Irina Erchova Abstract

Reverse engineering neuronal signal transmissions

In the nervous system signals from the external world are converted into a spatially distributed series of electrical pulses emitted by individual neurons. Understanding the mechanisms of signal transmission, filtering and targeting are the keys to develop effective tools for diagnosing neuronal pathologies and enabling the development of brain – computer interfaces (BCIs).

My research focuses on signal coding and the mechanisms implicated in the propagation and direction of information flow in complex networks. The term ‘reverse engineering’ implies a systematic methodology by which quantitative mathematical models of brain function can be developed, based on measured data and validation of models in prediction-driven experiments.

I am going to show how the intrinsic properties of cells and their local connectivities might predict optimal signal coding in the system. I will discuss the origin of the time scales for neural information representation and relate them to underlying biophysical parameters. In addition, I will show how response to a stimulus depends on ongoing network activity. This activity reflects, for example, previous experience, the current environment, or specific attention. Network modulation gates information flows and is thought to promote experience dependent changes in neuronal activity. I will illustrate a role of a local network modulation on plasticity in context-dependent stimuli integration in sensory cortex and discuss a possible role of global dynamic network modulation (brain rhythms) in relation to signal propagation and formation of memories.