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Found 6 papers, 3 papers with code
Efficient characterization of electrically evoked responses for neural interfaces
Large-scale, high-density electrical recording and stimulation in primate retina were used as a lab prototype for an artificial retina.
YASS: Yet Another Spike Sorter
Spike sorting is a critical first step in extracting neural signals from large-scale electrophysiological data.
Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons
Decoding sensory stimuli from neural signals can be used to reveal how we sense our physical environment, and is valuable for the design of brain-machine interfaces.
Recognizing retinal ganglion cells in the dark
Here, we develop automated classifiers for functional identification of retinal ganglion cells, the output neurons of the retina, based solely on recorded voltage patterns on a large scale array.
Inferring synaptic conductances from spike trains with a biophysically inspired point process model
We show that the model fit to extracellular spike trains can predict excitatory and inhibitory conductances elicited by novel stimuli with nearly the same accuracy as a model trained directly with intracellular conductances.
Characterizing Neural Gain Control using Spike-triggered Covariance
Spike-triggered averaging techniques are effective for linear characterization of neural responses.
