Material-based Non-neural Analogues of Lateral Inhibition: A Multi-agent Approach

TL;DR

This paper demonstrates that simple, non-neural organisms like slime mould can exhibit lateral inhibition-like contrast enhancement through bulk transport effects in a multi-agent model, offering insights into biological computation and distributed systems.

Contribution

It introduces a multi-agent model showing how lateral inhibition can emerge without neural wiring, using only transport effects in slime mould-like systems.

Findings

LI can be approximated without neural tissue

Contrast amplification observed in the model

Potential applications in distributed computing and robotics

Abstract

Lateral Inhibition (LI) phenomena occur in a wide range of sensory modalities and are most famously described in the human visual system. In LI the activity of a stimulated neuron is itself excited and suppresses the activity of its local neighbours via inhibitory connections, increasing the contrast between spatial environmental stimuli. Simple or- ganisms, such as the single-celled slime mould Physarum polycephalum possess no neural tissue yet, despite this, are known to exhibit complex computational behaviour. Could simple organisms such as slime mould approximate LI without recourse to neural tissue? We describe a model whereby LI can emerge without explicit inhibitory wiring, using only bulk transport effects. We use a multi-agent virtual material model of slime mould to reproduce the characteristic contrast amplification response of LI using excitation via attractant stimuli. Restoration of baseline activ- ity occurs when the stimuli are removed. We also explore an opposite counterpart behaviour, Lateral Activation (LA), using repellent stimuli. These preliminary results suggest that simple organisms without neural tissue may approximate sensory contrast enhancement using alternative analogues of LI and suggests novel approaches towards generating collec- tive contrast enhancement in distributed computing and robotic devices.