Abstract
The hippocampus is a heavily studied brain structure due to its involvement in learning and memory. Detailed models of excitatory, pyramidal cells in hippocampus have been developed using a range of experimental data. These models have been used to help us understand, for example, the effects of synaptic integration and voltage gated channel densities and distributions on cellular responses. However, these cellular outputs need to be considered from the perspective of the networks in which they are embedded. Using modeling approaches, if cellular representations are too detailed, it quickly becomes computationally unwieldy to explore large network simulations. Thus, simple models are preferable, but at the same time they need to have a clear, experimental basis so as to allow physiologically based understandings to emerge. In this article, we describe the development of simple models of CA1 pyramidal cells, as derived in a well-defined experimental context of an intact, whole hippocampus preparation expressing population oscillations. These models are based on the intrinsic properties and frequency-current profiles of CA1 pyramidal cells, and can be used to build, fully examine, and analyze large networks.
Highlights
Networks of excitatory and inhibitory neurons are essential components constituting the functional structures of our brains
We present the result of CA1 pyramidal cell models developed in the same context of this in vitro whole hippocampus preparation
All animals were treated according to protocols and guidelines approved by McGill University and the Canadian Council of Animal Care (CCAC)
Summary
Networks of excitatory and inhibitory neurons are essential components constituting the functional structures of our brains. Mathematical models of neurons and networks are developed so that they can be used to determine the mechanisms underlying brain functions. It is well known that cellular models used in building network models affect and can dictate the network output[4,5] To address this recognized difficulty we are developing models that are based on well-defined experimental contexts in which both the cellular and the network aspects of the model can be considered simultaneously[6,7]. Using such models, we aim to help determine, predict and test biologically based mechanisms
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