8 papers
Implications of hierarchical Markov models of behavior: on irreversibility, predictability, and dimensionality
John J. Vastola, Kanaka Rajan
The maturation of quantitative tools for studying the high-level structure of animal behavior, and especially tools which represent spontaneous behavior as a sequence of stereotype…
A dimensional R2 regression metric
Jaesung Yoo, Stefan Lemke, Jian Zhong Guo +2
R2 score is the standard metric for evaluating regression tasks, offering a normalized magnitude-agnostic measure of accuracy that captures variance. However, R2 has three key limi…
A Variational Manifold Embedding Framework for Nonlinear Dimensionality Reduction
John J. Vastola, Samuel J. Gershman, Kanaka Rajan
Dimensionality reduction algorithms like principal component analysis (PCA) are workhorses of machine learning and neuroscience, but each has well-known limitations. Variants of PC…
Measuring and Controlling Solution Degeneracy across Task-Trained Recurrent Neural Networks
Ann Huang, Satpreet H. Singh, Flavio Martinelli +1
Task-trained recurrent neural networks (RNNs) are widely used in neuroscience and machine learning to model dynamical computations. To gain mechanistic insight into how neural syst…
Active Electrosensing and Communication in MARL-trained Weakly Electric Fish Collectives
Satpreet H. Singh, Sonja Johnson-Yu, Zhouyang Lu +7
How complex collective behavior emerges from individual interactions is a fundamental scientific question, but experimental cost and difficulty of simultaneous multi-brain recordin…
InputDSA: Demixing then Comparing Recurrent and Externally Driven Dynamics
Ann Huang, Mitchell Ostrow, Satpreet H. Singh +3
In control problems and basic scientific modeling, it is important to compare observations with dynamical simulations. For example, comparing two neural systems can shed light on t…