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Found 5 papers, 0 papers with code
Dimension of activity in random neural networks
Neural networks are high-dimensional nonlinear dynamical systems that process information through the coordinated activity of many connected units.
Action-modulated midbrain dopamine activity arises from distributed control policies
The model provides a computational account for numerous experimental findings about dopamine activity that cannot be explained by classic models of reinforcement learning in the basal ganglia.
Learning to Learn with Feedback and Local Plasticity
Interest in biologically inspired alternatives to backpropagation is driven by the desire to both advance connections between deep learning and neuroscience and address backpropagation's shortcomings on tasks such as online, continual learning.
Evolving the Olfactory System
In this study, we address the optimality of evolutionary design in olfactory circuits by studying artificial neural networks trained to sense odors.
Feedback alignment in deep convolutional networks
We demonstrate that a modification of the feedback alignment method that enforces a weaker form of weight symmetry, one that requires agreement of weight sign but not magnitude, can achieve performance competitive with backpropagation.
