Optimization of noise-induced resonance mechanisms on co-evolutionary neurobiological networks

• Applicant: Dr. Marius Yamakou
• Subject Area: Statistical Physics, Nonlinear Dynamics, Complex Systems, Soft and Fluid Matter, Biological Physics
• Term: from 2021 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 456989199

The functional role of noise is a long-standing research question in neurobiology. While noise is generally undesirable, its resonance effect is well known and is generally accepted to be crucial for the proper functioning of neurons in terms of their information coding capabilities. The phenomenon of resonance has been observed both in individual neurons and as well as in networks of neurons. It is also known that different types of noise-induced resonance mechanisms occur under different conditions. These consist of different combinations of neuron parameters, synaptic connections between neurons, network topology, and noise sources. All previous research has been focused on understanding the optimization of each of these noise-induced resonance mechanisms: (a) in non-adaptive neural networks, and (b) independently of one another. A comprehensive understanding of optimization of information processing via the optimization of these noise-induced resonance mechanisms in (a) co-evolutionary (adaptive) neural networks, and (b) by using the interplay between two or more noise-induced mechanisms, is still completely lacking. The main objective of this project is to design and classify in terms of efficiency, a plethora of optimization schemes for three different types of noise-induced resonance mechanisms, in co-evolutionary biological neural networks. The main focus is on the noise-induced resonance mechanisms of coherence resonance (CR), self-induced stochastic resonance (SISR), and recurrence resonance (RR) in co-evolutionary network motifs, scale-free networks, small-world networks, random networks, and their multilayer networks. These neural networks will consist of the biophysical Hodgin-Huxley (HH) neuron model and evolve according to the spiking time-dependent plasticity learning rule or a temporal activity-dependent structural plasticity learning rule. Before considering the HH neurons/networks, the necessary conditions for the occurrence of CR and SISR in an isolated HH neuron will be determined using geometric singular perturbation theory and stochastic multi-dimensional reaction rate theory.Depending on the topology, the synaptic properties, and the learning rule of a network, different optimization schemes for CR, SISR, and RR will design and classified with respect to their efficiency. This is a timely and unique proposal that promises to push our understanding of optimal neural coding and information processing to new frontiers.

• DFG Programme: Research Grants