About

My research broadly focuses on understanding the dynamics of neural circuits that subserve cognitive functions in both artificial and biological systems, by adopting problem-solving approaches from physics, mathematics, electrical engineering, and computer science.

Publications

submitted

Raju P. C. Geometric Stability: The Missing Axis of Representations. [link] [pdf] [code]

Raju P. C. The Geometric Canary: Predicting Steerability and Detecting Drift via Representational Stability. [link] [pdf] [code]

Raju P. C. The Geometric Alignment Tax: Tokenization vs. Continuous Geometry in Scientific Foundation Models. [link] [pdf] [code]

Raju P. C. Geometric coherence of single-cell CRISPR perturbations reveals regulatory architecture and predicts cellular stress. [link] [pdf] [code]

Raju P. C. Recurrent circuitry stabilizes representational geometry across neural circuits. [link] [pdf] [code]

Raju P. C. Geometric Phase Transition Enables Extreme Hippocampal Memory Capacity. [link] [pdf] [code]

2024

Barreiro, A. K., Fontenele, A. J., Cheng, L., Raju P. C., Gautam, S. H., & Shew, W. L. (2024). Sensory input to cortex encoded on low-dimensional periphery-correlated subspaces. PNAS Nexus 3(1), [link] [pdf] [si]

2020

Golan, T., Raju P. C., & Kriegeskorte, N. (2020). Controversial stimuli: Pitting neural networks against each other as models of human cognition. Proceedings of the National Academy of Sciences, 117(47), 29330-29337 [link] [pdf] [si] [code]

Perspectives

2026

Raju P. C. From Syntax to Semantics: Geometric Stability as the Missing Axis of Perturbation Biology. [link] [pdf] [code]

Conference Abstracts

To Appear

Raju P. C. (2026). Geometric Stability: The Missing Axis of Representations. Spotlight Award. Unpublished conference paper. 7th International Conference on the Mathematics of Neuroscience and AI. Rome, Italy.

Raju P. C. (2026). Geometric Phase Transition Enables Extreme Hippocampal Memory Capacity. Unpublished conference paper. 7th International Conference on the Mathematics of Neuroscience and AI. Rome, Italy.

2020

Golan, T., Raju P. C., & Kriegeskorte, N. (2020). Controversial stimuli: adjudicating between deep neural network models of biological vision with synthetic images. Journal of Vision 20(11), 947 doi: 10.1167/jov.20.11.947 [link]

Golan, T., Raju P. C., & Kriegeskorte, N. (2020). Adjudicating between deep neural network models of biological vision with controversial stimuli. Unpublished conference paper. Computational and Systems Neuroscience (Cosyne). Denver, CO. (Poster III-53) [link] [poster]

Software

Shesha: Self-Consistency Metrics for Representational Stability.

PyPI Downloads DOI
Tutorials

Explore shesha with these interactive notebooks (each takes < 5 minutes to run):

Open In Colab LLM Embeddings — Analyze embedding stability across layers and models using feature_split.
Open In Colab Steering Vectors — Compute steering vectors from contrastive pairs and measure their effectiveness and consistency.
Open In Colab Vision Models — Compare geometric stability and class separability across ResNets, ViTs, and other vision architectures.
Open In Colab Representational Drift — Measure drift caused by Gaussian noise injection and LoRA fine-tuning using rdm_drift.
Open In Colab Training Dynamics — Track geometric stability during model training to detect representation collapse or divergence.
Open In Colab CRISPR (Bio) — Use shesha.bio to analyze stability and effect sizes in single-cell CRISPR perturbation experiments.

Contact

I am always happy to help anyone looking to learn or looking to discuss anything related to neuroscience or AI.