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Found 10 papers, 3 papers with code
Code-specific policy gradient rules for spiking neurons
Here, we show that different learning rules emerge from a policy gradient approach depending on which features of the spike trains are assumed to influence the reward signals, i. e., depending on which neural code is in effect.
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.
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.
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data
A common problem in neuroscience is to elucidate the collective neural representations of behaviorally important variables such as head direction, spatial location, upcoming movements, or mental spatial transformations.
Automatic differentiation of Sylvester, Lyapunov, and algebraic Riccati equations
Sylvester, Lyapunov, and algebraic Riccati equations are the bread and butter of control theorists.
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data
Here, we propose a new family of “dynamical” priors over trajectories, in the form of GP covariance functions that express a property shared by most dynamical systems: temporal non-reversibility.
Natural continual learning: success is a journey, not (just) a destination
In contrast, artificial agents are prone to 'catastrophic forgetting' whereby performance on previous tasks deteriorates rapidly as new ones are acquired.
iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data
To achieve this, a common approach is to record neural populations in behaving animals, and model these data as emanating from a latent dynamical system whose state trajectories can then be related back to behavioural observations via some form of decoding.
Scalable Bayesian GPFA with automatic relevance determination and discrete noise models
We apply bGPFA to continuous recordings spanning 30 minutes with over 14 million data points from primate motor and somatosensory cortices during a self-paced reaching task.
