Mathematical Biology Seminar Steve Prescott University of Pittsburgh, School of Medicine 3:05PM, Wednesday, April 13, 2011 LCB 219 Nonlinear dynamics of single neuron coding properties Abstract: Neurons transmit information using action potential, or spikes. When given the same input, different neurons (or even the same neuron under different conditions) can produce very different output spike trains. This has important implications for neural information processing. Based on simulations in reduced conductance-based models and on experimental data, I will demonstrate that neurons have a limited repertoire of basic nonlinear dynamical mechanisms by which they can generate spikes. These nonlinear mechanisms reflect time- and voltage-dependent competition between membrane currents. Although different types of neurons may express very different membrane currents, even subtle shifts in the balance between membrane currents, such as might occur during normal modulation or under pathological conditions, can lead to qualitative changes in threshold mechanism. From an encoding perspective, the threshold mechanism bears directly on what sort of information a neuron’s output spike train can convey about the neuron’s input. I will argue that neurons can preferentially encode the integral or derivative of their input depending on the magnitude and direction of the neuron’s subthreshold current. These encoding properties in turn affect what sort of coding schemes (e.g. rate coding vs. temporal coding) can be used by single neurons and by larger neuronal ensembles.