HVC Interneuron Properties from Statistical Data Assimilation

2016/08/15 by Daniel Breen, Breen, Daniel, Sasha Shirman +7
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · Neuroscience · #Animal Behavior and Reproduction #Animal Vocal Communication and Behavior #FOS: Biological sciences #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC)

paper · pdf · doi:10.48550/arxiv.1608.04433

openalex publication_date 2016/08/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Data assimilation (DA) solves the inverse problem of inferring initial conditions given data and a model. Here we use biophysically motivated Hodgkin-Huxley (HH) models of avian HVCI neurons, experimentally obtained recordings of these neurons, and our data assimilation algorithm to infer the full set of parameters and a minimal set of ionic currents precisely reproducing the observed waveform information. We find many distinct validated sets of parameters selected by our DA method and choice of model. We conclude exploring variations on the inverse problem applied to neurons producing accurate or inaccurate results; by manipulating data presented to the algorithm, varying sample rate and waveform; and by manipulating the model by adding and subtracting ionic currents.

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