Transformation of inputs in a model of the rat hippocampal CA1 network

TitleTransformation of inputs in a model of the rat hippocampal CA1 network
Publication TypeConference Paper
Year of Publication2010
AuthorsOlypher, A. V., Lytton WW., & Prinz A. A.
Conference NameSociety for Neuroscience 2010 (SFN '10)
KeywordsSFN, Society for Neuroscience

Information processing in the hippocampus involves synchronized spiking of subsets of neurons. The functional properties of synchronous spiking are unclear. According to one hypothesis, synchronously spiking neurons form relatively stable assemblies. We used computer simulations and theoretical analyses to study whether such assemblies could result from intrinsic neuronal properties and connectivity patterns alone without synaptic weight tuning. More generally, we assessed conditions under which CA1 principal cells that receive synchronous inputs could generalize similar input patterns and discriminate distinct patterns. Network effects were simulated by partially overlapped inputs to 23,500 copies of a CA1 principal cell model corresponding to the approximate number of cells in one square millimeter in the CA1 rat hippocampus. Overlap of inputs was based on hippocampal neuroanatomy. Interactions between the CA1 principal cells were not considered. We assumed that all the cells received their excitatory and inhibitory inputs synchronously, as occurs due to strong modulation by ongoing theta and gamma rhythm. Each cell was modeled by biophysically realistic multicompartment models of reconstructed CA1 principal cells using NEURON. We characterized the network performance by its response to synchronous inputs within 40 milliseconds. If a modeled CA1 cell spiked within this interval, it contributed ``1'' to the output of the network, otherwise the cell contributed ``0''. In our analyses, we compared the distances between pairs of input patterns and the distances between the corresponding pairs of CA1 activity (output). We performed this analysis for CA3, entorhinal, and mixed input patterns. These data suggests the importance of the input overlap to different CA1 cells on the variability of the CA1 network responses. Our results provide a benchmark for comparing the CA1 network performance under normal and psychotic conditions, such as in schizophrenia. Revealed changes in the network ability to process similar and distinct inputs in pathological states could underlie cognitive deficits.