3 citations · 4 across the 4 of their papers we have counts for
10 papers
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…
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…