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Found 7 papers, 0 papers with code
Reconstruction of Natural Visual Scenes from Neural Spikes with Deep Neural Networks
The SID is an end-to-end decoder with one end as neural spikes and the other end as images, which can be trained directly such that visual scenes are reconstructed from spikes in a highly accurate fashion.
Probabilistic Inference of Binary Markov Random Fields in Spiking Neural Networks through Mean-field Approximation
Recent studies have suggested that the cognitive process of the human brain is realized as probabilistic inference and can be further modeled by probabilistic graphical models like Markov random fields.
Revealing Fine Structures of the Retinal Receptive Field by Deep Learning Networks
When a deep CNN with many layers is used for the visual system, it is not easy to compare the structure components of CNNs with possible neuroscience underpinnings due to highly complex circuits from the retina to higher visual cortex.
Neural System Identification with Spike-triggered Non-negative Matrix Factorization
Furthermore, we show that STNMF can separate spikes of a ganglion cell into a few subsets of spikes where each subset is contributed by one presynaptic bipolar cell.
Winner-Take-All as Basic Probabilistic Inference Unit of Neuronal Circuits
Taken together, our results suggest that the WTA circuit could be seen as the minimal inference unit of neuronal circuits.
A simple blind-denoising filter inspired by electrically coupled photoreceptors in the retina
Photoreceptors in the retina are coupled by electrical synapses called "gap junctions".
Revealing structure components of the retina by deep learning networks
By training CNNs with white noise images to predicate neural responses, we found that convolutional filters learned in the end are resembling to biological components of the retinal circuit.
