Subhaneil Lahiri

Large rotating AdS black holes from fluid mechanics

We use the AdS/CFT correspondence to argue that large rotating black holes in global AdSD spaces are dual to stationary solutions of the relativistic Navier-Stokes equations on SD−2. Reading off the equation of state of this fluid from the thermodynamics of non-rotating black holes, we proceed to construct the nonlinear spinning solutions of fluid mechanics that are dual to rotating black holes. In all known examples, the thermodynamics and the local stress tensor of our solutions are in precise agreement with the thermodynamics and boundary stress tensor of the spinning black holes. Our fluid dynamical description applies to large non-extremal black holes as well as a class of large non-supersymmetric extremal black holes, but is never valid for supersymmetric black holes. Our results yield predictions for the thermodynamics of all large black holes in all theories of gravity on AdS spaces, for example, string theory on AdS5 × S5 and M theory on AdS4 × S7 and AdS7 × S4.

Two Alternating Motor Programs Drive Navigation in Drosophila Larva

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.

Plasmarings as dual black rings

We construct solutions to the relativistic Navier-Stokes equations that describe the long wavelength collective dynamics of the deconfined plasma phase of = 4 Yang Mills theory compactified down to d = 3 on a Scherk-Schwarz circle and higher dimensional generalisations. Our solutions are stationary, axially symmetric spinning balls and rings of plasma. These solutions, which are dual to (yet to be constructed) rotating black holes and black rings in Scherk-Schwarz compactified AdS5 and AdS6, and have properties that are qualitatively similar to those of black holes and black rings in flat five dimensional supergravity.

Statistical mechanics of complex neural systems and high dimensional data

Recent experimental advances in neuroscience have opened new vistas into the immense complexity of neuronal networks. This proliferation of data challenges us on two parallel fronts. First, how can we form adequate theoretical frameworks for understanding how dynamical network processes cooperate across widely disparate spatiotemporal scales to solve important computational problems? Second, how can we extract meaningful models of neuronal systems from high dimensional datasets? To aid in these challenges, we give a pedagogical review of a collection of ideas and theoretical methods arising at the intersection of statistical physics, computer science and neurobiology. We introduce the interrelated replica and cavity methods, which originated in statistical physics as powerful ways to quantitatively analyze large highly heterogeneous systems of many interacting degrees of freedom. We also introduce the closely related notion of message passing in graphical models, which originated in computer science as a distributed algorithm capable of solving large inference and optimization problems involving many coupled variables. We then show how both the statistical physics and computer science perspectives can be applied in a wide diversity of contexts to problems arising in theoretical neuroscience and data analysis. Along the way we discuss spin glasses, learning theory, illusions of structure in noise, random matrices, dimensionality reduction and compressed sensing, all within the unified formalism of the replica method. Moreover, we review recent conceptual connections between message passing in graphical models, and neural computation and learning. Overall, these ideas illustrate how statistical physics and computer science might provide a lens through which we can uncover emergent computational functions buried deep within the dynamical complexities of neuronal networks.

Lumps of plasma in arbitrary dimensions

We use the AdS/CFT correspondence in a regime in which the field theory reduces to fluid dynamics to construct an infinite class of new black objects in Scherk-Schwarz compactified AdSd+2 space. Our configurations are dual to black objects that generalize black rings and have horizon topology Sd−nTn for

A saturation hypothesis to explain both enhanced and impaired learning with enhanced plasticity

n \leq \frac{{d - 1}}{2}

Accurate estimation of neural population dynamics without spike sorting

. Locally our fluid configurations are plasma sheets that curve around into tori whose radii are large compared to the thickness of the sheets (the ratio of these radii constitutes a small parameter that permits the perturbative construction of these configurations). These toroidal configurations are stabilized by angular momentum. We study solutions whose dual horizon topologies are S3 × S1, S4 × S1 and S3 × T2 in detail; in particular we investigate the thermodynamic properties of these objects. We also present a formal general construction of the most general stationary configuration of fluids with boundaries that solve the d dimensional relativistic Navier-Stokes equation.

Supersymmetric states of = 4 Yang-Mills from giant gravitons

Across many studies, animals with enhanced synaptic plasticity exhibit either enhanced or impaired learning, raising a conceptual puzzle: how enhanced plasticity can yield opposite learning outcomes? Here, we show that the recent history of experience can determine whether mice with enhanced plasticity exhibit enhanced or impaired learning in response to the same training. Mice with enhanced cerebellar LTD, due to double knockout (DKO) of MHCI H2-Kb/H2-Db (KbDb−/−), exhibited oculomotor learning deficits. However, the same mice exhibited enhanced learning after appropriate pre-training. Theoretical analysis revealed that synapses with history-dependent learning rules could recapitulate the data, and suggested that saturation may be a key factor limiting the ability of enhanced plasticity to enhance learning. Optogenetic stimulation designed to saturate LTD produced the same impairment in WT as observed in DKO mice. Overall, our results suggest that the recent history of activity and the threshold for synaptic plasticity conspire to effect divergent learning outcomes. DOI: http://dx.doi.org/10.7554/eLife.20147.001

Exponential expressivity in deep neural networks through transient chaos

A central goal of systems neuroscience is to relate an organism’s neural activity to behavior. Neural population analysis often begins by reducing the dimensionality of the data to focus on the patterns most relevant to a given task. A major practical hurdle to data analysis is spike sorting, and this problem is growing rapidly as the number of neurons measured increases. Here, we investigate whether spike sorting is necessary to estimate neural 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 re-analyzed data from three previous studies and found that neural dynamics and scientific conclusions are quite similar using multi-unit threshold crossings in place of 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.