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Shrivatsa-Deshmukh/README.md

Shrivatsa Deshmukh

Computational Neuroscience Researcher · Physicist & Engineer · Understanding Intelligence

From neural circuits to AI systems

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I trained as an engineer and a physicist - one taught me to build, the other taught me to model. Both kept pointing me toward the same question: how does a complex system produce intelligent behavior?

That question pulled me through biomechanics and medical robotics, and eventually to the thing I couldn't stop thinking about - the brain.

What I work on:

→ Neural data analysis & computational modeling across scales - single neurons to whole-brain dynamics

→ Statistical and Bayesian tools to decode how the brain encodes, adapts, and breaks down

→ Neurological and psychiatric disorders through a rigorous computational lens

Where I'm headed:

→ Building depth in computational neuroscience

→ NeuroAI eventually - because the path there runs through actually understanding the brain, not just borrowing its metaphors


Always open to research collaborations and people working at the neuroscience-AI boundary.

Popular repositories Loading

  1. speech-bci-decoder speech-bci-decoder Public

    GRU-based neural phoneme decoder adapted from Willett et al. (2023, Nature).

    Jupyter Notebook

  2. eeg-grasp-classification eeg-grasp-classification Public

    EEGNet for 3-class hand grasp motor imagery classification; BCI Competition IV 2020 Track 4. Session to session evaluation across 15 subjects- 64.97% accuracy.

    Python

  3. dmd-for-neuroscience dmd-for-neuroscience Public

    Learning notes on DMD variants - diagnosing spatiotemporal mode incoherence as the core bottleneck for long-horizon neural decomposition.

  4. astrocyte-calcium-pipeline astrocyte-calcium-pipeline Public

    Pipeline for AQuA2 astrocyte calcium event analysis, normalization, and non-parametric statistical comparison across experimental groups.

    Jupyter Notebook

  5. Shrivatsa-Deshmukh Shrivatsa-Deshmukh Public

  6. neural-mass-sbi neural-mass-sbi Public

    Amortized Bayesian inference for EEG neural mass model via SNPE