I am currently a post-doc in the Ganguli Lab (Department of Applied Physics, Stanford University) where I study theoretical neurobiology, in particular the effects of synaptic plasticity mechanisms on learning and memory.

Before this, I was a post-doc in the Samuel Lab (Department of Physics and Center for Brain Science, Harvard University) where I studied brain and behavior in the Drosophila larva and C. elegans.

Prior to that, I was a graduate student in the High Energy Theory Group (Department of Physics, Harvard University) where I studied string theory, using the AdS/CFT correspondence to investigate black hole thermodynamics, especially in the fluid mechanics regime.

Academic Appointments

  • Physical Science Research Scientist, Edward L. Ginzton Laboratory

Honors & Awards

  • Scott Prize for best performance in M.Phys. examination, Oxford University (2003)
  • Certificate of distinction in teaching, Harvard University (Spring 2008)
  • Outstanding Paper Award, Neural Information Processing Systems (December 2013)

Professional Education

  • Doctor of Philosophy, Harvard University, Physics (2009)
  • Master of Physics, University of Oxford, Physics (2003)

Current Research and Scholarly Interests

I am interested in most aspects of theoretical neurobiology. I am currently studying the role of complex synapses in learning and memory.

Our brains store long term memories by adjusting the strengths of the synapses that connect neurons. The tendency for new memories to overwrite old ones leads to a trade-off between learning and remembering: if synapses are too plastic older memories will be wiped out too easily, if they are too rigid it becomes difficult to learn new memories in the first place. I am studying theoretical models of synapses to understand how their internal structure can be used to balance these effects and maximize their memory storage.

All Publications

  • Universal energy-accuracy tradeoffs in nonequilibrium cellular sensing. Physical review. E Harvey, S. E., Lahiri, S., Ganguli, S. 2023; 108 (1-1): 014403


    We combine stochastic thermodynamics, large deviation theory, and information theory to derive fundamental limits on the accuracy with which single cell receptors can estimate external concentrations. As expected, if the estimation is performed by an ideal observer of the entire trajectory of receptor states, then no energy consuming nonequilibrium receptor that can be divided into bound and unbound states can outperform an equilibrium two-state receptor. However, when the estimation is performed by a simple observer that measures the fraction of time the receptor is bound, we derive a fundamental limit on the accuracy of general nonequilibrium receptors as a function of energy consumption. We further derive and exploit explicit formulas to numerically estimate a Pareto-optimal tradeoff between accuracy and energy. We find this tradeoff can be achieved by nonuniform ring receptors with a number of states that necessarily increases with energy. Our results yield a thermodynamic uncertainty relation for the time a physical system spends in a pool of states and generalize the classic Berg-Purcell limit [H. C. Berg and E. M. Purcell, Biophys. J. 20, 193 (1977)0006-349510.1016/S0006-3495(77)85544-6] on cellular sensing along multiple dimensions.

    View details for DOI 10.1103/PhysRevE.108.014403

    View details for PubMedID 37583173

  • Accurate Estimation of Neural Population Dynamics without Spike Sorting. Neuron Trautmann, E. M., Stavisky, S. D., Lahiri, S. n., Ames, K. C., Kaufman, M. T., O'Shea, D. J., Vyas, S. n., Sun, X. n., Ryu, S. I., Ganguli, S. n., Shenoy, K. V. 2019


    A central goal of systems neuroscience is to relate an organism's neural activity to behavior. Neural population analyses often reduce the data dimensionality to focus on relevant activity patterns. A major hurdle to data analysis is spike sorting, and this problem is growing as the number of recorded neurons increases. Here, we investigate whether spike sorting is necessary to estimate neural population dynamics. The theory of random projections suggests that we can accurately estimate the geometry of low-dimensional manifolds from a small number of linear projections of the data. We recorded data using Neuropixels probes in motor cortex of nonhuman primates and reanalyzed data from three previous studies and found that neural dynamics and scientific conclusions are quite similar using multiunit threshold crossings rather than sorted neurons. This finding unlocks existing data for new analyses and informs the design and use of new electrode arrays for laboratory and clinical use.

    View details for DOI 10.1016/j.neuron.2019.05.003

    View details for PubMedID 31171448

  • A saturation hypothesis to explain both enhanced and impaired learning with enhanced plasticity ELIFE Nguyen-Vu, T., Zhao, G. Q., Lahiri, S., Kimpo, R. R., Lee, H., Ganguli, S., Shatz, C. J., Raymond, J. L. 2017; 6
  • Exponential expressivity in deep neural networks through transient chaos Neural Information Processing Systems (NIPS) Poole, B., Subhaneil, L., Raghu, M., Sohl-Dickstein, J., Ganguli, S. 2016: 3360–3368
  • Statistical mechanics of complex neural systems and high dimensional data JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT Advani, M., Lahiri, S., Ganguli, S. 2013
  • Two Alternating Motor Programs Drive Navigation in Drosophila Larva PLOS ONE Lahiri, S., Shen, K., Klein, M., Tang, A., Kane, E., Gershow, M., Garrity, P., Samuel, A. D. 2011; 6 (8)


    When placed on a temperature gradient, a Drosophila larva navigates away from excessive cold or heat by regulating the size, frequency, and direction of reorientation maneuvers between successive periods of forward movement. Forward movement is driven by peristalsis waves that travel from tail to head. During each reorientation maneuver, the larva pauses and sweeps its head from side to side until it picks a new direction for forward movement. Here, we characterized the motor programs that underlie the initiation, execution, and completion of reorientation maneuvers by measuring body segment dynamics of freely moving larvae with fluorescent muscle fibers as they were exposed to temporal changes in temperature. We find that reorientation maneuvers are characterized by highly stereotyped spatiotemporal patterns of segment dynamics. Reorientation maneuvers are initiated with head sweeping movement driven by asymmetric contraction of a portion of anterior body segments. The larva attains a new direction for forward movement after head sweeping movement by using peristalsis waves that gradually push posterior body segments out of alignment with the tail (i.e., the previous direction of forward movement) into alignment with the head. Thus, reorientation maneuvers during thermotaxis are carried out by two alternating motor programs: (1) peristalsis for driving forward movement and (2) asymmetric contraction of anterior body segments for driving head sweeping movement.

    View details for DOI 10.1371/journal.pone.0023180

    View details for Web of Science ID 000293953700006

    View details for PubMedID 21858019

    View details for PubMedCentralID PMC3156121

  • Lumps of plasma in arbitrary dimensions JOURNAL OF HIGH ENERGY PHYSICS Bhattacharya, J., Lahiri, S. 2010
  • Large rotating AdS black holes from fluid mechanics JOURNAL OF HIGH ENERGY PHYSICS Bhattacharyya, S., Lahiri, S., Loganayagam, R., Minwalla, S. 2008
  • Plasmarings as dual black rings JOURNAL OF HIGH ENERGY PHYSICS Lahiri, S., Minwalla, S. 2008
  • Supersymmetric states of N=4 Yang-Mills from giant gravitons JOURNAL OF HIGH ENERGY PHYSICS Biswas, I., Gaiotto, D., Lahiri, S., Minwalla, S. 2007