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Found 7 papers, 0 papers with code
A universal probabilistic spike count model reveals ongoing modulation of neural variability
We find that variability in these cells defies a simple parametric relationship with mean spike count as assumed in standard models, its modulation by external covariates can be comparably strong to that of the mean firing rate, and slow low-dimensional latent factors explain away neural correlations.
Exact natural gradient in deep linear networks and its application to the nonlinear case
Stochastic gradient descent (SGD) remains the method of choice for deep learning, despite the limitations arising for ill-behaved objective functions.
Efficient state-space modularization for planning: theory, behavioral and neural signatures
Even in state-spaces of modest size, planning is plagued by the “curse of dimensionality”.
Analog Memories in a Balanced Rate-Based Network of E-I Neurons
The persistent and graded activity often observed in cortical circuits is sometimes seen as a signature of autoassociative retrieval of memories stored earlier in synaptic efficacies.
Fast Sampling-Based Inference in Balanced Neuronal Networks
Multiple lines of evidence support the notion that the brain performs probabilistic inference in multiple cognitive domains, including perception and decision making.
A Dual Algorithm for Olfactory Computation in the Locust Brain
We study the early locust olfactory system in an attempt to explain its well-characterized structure and dynamics.
Correlations strike back (again): the case of associative memory retrieval
It has long been recognised that statistical dependencies in neuronal activity need to be taken into account when decoding stimuli encoded in a neural population.
