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Found 6 papers, 1 papers with code
Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics
How are the spatial patterns of spontaneous and evoked population responses related?
full-FORCE: A Target-Based Method for Training Recurrent Networks
We present a target-based method for modifying the full connectivity matrix of a recurrent network to train it to perform tasks involving temporally complex input/output transformations.
A brain basis of dynamical intelligence for AI and computational neuroscience
To motivate a brain basis of neural computation, we present a dynamical view of intelligence from which we elaborate concepts of sparsity in network structure, temporal dynamics, and interactive learning.
Efficient and robust multi-task learning in the brain with modular latent primitives
For this reason, a central aspect of human learning is the ability to recycle previously acquired knowledge in a way that allows for faster, less resource-intensive acquisition of new skills.
Curriculum learning as a tool to uncover learning principles in the brain
Using recurrent neural networks (RNNs) and models of common experimental neuroscience tasks, we demonstrate that curricula can be used to differentiate learning principles using target-based and a representation-based loss functions as use cases.
TRAKR – A reservoir-based tool for fast and accurate classification of neural time-series patterns
Therefore, TRAKR can be used as a fast and accurate tool to distinguish patterns in complex nonlinear time-series data, such as neural recordings.
