
A Similaritypreserving Neural Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit
Learning to detect contentindependent transformations from data is one ...
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A Neural Network with Local Learning Rules for Minor Subspace Analysis
The development of neuromorphic hardware and modeling of biological neur...
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A biologically plausible neural network for local supervision in cortical microcircuits
The backpropagation algorithm is an invaluable tool for training artific...
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A simple normative network approximates local nonHebbian learning in the cortex
To guide behavior, the brain extracts relevant features from highdimens...
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A biologically plausible neural network for Slow Feature Analysis
Learning latent features from time series data is an important problem i...
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BioNICA: A biologically inspired singlelayer network for Nonnegative Independent Component Analysis
Blind source separation, the problem of separating mixtures of unknown s...
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A biologically plausible neural network for multichannel Canonical Correlation Analysis
Cortical pyramidal neurons receive inputs from multiple distinct neural ...
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Neuroscienceinspired online unsupervised learning algorithms
Although the currently popular deep learning networks achieve unpreceden...
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Biologically Plausible Online Principal Component Analysis Without Recurrent Neural Dynamics
Artificial neural networks that learn to perform Principal Component Ana...
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Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching
Big data problems frequently require processing datasets in a streaming ...
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Blind nonnegative source separation using biological neural networks
Blind source separation, i.e. extraction of independent sources from a m...
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Why do similarity matching objectives lead to Hebbian/antiHebbian networks?
Modeling selforganization of neural networks for unsupervised learning ...
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Selfcalibrating Neural Networks for Dimensionality Reduction
Recently, a novel family of biologically plausible online algorithms for...
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Optimization theory of Hebbian/antiHebbian networks for PCA and whitening
In analyzing information streamed by sensory organs, our brains face cha...
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A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks
To make sense of the world our brains must analyze highdimensional data...
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A Hebbian/AntiHebbian Network for Online Sparse Dictionary Learning Derived from Symmetric Matrix Factorization
Olshausen and Field (OF) proposed that neural computations in the primar...
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A Hebbian/AntiHebbian Network Derived from Online NonNegative Matrix Factorization Can Cluster and Discover Sparse Features
Despite our extensive knowledge of biophysical properties of neurons, th...
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A Hebbian/AntiHebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data
Neural network models of early sensory processing typically reduce the d...
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A Neuron as a Signal Processing Device
A neuron is a basic physiological and computational unit of the brain. W...
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Machine learning of hierarchical clustering to segment 2D and 3D images
We aim to improve segmentation through the use of machine learning tools...
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Online computation of sparse representations of time varying stimuli using a biologically motivated neural network
Natural stimuli are highly redundant, possessing significant spatial and...
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A network of spiking neurons for computing sparse representations in an energy efficient way
Computing sparse redundant representations is an important problem both ...
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Sparse LMS via Online Linearized Bregman Iteration
We propose a version of leastmeansquare (LMS) algorithm for sparse sys...
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Ocular dominance patterns in mammalian visual cortex: A wire length minimization approach
We propose a theory for ocular dominance (OD) patterns in mammalian prim...
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Dmitri B. Chklovskii
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Group Leader for Neuroscience, CCB, Flatiron Institute at Simons Foundation