Abstract
While multicompartment models have long been used to study the biophysics of neurons, it is still challenging to infer the parameters of such models from data including uncertainty estimates. Here, we performed Bayesian inference for the parameters of detailed neuron models of a photoreceptor and an OFF- and an ON-cone bipolar cell from the mouse retina based on two-photon imaging data. We obtained multivariate posterior distributions specifying plausible parameter ranges consistent with the data and allowing to identify parameters poorly constrained by the data. To demonstrate the potential of such mechanistic data-driven neuron models, we created a simulation environment for external electrical stimulation of the retina and optimized stimulus waveforms to target OFF- and ON-cone bipolar cells, a current major problem of retinal neuroprosthetics.
Highlights
Mechanistic models have been extensively used to study the biophysics underlying information processing in single neurons and small networks in great detail[1,2]
We show that the inferred models and the inference algorithm can be used to efficiently guide the design of electrical stimuli for retinal neuroprosthetics to selectively activate OFF- or ON-bipolar cell (BC)
We found that simulations generated with the parameter set with minimal total discrepancy or parameters sampled from the posterior matched the target traces very well for both OFF- and ON-BC models (Fig. 6)
Summary
Mechanistic models have been extensively used to study the biophysics underlying information processing in single neurons and small networks in great detail[1,2]. In contrast to phenomenological models used for neural system identification, such models try to preserve certain physical properties of the studied system to facilitate interpretation and a causal understanding. Biophysical models can incorporate the detailed anatomy of a neuron[3], its ion channel types[4,5] and the distributions of these channels[6] as well as synaptic connections to other cells[7]. For all these properties, the degree of realism can be adjusted as needed. While the classical Hodgkin-Huxley model with one compartment has already ten free parameters[4], detailed multicompartment models of neurons can have dozens or even hundreds of parameters[8]
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