publications

Papers

2023

1. 

   Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamical Similarity Analysis

   Ostrow, Mitchell, Eisen, Adam,  Kozachkov, Leo and 1 more author

   ArXiv 2023

   Abs Bib Code

   How can we tell whether two neural networks are utilizing the same internal processes for a particular computation? This question is pertinent for multiple subfields of both neuroscience and machine learning, including neuroAI, mechanistic interpretability, and brain-machine interfaces. Standard approaches for comparing neural networks focus on the spatial geometry of latent states. Yet in recurrent networks, computations are implemented at the level of neural dynamics, which do not have a simple one-to-one mapping with geometry. To bridge this gap, we introduce a novel similarity metric that compares two systems at the level of their dynamics. Our method incorporates two components: Using recent advances in data-driven dynamical systems theory, we learn a high-dimensional linear system that accurately captures core features of the original nonlinear dynamics. Next, we compare these linear approximations via a novel extension of Procrustes Analysis that accounts for how vector fields change under orthogonal transformation. Via four case studies, we demonstrate that our method effectively identifies and distinguishes dynamic structure in recurrent neural networks (RNNs), whereas geometric methods fall short. We additionally show that our method can distinguish learning rules in an unsupervised manner. Our method therefore opens the door to novel data-driven analyses of the temporal structure of neural computation, and to more rigorous testing of RNNs as models of the brain.

   @article{ostrow2023geometry,
     title = {Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamical Similarity Analysis},
     author = {Ostrow, Mitchell and Eisen, Adam and Kozachkov, Leo and Fiete, Ila},
     year = {2023},
     journal = {ArXiv},
   }

2022

1. 

   Representational Geometry of Social Inference and Generalization in a Competitive Game

   Ostrow, Mitchell, Yang, G.R.,  and Seo, H.

   Robotics: Science and Systems 2022

   Abs Video Bib PDF Code

   The use of an internal model to infer and predict others‘ mental states and actions, broadly referred to as Theory of Mind (ToM), is a fundamental aspect of human social intelligence. Nevertheless, it remains unknown how these models are used during social interactions, and how they help an agent generalize to new contexts. We investigated a putative neural mechanism of ToM in a recurrent circuit through the lens of an artificial neural network trained with reinforcement learning (RL) to play a competitive matching pennies game against many algorithmic opponents. The network showed near-optimal performance against unseen opponents, indicating that it had acquired the capacity to adapt against new strategies online. Analysis of recurrent states during play against out-of-training-distribution (OOD) opponents in relation to those of within-training-distribution (WD) opponents revealed two similarity-based mechanisms by which the network might generalize: mapping to a known strategy (template matching) or known opponent category (interpolation). Even when the network’s strategy cannot be explained by template-matching or interpolation, the recurrent activity fell upon the low-dimensional manifold of the WD neural activity, suggesting the contribution of prior experience with WD opponents. Furthermore, these states occupied low-density edges of the WD-manifold, suggesting that the network can extrapolate beyond any learned strategy or category. Our results suggest that a neural implementation for ToM may be a reservoir of learned representations that provide the capacity for generalization via flexible access and reuse of these stored features.

   @article{MPRNN,
     title = {Representational Geometry of Social Inference and Generalization in a Competitive Game},
     author = {Ostrow, Mitchell and Yang, G.R. and Seo, H.},
     year = {2022},
     video = {https://youtu.be/8hiGqHDH-3w},
     journal = {Robotics: Science and Systems },
     workshop = {Social Intelligence in Humans and Robots},
   }

2021

1. Examining the Viability of Computational Psychiatry: Approaches into the Future

   Ostrow, Mitchell

   Yale Undergraduate Research Journal 2021

   Abs HTML

   As modern medicine becomes increasingly personalized, psychiatry lags behind, using poorly-understood drugs and therapies to treat mental disorders. With the advent of methods that capture large quantities of data, such as genome-wide analyses or fMRI, machine learning (ML) approaches have become prominent in neuroscience. This is promising for studying the brain’s function, but perhaps more importantly, these techniques can potentially predict the onset of disorder and treatment response. Experimental approaches that use naive machine learning algorithms have dominated research in computational psychiatry over the past decade. In a critical review and analysis, I argue that biologically realistic approaches will be more effective in clinical practice, and research trends should reflect this. Hybrid models are considered, and a brief case study on major depressive disorder is presented. Finally, I propose a novel four-step approach for the future implementation of computational methods in psychiatric clinics.

Talks

2023

1. 

   Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamic Similarity Analysis.

   Ostrow, M.B., Eisen, A.J.,  Kozachkov, L and 1 more author

   2023
2. 

   Do Deep Neural Networks Have Concepts?

   Ostrow, Mitchell, Chen, Tony,  Zhang, Cedegao and 1 more author

   Philosophy of Deep Learning Conference 2023
3. Investigating the Interplay of Anatomical, Biophysical, and Functional Modularity in Task-Optimized RNNs

   Ostrow, Mitchell, Sung, Hokyung,  and Fiete, Ila

   IBL U19 Conference 2023

2022

1. 

   Representational Geometry of Social Inference and Generalization in a Competitive Game

   Ostrow, Mitchell, Yang, G.R.,  and Seo, H.

   Robotics: Science and Systems (Spotlight Talk) 2022

   Bib

   @misc{MPRNO,
     title = {Representational Geometry of Social Inference and Generalization in a Competitive Game},
     author = {Ostrow, Mitchell and Yang, G.R. and Seo, H.},
     year = {2022},
     howpublished = {Robotics: Science and Systems (Spotlight Talk)},
   }

Posters

2023

1. 

   Predictive Models are not Enough for Explanation-Seeking Curiosity: A Case Study.

   Sung, Hokyung,  and Ostrow, Mitchell

   In Curiosity, Creativity and Complexity Conference 2023
2. 

   Network Dimensions Alter Reversal Learning Strategies

   In Computational and Systems Neuroscience (COSYNE) 2023
3. 

   Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamic Similarity Analysis.

   Ostrow, M.B., Eisen, A.J.,  Kozachkov, L and 1 more author

   In Cognitive Computational Neuroscience (CCN), selected as an oral presentation (5%) 2023
4. 

   Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamic Similarity Analysis.

   Ostrow, M.B., Eisen, A.J.,  Kozachkov, L and 1 more author

   In Computational Systems Neuroscience Conference (COSYNE), selected as an oral presentation (2%) 2023

2022

1. 

   Neural Representations of Opponent Strategy Support the Adaptive Behavior of Recurrent Actor-Critics in a Competitive Game

   Ostrow, Mitchell, Yang, G.R.,  and Seo, H.

   In Computational and Systems Neuroscience (COSYNE) 2022