Cortical Feedback Inhibition

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DESCRIPTION (provided by applicant): Synaptic inhibition exerts a crucial function in shaping the activity of neuronal populations in space and time and in preventing excitation to spread unrestrained through networks of cortical neurons. Feedback inhibitory circuits are a major source of synaptic inhibition and thus, play a very important role in the control of hyperexcitability. Feedback inhibition occurs when inhibitory neurons project to the population of neurons from whom they receive excitation. Such circuits are stereotypical in cortical areas and are regarded as a general principle of cortical organization. In contrast to the wealth of knowledge on the anatomical properties of feedback inhibition, the specific means by which this circuit controls the activity of networks of neurons is poorly understood. This proposal addresses the mechanism by which feedback inhibitory circuits control the excitability of pyramidal neurons in the hippocampus, a structure where a slight imbalance between excitation and inhibition can lead to epileptiform activity. Our preliminary data suggest that at least two independent feedback pathways inhibit hippocampal pyramidal cells. One pathway is preferentially activated by low spiking frequencies (< 10 Hz) while the other is activated at higher spiking frequencies (>10 Hz) of pyramidal cells. Furthermore, one pathway inhibits the soma while the other inhibits the dendrites of pyramidal cells, suggesting that they may affect different sets of excitatory inputs. This study will reveal the mechanism by which a simple but powerful and ubiquitous neuronal circuit controls excitation in the hippocampus and may thus contribute to the development of therapies aimed a preventing hyperexcitability and epileptogenesis in cortical areas. Furthermore, elucidating the functional properties of elementary circuits, like feedback inhibition, will allow, in the long term, to understand the mechanisms that determine the spatial and temporal activity patterns of larger networks of neurons.