Simplification of the data-driven hippocampal CA1 microcircuit

Source Title
Print ISSN
Electronic ISSN
Bilkent University
Journal Title
Journal ISSN
Volume Title

Biophysically realistic neuron models provide emergent neural and circuit be-haviors due to their data-driven approach and high sensitivity in representing the ionic currents. These models comprise multiple compartments for representing dendrites, which allows getting high accuracy during dendrosomatic and somato-dendritic signals. An average pyramidal cell model having around 170 compart-ments and 1000 synapses makes up a significant cost in computational time and memory load. When a high number of cells are considered, an ordinary com-puter becomes unable to simulate such circuits. With the recent developments in neuron simplification algorithms, the voltage deflection timings at the soma can be captured by preserving the transfer impedance of excitatory & inhibitory synapses to soma. It has been shown that the simplified cortical neuron models can get a high spike-synchrony with up to 250x faster simulation time. Despite its performance at single cell level, the application of neuron reduce algorithm to circuit level analysis has not been shown yet. This work extends the use cases of the algorithm by applying it to various hippocampal morpholo-gies and shows its performance in both single cell and network level simulations. The validations include total neuron activation rates by morphology, inter-spike intervals of the circuits, raster plots and average firing rates throughout the cir-cuit in experiments including somatic current injection, miniature post synaptic potentials, external innervation from Schaffer Collaterals and LFP simulations. We show the successfully replicated emergent behaviors and the limitations of the simplification algorithm on neurons firing in broad frequency regimes and further present optimization techniques for those that perform sub-optimally with the simplification alone.

Other identifiers
Book Title
Hippocampus, CA1, Simplification, In-silico neuroscience, Simulation
Published Version (Please cite this version)