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HVC Interneuron Properties from Statistical Data Assimilation
TL;DR: This work uses biophysically motivated Hodgkin-Huxley (HH) models of avian HVCI neurons, experimentally obtained recordings of these neurons, and the data assimilation algorithm to infer the full set of parameters and a minimal set of ionic currents precisely reproducing the observed waveform information.
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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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Citations
Assimilation of Biophysical Neuronal Dynamics in Neuromorphic VLSI
Jun Wang,Daniel Breen,Abraham Akinin,Frederic D. Broccard,Henry D. I. Abarbanel,Gert Cauwenberghs +5 more
TL;DR: A set of procedures assimilating and emulating neurobiological data on a neuromorphic very large scale integrated (VLSI) circuit to enable the use of NeuroDyn as a tool to probe electrical and molecular properties of functional neural circuits.
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The Dynamics of Nonlinear Inference
Nirag Kadakia
- 01 Jan 2017
TL;DR: Kadakia et al. as mentioned in this paper proposed the variational annealing (VA) technique, which leverages an inherent separability between the convex and nonconvex contributions of the resulting functional forms.
Optimal control methods for nonlinear parameter estimation in biophysical neuron models
TL;DR: In this paper , a method for joint parameter and state inference that combines traditional state space modeling with chaotic synchronization and optimal control is proposed, tailored particularly to situations with considerable measurement noise, sparse observability, very nonlinear or chaotic dynamics, and highly uninformed priors.
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An optimization method for predicting the functional mode of a biological neuronal network
Eve Armstrong
- 09 Nov 2017
TL;DR: An optimization procedure is employed to estimate parameters of a small model biological neuronal network, which assumes multiple modes of activity, depending on parameter values, and shows how this method can reduce a model of unnecessarily high complexity to a representation that contains the maximum dimensionality contained in the available measurements.
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Optimal control methods for nonlinear parameter estimation in biophysical neuron models
TL;DR: In this article , a method for joint parameter and state inference that combines traditional state space modeling with chaotic synchronization and optimal control is proposed. But it is tailored particularly to situations with considerable measurement noise, sparse observability, very nonlinear or chaotic dynamics, and highly uninformed priors.
1
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