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Found 9 papers, 3 papers with code
See and Copy: Generation of complex compositional movements from modular and geometric RNN representations
A hallmark of biological intelligence and control is combinatorial generalization: animals are able to learn various things, then piece them together in new combinations to produce appropriate outputs for new tasks.
Winning the lottery with neurobiology: faster learning on many cognitive tasks with fixed sparse RNNs
However, fully-connected RNNs contrast structurally with their biological counterparts, which are extremely sparse ($\sim 0. 1$\%).
How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective
Recent works have examined theoretical and empirical properties of wide neural networks trained in the Neural Tangent Kernel (NTK) regime.
Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice
Third, the geometry of RNN dynamics reflects an induced coupling between the two separate inference processes necessary to solve the task.
Bipartite expander Hopfield networks as self-decoding high-capacity error correcting codes
Neural network models of memory and error correction famously include the Hopfield network, which can directly store---and error-correct through its dynamics---arbitrary N-bit patterns, but only for ~N such patterns.
Kernel RNN Learning (KeRNL)
We describe Kernel RNN Learning (KeRNL), a reduced-rank, temporal eligibility trace-based approximation to backpropagation through time (BPTT) for training recurrent neural networks (RNNs) that gives competitive performance to BPTT on long time-dependence tasks.
Associative content-addressable networks with exponentially many robust stable states
The brain must robustly store a large number of memories, corresponding to the many events encountered over a lifetime.
Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems
Self-localization during navigation with noisy sensors in an ambiguous world is computationally challenging, yet animals and humans excel at it.
Neurons and Cognition
