Multi-layer spike-in spike-out representation of Hippocampus circuitry

One of the frontier research of neuroscience focuses on replacing the damaged human Hippocampus regions with a prosthetic device replicating Hippocampus neural functionality. Since neural cognition in general and memory formation in particular are the result of neural processing in multiple layers of neural circuitries, it is crucial to develop neural models mimicking the same topology principals. This paper presents a two later spike-in-spike-out model of the Hippocampus neural circuitry with the emphasis on providing a methodology for tuning parameters of the model for DG→CA1 spike-in-spike-out transformation. Volterra series expansion with Laguerre basis functions represents layers of the circuit, in which network layers are communication by spikes. Utilizing the input-output recordings of the Hippocampus, the novel model predicts spike timing of the CA1 neurons. The simulation result justifies that neurobiological recording of DG→CA1 can be successfully generated by utilizing the methodology of this paper.