4 citations · 13 across the 20 of their papers we have counts for
23 papers
Modeling Neural Activity with Conditionally Linear Dynamical Systems
Victor Geadah, Amin Nejatbakhsh, David Lipshutz +2
Neural population activity exhibits complex, nonlinear dynamics, varying in time, over trials, and across experimental conditions. Here, we develop Conditionally Linear Dynamical S…
Comparing noisy neural population dynamics using optimal transport distances
Amin Nejatbakhsh, Victor Geadah, Alex H. Williams +1
Biological and artificial neural systems form high-dimensional neural representations that underpin their computational capabilities. Methods for quantifying geometric similarity i…
What Representational Similarity Measures Imply about Decodable Information
Sarah E. Harvey, David Lipshutz, Alex H. Williams
Neural responses encode information that is useful for a variety of downstream tasks. A common approach to understand these systems is to build regression models or ``decoders'' th…
Discriminating image representations with principal distortions
Jenelle Feather, David Lipshutz, Sarah E. Harvey +2
Image representations (artificial or biological) are often compared in terms of their global geometric structure; however, representations with similar global structure can have st…
Shaping the distribution of neural responses with interneurons in a recurrent circuit model
David Lipshutz, Eero P. Simoncelli
Efficient coding theory posits that sensory circuits transform natural signals into neural representations that maximize information transmission subject to resource constraints. L…
Neuronal Temporal Filters as Normal Mode Extractors
Siavash Golkar, Jules Berman, David Lipshutz +3
To generate actions in the face of physiological delays, the brain must predict the future. Here we explore how prediction may lie at the core of brain function by considering a ne…
Adaptive whitening with fast gain modulation and slow synaptic plasticity
Lyndon R. Duong, Eero P. Simoncelli, Dmitri B. Chklovskii +1
Neurons in early sensory areas rapidly adapt to changing sensory statistics, both by normalizing the variance of their individual responses and by reducing correlations between the…
Normative framework for deriving neural networks with multi-compartmental neurons and non-Hebbian plasticity
David Lipshutz, Yanis Bahroun, Siavash Golkar +2
An established normative approach for understanding the algorithmic basis of neural computation is to derive online algorithms from principled computational objectives and evaluate…
Adaptive whitening in neural populations with gain-modulating interneurons
Lyndon R. Duong, David Lipshutz, David J. Heeger +2
Statistical whitening transformations play a fundamental role in many computational systems, and may also play an important role in biological sensory systems. Existing neural circ…
An online algorithm for contrastive Principal Component Analysis
Siavash Golkar, David Lipshutz, Tiberiu Tesileanu +1
Finding informative low-dimensional representations that can be computed efficiently in large datasets is an important problem in data analysis. Recently, contrastive Principal Com…
Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation
David Lipshutz, Cengiz Pehlevan, Dmitri B. Chklovskii
Early sensory systems in the brain rapidly adapt to fluctuating input statistics, which requires recurrent communication between neurons. Mechanistically, such recurrent communicat…
A biologically plausible neural network for local supervision in cortical microcircuits
Siavash Golkar, David Lipshutz, Yanis Bahroun +2
The backpropagation algorithm is an invaluable tool for training artificial neural networks; however, because of a weight sharing requirement, it does not provide a plausible model…
A simple normative network approximates local non-Hebbian learning in the cortex
Siavash Golkar, David Lipshutz, Yanis Bahroun +2
To guide behavior, the brain extracts relevant features from high-dimensional data streamed by sensory organs. Neuroscience experiments demonstrate that the processing of sensory i…
A biologically plausible neural network for Slow Feature Analysis
David Lipshutz, Charlie Windolf, Siavash Golkar +1
Learning latent features from time series data is an important problem in both machine learning and brain function. One approach, called Slow Feature Analysis (SFA), leverages the…
Biologically plausible single-layer networks for nonnegative independent component analysis
David Lipshutz, Cengiz Pehlevan, Dmitri B. Chklovskii
An important problem in neuroscience is to understand how brains extract relevant signals from mixtures of unknown sources, i.e., perform blind source separation. To model how the…
A biologically plausible neural network for multi-channel Canonical Correlation Analysis
David Lipshutz, Yanis Bahroun, Siavash Golkar +2
Cortical pyramidal neurons receive inputs from multiple distinct neural populations and integrate these inputs in separate dendritic compartments. We explore the possibility that c…
Customer-server population dynamics in heavy traffic
Rami Atar, Prasenjit Karmakar, David Lipshutz
We study a many-server queueing model with server vacations, where the population size dynamics of servers and customers are coupled: a server may leave for vacation only when no c…
Sensitivity analysis for the stationary distribution of reflected Brownian motion in a convex polyhedral cone
David Lipshutz, Kavita Ramanan
Reflected Brownian motion (RBM) in a convex polyhedral cone arises in a variety of applications ranging from the theory of stochastic networks to math finance, and under general st…
Stability of synchronous slowly oscillating periodic solutions for systems of delay differential equations with coupled nonlinearity
David Lipshutz, Robert J. Lipshutz
We study stability of so-called synchronous slowly oscillating periodic solutions (SOPSs) for a system of identical delay differential equations (DDEs) with linear decay and nonlin…
A Monte Carlo method for estimating sensitivities of reflected diffusions in convex polyhedral domains
David Lipshutz, Kavita Ramanan
In this work we develop an effective Monte Carlo method for estimating sensitivities, or gradients of expectations of sufficiently smooth functionals, of a reflected diffusion in a…
Exit time asymptotics for small noise stochastic delay differential equations
David Lipshutz
Dynamical system models with delayed dynamics and small noise arise in a variety of applications in science and engineering. In many applications, stable equilibrium or periodic be…
Pathwise differentiability of reflected diffusions in convex polyhedral domains
David Lipshutz, Kavita Ramanan
Reflected diffusions in convex polyhedral domains arise in a variety of applications, including interacting particle systems, queueing networks, biochemical reaction networks and m…
On directional derivatives of Skorokhod maps in convex polyhedral domains
David Lipshutz, Kavita Ramanan
The study of both sensitivity analysis and differentiability of the stochastic flow of a reflected process in a convex polyhedral domain is challenging because the dynamics are dis…