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Found 7 papers, 3 papers with code
A Framework for Standardizing Similarity Measures in a Rapidly Evolving Field
Similarity measures are fundamental tools for quantifying the alignment between artificial and biological systems.
Baba Is AI: Break the Rules to Beat the Benchmark
Humans solve problems by following existing rules and procedures, and also by leaps of creativity to redefine those rules and objectives.
Differentiable Optimization of Similarity Scores Between Models and Brains
How similar can these synthetic datasets be to neural activity while failing to encode task relevant variables?
Recurrent neural network models for working memory of continuous variables: activity manifolds, connectivity patterns, and dynamic codes
We find and characterize the connectivity patterns that support the Clifford torus.
Emergence of functional and structural properties of the head direction system by optimization of recurrent neural networks
Overall, our results show that optimization of RNNs in a goal-driven task can recapitulate the structure and function of biological circuits, suggesting that artificial neural networks can be used to study the brain at the level of both neural activity and anatomical organization.
Emergence of grid-like representations by training recurrent neural networks to perform spatial localization
As a new way to understand these neural representations, we trained recurrent neural networks (RNNs) to perform navigation tasks in 2D arenas based on velocity inputs.
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.
