# Mean Field Approach for Configuring Population Dynamics on a Biohybrid Neuromorphic System

## TL;DR

This paper introduces a large-scale neuromorphic system with a theory-guided configuration method that accurately replicates biological population dynamics, enabling advanced biohybrid neural interfaces.

## Contribution

It presents a novel mesoscopic, theory-based approach for configuring large neuromorphic networks to emulate biological bursting behaviors.

## Key findings

- Successfully replicated in-vitro bursting statistics
- Achieved high-fidelity biologically realistic behavior
- Enabled targeted exploration of behavioral space

## Abstract

Real-time coupling of cell cultures to neuromorphic circuits necessitates a neuromorphic network that replicates biological behaviour both on a per-neuron and on a population basis, with a network size comparable to the culture. We present a large neuromorphic system composed of 9 chips, with overall 2880 neurons and 144M conductance-based synapses. As they are realized in a robust switched-capacitor fashion, individual neurons and synapses can be configured to replicate with high fidelity a wide range of biologically realistic behaviour. In contrast to other exploration/heuristics-based approaches, we employ a theory-guided mesoscopic approach to configure the overall network to a range of bursting behaviours, thus replicating the statistics of our targeted in-vitro network. The mesoscopic approach has implications beyond our proposed biohybrid, as it allows a targeted exploration of the behavioural space, which is a non-trivial task especially in large, recurrent networks.