Self-organized optimization of synaptic heterogeneity in recurrent neuronal networks

• Applicant: Dr. Michael Fauth
• Subject Area: Experimental and Theoretical Network Neuroscience
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 521492574

Signals transmitted by biological synapses are influenced by their transmission characteristics, such as the adaptation transmission efficacy over time, which ultimately results in synapses performing temporal filtering and computation. These synaptic transmission characteristics are moreover heterogeneous - that is, they are different for each neuron and synapse. This fact, however, is often disregarded and it is largely unknown, whether synaptic heterogeneity plays a functional role in computation of neuronal networks and, if so, whether networks can shape synaptic heterogeneity in a self-organized manner. On the other hand, recent experiments show that biological networks are continuously rewired by creating and removing synapses. Also the functional relevance of this process, which is referred to as structural plasticity, is largely unknown. The main hypothesis of this project is that structural plasticity provides a self-organization mechanism that optimizes the heterogeneous synaptic properties to improve temporal computation in neuronal networks. As the brain is capable of rapid switching between tasks relying on the same (sensory) inputs, we further hypothesize that this optimization is (at least partly) task-independent, but rather adapts the network to provide a broadest basis for computations on the inputs it receives. Along this line, each of the heterogeneous characteristics considered in this project - short-term plasticity characteristics and synaptic plasticity characteristics - acts on a different timescale and, thus, can adapt to a different regime of timescales at which inputs convey information. Consequently, we assume that structural plasticity optimizing multiple heterogeneous properties acting on different timescales can adapt the network to multi-timescale inputs which occur in many biologically relevant perception tasks, such as language or music processing or understanding observed action sequences. We will address this hypothesis using mathematical models and a top-down-approach. To this end, we will derive such a structural plasticity mechanism and demonstrate in simulations that it allows neuronal networks to solve complex perception tasks that require integration of information over multiple timescales. The derived algorithms and resulting networks will then be compared to experimental observations and used to make predictions to experimentally test our hypothesis.

• DFG Programme: Research Grants