Embedded Files
Conference Publications
Global Distortions from Local Rewards: Neural Coding Strategies in Path-Integrating Neural Systems.
Acosta, F., Dinc, F., Redman, W., Madhav M., Klindt, D., Miolane, N. (2024). NeurIPS.
Not so griddy: Internal representations of RNNs path integrating more than one agent.
Redman, W., Acosta, F., Acosta-Mendoza, S., Miolane, N. (2024). NeurIPS. [paper]
Relating Representational Geometry to Cortical Geometry in Visual Cortex.
Acosta, F., Conwell, C., Sanborn, S., Klindt, D., Miolane, N. (2023). NeurIPS Workshop on Unifying Representations in Neural Models. [paper]
Conference Abstracts
The emergence of discrete grid cell modules from smooth gradients in the brain.
Khona, M., Chandra, S., Acosta, F., Fiete, I. (2021) Computational and Systems Neuroscience Conference (COSYNE). [paper]
Under Review
Geometry of the neural code: From low-dimensional latent circuits to high-dimensional neural manifolds.
Dinc, F., Klindt, D., Blanco-Pozo, M., Acosta, F., Jiang, Y., Ebrahimi, S., Yuan, P., Shai, A., Tanaka, H., Miolane, N., Schnitzer, M. (2024). Under Review.
In Preparation
A theory of latent circuits subserving computation in artificial and biological networks.
Dinc, F., Acosta, F., Jiang, Y., Blanco-Pozo, M., Klindt, D., Ebrahimi, S., Yuan, P., Shai, A., Tanaka, H., Miolane, N., Schnitzer, M. In Preparation.
Identifying Interpretable Visual Features in Artificial and Biological Neural System.
Klindt, D., Sanborn, S., Acosta, F., Poitevin, F., Miolane, N. [paper]. In Preparation.
Software Contributions
Geomstats
Miolane et al. (2023) Geomstats: A Python Package for Riemannian Geometry in Machine Learning. Geomstats release (v2.6.0+). [https://geomstats.github.io]
Nerometry
Acosta, F., ..., Miolane, N. Quantify geometric intelligence in natural and artificial brains. Under Development. [https://geometric-intelligence.github.io/index.html]
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