Tosif Ahamed

Contact: ahamedt at janelia dott hhmi dot org

I am currently a theory fellow at HHMI's amazing Janelia Research Campus. I have a background in theoretical biophysicist, with a focus on dynamical systems, data-driven modeling and machine learning. My primary research focus is to understand the mathematical principles and neurobiological mechanisms by which animals adapt their behavior to suit the environmental context. Theoretically, this question entails the study of complex, multi-scale dynamical systems with time-varying parameters. While, biologically, it translates to how neural circuits can rewire in a time and context depentent manner (e.g. by neuromodulation through chemicals such as monoamines (dopamine, serotonin etc.)).

Although, the tools and ideas I use are general. I enjoy the experimental tractability of the roundworm C. elegans as a model system. I have developed mathematical models and computational tools to study worm behavior from the perspective of dynamical systems. In particular, I developed a continuous state-space from video recordings of moving worms. A state-space is a maximally predictive geometric representation of the measurement data. Analyzing the local state-space geometry and global topology provides detailed insights into the structure and organization of behavioral dynamics.

Recent technological breakthroughs have made it possible to perform simultaneous recordings of whole-brain neural activity along with behavioral imaging in moving worms. My current focus is to study adaptive behavior using such neurobehavioral datasets

Research

Full list of my publications is available on Google Scholar

Behavioral dynamics from data

(With Antonio Costa and Greg Stephens) Dynamical systems analysis analysis of C. elegans locomotion revealed that the dynamics could be captured by a 7 dimensional attractor. Study of the spectrum of Lyapunov exponents showed an intriguing mathematical structure, where behavioral variability shows signs of chaotic dynamics with a damped-driven Hamiltonian structure.

Link to the preprint
Link to the paper on Nature Physics
Nature Physics News and Views Feature
F1000 Recommendation
Link to Twitter thread

Extrasynaptic signaling enables an asymmetric juvenile motor circuit to produce a symmetric gait

(With Yangning Lu and Zhen Lab) The first stage larval C. elegans worms have an unusually asymmetric motor circuit, where muscles on only one side of the body receive direct excitation from motor neurons. Yet the animals can perform seemingly normal locmotion. How do muscles on the other side contract? In this study we used a combination of electron microscopy, calcium imaging, genetic perturbation, optogenetics and computational modeling to show that extrasynaptic signaling, in the form of volume transduction provides the excitation necesarry for muscle contraction on the other side.

Link to the preprint
Link to Twitter thread

Maximally predictive ensemble dynamics from data

(With Antonio Costa, David Jordan and Greg Stephens) Much like language, which has structure across multiple scales, from vibrations of throat muscles to phonemes; animal behavior is also composed of structure across multiple scales. How do we study such complex dynamics in a purely data-driven way has been a challenge. In this work we propose a solution using operator theoretic methods. We learn an operator representation of the dynamics, use it to build a maximally predictive state-space and through the operator eigen-vectors decompose the hidden scales.

Link to the preprint
Link to Twitter thread

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