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08/25/2023
Adaptive whitening with fast gain modulation and slow synaptic plasticity
Neurons in early sensory areas rapidly adapt to changing sensory statist...
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08/02/2023
Unlocking the Potential of Similarity Matching: Scalability, Supervision and Pre-training
While effective, the backpropagation (BP) algorithm exhibits limitations...
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02/20/2023
A normative framework for deriving neural networks with multi-compartmental neurons and non-Hebbian plasticity
An established normative approach for understanding the algorithmic basi...
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01/27/2023
Statistical whitening of neural populations with gain-modulating interneurons
Statistical whitening transformations play a fundamental role in many co...
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11/14/2022
An online algorithm for contrastive Principal Component Analysis
Finding informative low-dimensional representations that can be computed...
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09/21/2022
Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation
Early sensory systems in the brain rapidly adapt to fluctuating input st...
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12/03/2021
Bridging the Gap: Point Clouds for Merging Neurons in Connectomics
In the field of Connectomics, a primary problem is that of 3D neuron seg...
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11/17/2021
A Normative and Biologically Plausible Algorithm for Independent Component Analysis
The brain effortlessly solves blind source separation (BSS) problems, bu...
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11/12/2021
Neural optimal feedback control with local learning rules
A major problem in motor control is understanding how the brain plans an...
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02/10/2021
A Similarity-preserving Neural Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit
Learning to detect content-independent transformations from data is one ...
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02/10/2021
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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11/30/2020
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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10/23/2020
A simple normative network approximates local non-Hebbian learning in the cortex
To guide behavior, the brain extracts relevant features from high-dimens...
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10/23/2020
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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10/23/2020
Bio-NICA: A biologically inspired single-layer network for Nonnegative Independent Component Analysis
Blind source separation, the problem of separating mixtures of unknown s...
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10/01/2020
A biologically plausible neural network for multi-channel Canonical Correlation Analysis
Cortical pyramidal neurons receive inputs from multiple distinct neural ...
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08/05/2019
Neuroscience-inspired online unsupervised learning algorithms
Although the currently popular deep learning networks achieve unpreceden...
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10/16/2018
Biologically Plausible Online Principal Component Analysis Without Recurrent Neural Dynamics
Artificial neural networks that learn to perform Principal Component Ana...
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08/06/2018
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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06/01/2017
Blind nonnegative source separation using biological neural networks
Blind source separation, i.e. extraction of independent sources from a m...
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03/23/2017
Why do similarity matching objectives lead to Hebbian/anti-Hebbian networks?
Modeling self-organization of neural networks for unsupervised learning ...
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12/11/2016
Self-calibrating Neural Networks for Dimensionality Reduction
Recently, a novel family of biologically plausible online algorithms for...
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11/30/2015
Optimization theory of Hebbian/anti-Hebbian networks for PCA and whitening
In analyzing information streamed by sensory organs, our brains face cha...
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11/30/2015
A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks
To make sense of the world our brains must analyze high-dimensional data...
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03/02/2015
A Hebbian/Anti-Hebbian 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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03/02/2015
A Hebbian/Anti-Hebbian Network Derived from Online Non-Negative Matrix Factorization Can Cluster and Discover Sparse Features
Despite our extensive knowledge of biophysical properties of neurons, th...
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03/02/2015
A Hebbian/Anti-Hebbian 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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05/12/2014
A Neuron as a Signal Processing Device
A neuron is a basic physiological and computational unit of the brain. W...
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03/25/2013
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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10/13/2012
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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10/04/2012
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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10/01/2012
Sparse LMS via Online Linearized Bregman Iteration
We propose a version of least-mean-square (LMS) algorithm for sparse sys...
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06/14/1999
