Bridging single cell and population dynamics

Boston University, Boston MA

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Abstract

DESCRIPTION (provided by applicant): The goals of this study are to measure how the dynamics of individual neurons and synapses contribute to synchronous oscillatory activity in populations of neurons, and to understand how the neuromodulator acetylcholine changes intracellular and network properties. Experiments will measure the dynamics of neurons in brain slices of the entorhinal cortex (EC) using whole-cell patch-clamp techniques, along with advanced real-time experimental control. The experiments will be supplemented by computational work. This study has four aims: (1) There are at least three distinct neuronal populations in the EC that have distinct electrophysiological properties. It will be tested if these neurons have different mechanisms of synchronization (e.g., excitation- or inhibition-based synchronization) using 'spike time response' (STR) methods from applied mathematics. In STR techniques, neurons are characterized in terms of how spikes in periodically firing neurons are advanced or delayed by artificial synaptic inputs. (2) The neuromodulator acetylcholine (ACh) is known to alter firing properties of neurons in the EC, and change population rhythms in brain slices. Therefore, it is hypothesized that ACh changes cellular intrinsic properties in a manner that supports enhanced synchronization. The effect of ACh agonists on neurons will be studied using STR measurements. (3) STR methods can be used to predict how small neuronal networks will synchronize. This hypothesis will be directly measured by using real-time control system to construct "hybrid" networks of coupled biological neurons and computer-modeled counterparts. (4) Network activity becomes complicated with increasing number and types of neurons and types of neurons. Models from STR based model networks can predict the behavior of large networks. Modeling can be used to understand networks with multiple cellular components and more complex patterns of synaptic coupling.