Mechanism-Based and Input-Output Modeling of the Key Neuronal Connections and Signal Transformations in the CA3-CA1 Regions of the Hippocampus

Autor:	Dong Song, Dae C. Shin, Kunling Geng, Vasilis Z. Marmarelis, Robert E. Hampson, Sam A. Deadwyler, Theodore W. Berger
Rok vydání:	2018
Předmět:	0301 basic medicine Computer science Cognitive Neuroscience Models Neurological Action Potentials Machine learning computer.software_genre Receptors N-Methyl-D-Aspartate Synthetic data 03 medical and health sciences 0302 clinical medicine Receptors GABA Arts and Humanities (miscellaneous) Animals Humans CA1 Region Hippocampal Parametric statistics Neurons Input/output business.industry SIGNAL (programming language) Nonparametric statistics Experimental data Neural Inhibition CA3 Region Hippocampal 030104 developmental biology Nonlinear Dynamics Synapses Parametric model Data analysis Neural Networks Computer Artificial intelligence Biological system business computer 030217 neurology & neurosurgery
Zdroj:	Neural Computation. 30:149-183
ISSN:	1530-888X 0899-7667
DOI:	10.1162/neco_a_01031
Popis:	This letter examines the results of input-output (nonparametric) modeling based on the analysis of data generated by a mechanism-based (parametric) model of CA3-CA1 neuronal connections in the hippocampus. The motivation is to obtain biological insight into the interpretation of such input-output (Volterra-equivalent) models estimated from synthetic data. The insights obtained may be subsequently used to interpretat input-output models extracted from actual experimental data. Specifically, we found that a simplified parametric model may serve as a useful tool to study the signal transformations in the hippocampal CA3-CA1 regions. Input-output modeling of model-based synthetic data show that GABAergic interneurons are responsible for regulating neuronal excitation, controlling the precision of spike timing, and maintaining network oscillations, in a manner consistent with previous studies. The input-output model obtained from real data exhibits intriguing similarities with its synthetic-data counterpart, demonstrating the importance of a dynamic resonance in the system/model response around 2 Hz to 3 Hz. Using the input-output model from real data as a guide, we may be able to amend the parametric model by incorporating more mechanisms in order to yield better-matching input-output model. The approach we present can also be applied to the study of other neural systems and pathways.
Databáze:	OpenAIRE
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