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Found 8 papers, 4 papers with code
Most discriminative stimuli for functional cell type clustering
Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed.
Probabilistic Neural Transfer Function Estimation with Bayesian System Identification
While deep neural network models have demonstrated excellent power on neural prediction, they usually do not provide the uncertainty of the resulting neural representations and derived statistics, such as the stimuli driving neurons optimally, from in silico experiments.
Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience
Thus, we offer a flexible approach to remove inter-experimental variability and integrate datasets across experiments in systems neuroscience.
Estimating smooth and sparse neural receptive fields with a flexible spline basis
Spatio-temporal receptive field (STRF) models are frequently used to approximate the computation implemented by a sensory neuron.
System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina
Here, we present a computational model of temporal processing in the inner retina, including inhibitory feedback circuits and realistic synaptic release mechanisms.
Neural system identification for large populations separating “what” and “where”
Traditional methods for neural system identification do not capitalize on this separation of “what” and “where”.
Neural system identification for large populations separating "what" and "where"
Our network scales well to thousands of neurons and short recordings and can be trained end-to-end.
Supervised learning sets benchmark for robust spike detection from calcium imaging signals
A fundamental challenge in calcium imaging has been to infer the timing of action potentials from the measured noisy calcium fluorescence traces.
