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Found 6 papers, 3 papers with code
Learning Time-Invariant Representations for Individual Neurons from Population Dynamics
This suggests that neuronal activity is a combination of its time-invariant identity and the inputs the neuron receives from the rest of the circuit.
A coupled autoencoder approach for multi-modal analysis of cell types
Recent developments in high throughput profiling of individual neurons have spurred data driven exploration of the idea that there exist natural groupings of neurons referred to as cell types.
Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators
Finally, we derive an in-silico implementation of ModProp that could serve as a low-complexity and causal alternative to backpropagation through time.
Reconstructing neuronal anatomy from whole-brain images
Reconstructing multiple molecularly defined neurons from individual brains and across multiple brain regions can reveal organizational principles of the nervous system.
Mixture Representation Learning with Coupled Autoencoders
Jointly identifying a mixture of discrete and continuous factors of variability without supervision is a key problem in unraveling complex phenomena.
Mixture Representation Learning with Coupled Autoencoding Agents
Jointly identifying a mixture of discrete and continuous factors of variability can help unravel complex phenomena.
