From neurons to networks via noise

Magnus Richardson, Warwick University

After covering some of the basic biology of neurons, I will review the theoretical frameworks developed to capture their electrical properties. These will include the Hodgkin-Huxley model, arguably the most successful mathematical model in neurobiology, and the integrate-and-fire neuron, which can be thought of as neuroscience’s Ising model. Neurons in active neocortical networks, those in which our high level thought processes take place, are subject to highly fluctuating synaptic activity. I will describe our recent work to extract tractable models from experiment by using noisy stimuli that mimic naturalistic drive. I will then go on to show how the stochastic nature of neural activity can be used to bridge the gap between the cellular and network levels, allowing emergent network states to be causally linked to the experimentally verified properties of their component neurons.