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- Subhaneil Lahiri
- 2008

We construct solutions to the relativistic Navier-Stokes equations that describe the long wavelength collective dynamics of the deconfined plasma phase of N = 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.… (More)

- Indranil Biswas, Davide Gaiotto, Subhaneil Lahiri
- 2008

Mikhailov has constructed an infinite family of 1 8 BPS D3-branes in AdS5 × S5. We regulate Mikhailov’s solution space by focussing on finite dimensional submanifolds. Our submanifolds are topologically complex projective spaces with symplectic form cohomologically equal to 2πN times the Fubini-Study Kähler class. Upon quantization and removing the… (More)

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 NavierStokes 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… (More)

- Subhaneil Lahiri, Konlin Shen, +5 authors Aravinthan D. T. Samuel
- PloS one
- 2011

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… (More)

- Subhaneil Lahiri, Surya Ganguli
- NIPS
- 2013

An incredible gulf separates theoretical models of synapses, often described solely by a single scalar value denoting the size of a postsynaptic potential, from the immense complexity of molecular signaling pathways underlying real synapses. To understand the functional contribution of such molecular complexity to learning and memory, it is essential to… (More)

We combine Riemannian geometry with the mean field theory of high dimensional chaos to study the nature of signal propagation in generic, deep neural networks with random weights. Our results reveal an order-to-chaos expressivity phase transition, with networks in the chaotic phase computing nonlinear functions whose global curvature grows exponentially… (More)

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… (More)

- Subhaneil Lahiri, Jascha Sohl-Dickstein, Surya Ganguli
- ArXiv
- 2016

Maximizing the speed and precision of communication while minimizing power dissipation is a fundamental engineering design goal. Also, biological systems achieve remarkable speed, precision and power efficiency using poorly understood physical design principles. Powerful theories like information theory and thermodynamics do not provide general limits on… (More)

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 S × T for n ≤ d−1 2 . Locally our fluid configurations are plasma… (More)

- Td Barbara Nguyen-Vu, Grace Q Zhao, +5 authors Jennifer L Raymond
- eLife
- 2017

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… (More)