A Novel Biophysical Model for the Generation of Sharp Wave Ripples in CA1 Hippocampus.

Synchronous activities among neurons in the brain generate emergent network oscillations such as the hippocampal Sharp-wave ripples (SPWRs) that facilitate information processing during memory formation. However, how neurons and circuits are functionally organized to generate oscillations remains unresolved. Biophysical models of neuronal networks can shed light on how thousands of neurons interact in intricate circuits to generate such emergent SPWR network events. Here we developed a large-scale biophysically realistic neural network model of CA1 hippocampus with functionally organized circuit modules containing distinct types of neurons. Model simulations reproduced synaptic, cellular and network aspects of physiological SPWRs. The model provided insights into the role of neuronal types and their microcircuit motifs in generating SPWRs in the CA1 region. The model also suggests experimentally testable predictions including the role of specific neuron types in the genesis of hippocampal SPWRs.

Inferring Morphology of a Neuron from In Vivo LFP Data.

We propose a computational pipeline that uses biophysical modeling and sequential neural posterior estimation algorithm to infer the position and morphology of single neurons using multi-electrode in vivo extracellular voltage recordings. In this inverse modeling scheme, we designed a generic biophysical single neuron model with stylized morphology that had adjustable parameters for the dimensions of the soma, basal and apical dendrites, and their location and orientations relative to the multi-electrode probe. Preliminary results indicate that the proposed methodology can infer up to eight neuronal parameters well. We highlight the issues involved in the development of the novel pipeline and areas for further improvement.