Modeling of neuron-semiconductor interactions in neuronal networks interfaced with silicon chips

TL;DR

This paper proposes a modeling framework to simulate neuron-silicon chip interactions, aiming to predict computational capabilities of neuronal networks interfaced with semiconductors for scalable neurocomputing.

Contribution

It introduces a novel modeling approach to simulate neuron-semiconductor interactions, facilitating the design and analysis of scalable neurocomputers.

Findings

Model successfully replicates experimental neuron-silicon interactions

Predicts potential computational power of neuronal networks with silicon interfaces

Provides a tool for designing larger, more efficient neurocomputing devices

Abstract

Recent developments in the interfacing of neurons with silicon chips may pave the way for progress in constructing scalable neurocomputers. The assembly of synthetic neuronal networks with predefined synaptic connections and controlled geometric structure has been realized experimentally within the last decade. Furthermore, when such neuronal networks are interfaced with semiconductors, action potentials in neurons of the network can be elicited by capacitative stimulators, and voltage measurements can be made by transistors incorporated into the associated silicon chip. Despite the impressive progress, such preliminary devices have not yet demonstrated the performance of useful computations, and constructing larger devices can be both expensive and time-consuming. Accordingly, an appropriate modeling framework with the capability to simulate current experimental results in such devices may be used to make useful predictions regarding their potential computational power. A proposed modeling framework for functional neuronal networks interfaced with silicon chips is presented below.