Manipulation of Neuronal Network Firing Patterns using Temporal Deep Unfolding-based MPC

TL;DR

This paper introduces a novel control method using temporal deep unfolding-based model predictive control to manipulate firing patterns in neuronal networks, addressing nonlinear dynamics and zero-gradient challenges.

Contribution

It extends TDU-MPC to nonlinear neuronal systems with reset dynamics, enabling control of firing patterns without direct input application.

Findings

Successfully controlled firing frequencies in 15 and 30 neuron networks.

Demonstrated manipulation of module firing patterns without direct control inputs.

Extended TDU-MPC methodology to complex neuronal dynamics.

Abstract

Because neuronal networks are intricate systems composed of interconnected neurons, their control poses challenges owing to their nonlinearity and complexity. In this paper, we propose a method to design control input to a neuronal network to manipulate the firing patterns of modules within the network. We propose a methodology for designing a control input based on temporal deep unfolding-based model predictive control (TDU-MPC), a control methodology based on the deep unfolding technique actively investigated in the context of wireless signal processing. During the method development, we address the unique characteristics of neuron dynamics, such as zero gradients in firing times, by approximating input currents using a sigmoid function. The effectiveness of the proposed method is confirmed via numerical simulations. In networks with 15 and 30 neurons, the control was achieved to switch the firing frequencies of two modules without directly applying control inputs. This study includes a tailored methodology for networked neurons, the extension of TDU-MPC to nonlinear systems with reset dynamics, and the achievement of desired firing patterns in neuronal networks.