate that the combination of these unconventional ion dynamics consistently leads to affluent and diverse neuronal activity, able to express a range of rich and complex behaviours. When connected together, FFIC neurons with strongly coupled molecular mechanisms imbue SNNs with higher information processing capacity. The stationarity of concentrations is seen to influence their receptivity to different types of stimulation (current or spikes) and the expression of complex transfer functions.

AB - Modern Spiking Neural Networks (SNNs) employ efficient neuron models to scale networks using Deep Learning techniques. However, as conventional artificial neural networks continue to outperform and outpace SNNs, research shifted towards efficiency, thereby overlooking the question of performance. This document explores the idea of finding a computational edge for SNNs in greater biophysical realism. The authors’ inspiration lies in the known dependency of biological spike activity on an interplay of continuous flows of ions. Current spiking neuron models adopt a simplified representation of this principle by either not considering ions (Integrate-and-Fire) or viewing their concentrations as static quantities (Hodgkin-Huxley). After presenting some of the complexity surrounding biological neurons, the authors investigate intricate molecular mechanisms as well as non-stationary flows, incorporated under the proposed Free-Flowing Ion Concentrations (FFIC) model framework. Their importance for neuronal function is studied from a computational lens. Supported by novel descriptive techniques tailored for spike activity, computational properties of FFIC models are examined first within individual neurons and then as part of cortical microcircuit-like SNNs. Results indicate that the combination of these unconventional ion dynamics consistently leads to affluent and diverse neuronal activity, able to express a range of rich and complex behaviours. When connected together, FFIC neurons with strongly coupled molecular mechanisms imbue SNNs with higher information processing capacity. The stationarity of concentrations is seen to influence their receptivity to different types of stimulation (current or spikes) and the expression of complex transfer functions.

KW - Computational modeling;Biological system modeling;Neurons;Transfer functions;Spiking neural networks;Ions;Problem-solving;Spiking Neural Networks;Free-flowing Concentrations;Ion Dynamics;Computational Capacity;Spike Pattern Analysis

U2 - 10.1109/IJCNN60899.2024.10649998

DO - 10.1109/IJCNN60899.2024.10649998

M3 - Conference contribution

SN - 979-8-3503-5932-9

SP - 1

EP - 10

BT - 2024 International Joint Conference on Neural Networks (IJCNN)

PB - IEEE

ER -