Interaction of Excitatory and Inhibitory Synaptic Inputs in Neurons of the Thalamocortical Network During Normal and Pathological Brain Activity: Computational Modeling and Dynamic Clamp in Vitro.

The brain performs complex calculations using large parallel networks of neurons. Neurons are constantly receiving synaptic inputs, which they integrate and process with an end result of a spike output. Neurons do not simply perform these transformations as the linear sum of the synaptic inputs; the electrophysiological and geometric properties of the cells themselves also shape the complex behavior of the membrane voltage. The understanding of how action potentials are generated by neurons according to the interaction of synaptic input and intrinsic currents is paramount to understanding how the brain functions. The unifying theme of the work that comprises this thesis is the study of how excitatory and inhibitory synaptic inputs impinging on a neuron interact with each other and with that neuron's specific biophysical properties to determine the output behavior of the neuron. Focus is placed on two examples of neuronal populations receiving streams of excitatory and inhibitory input. First, neurons in the dorsal thalamic nuclei receive excitatory input from cortex and inhibitory inputs from the thalamic reticular nucleus. Using a biophysically detailed computational network model, I explore the dynamic interaction of those two inputs in the generation of physiological rhythms and their transformation into pathological seizures. Second, the neurons of the primary sensory cortex receive excitatory input from the neurons in the thalamus and from their excitatory neighbors, and feedforward inhibitory input from local cortical interneurons excited by the thalamic projection. Through dynamic clamp experiments performed in vitro, I explore how the dynamic interaction of excitation and inhibition determines the selectivity, or preference, of the neuronal response for certain parameters of sensory stimuli.