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Found 37 papers, 10 papers with code
Neuronal Temporal Filters as Normal Mode Extractors
Such variation in the temporal filter with input SNR resembles that observed experimentally in biological neurons.
The Neuron as a Direct Data-Driven Controller
In the quest to model neuronal function amidst gaps in physiological data, a promising strategy is to develop a normative theory that interprets neuronal physiology as optimizing a computational objective.
Adaptive whitening with fast gain modulation and slow synaptic plasticity
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 their responses.
Unlocking the Potential of Similarity Matching: Scalability, Supervision and Pre-training
This study focuses on the primarily unsupervised similarity matching (SM) framework, which aligns with observed mechanisms in biological systems and offers online, localized, and biologically plausible algorithms.
Duality Principle and Biologically Plausible Learning: Connecting the Representer Theorem and Hebbian Learning
In this work, we focus not on developing new algorithms but on showing that the Representer theorem offers the perfect lens to study biologically plausible learning algorithms.
Normative framework for deriving neural networks with multi-compartmental neurons and non-Hebbian plasticity
These NN models account for many anatomical and physiological observations; however, the objectives have limited computational power and the derived NNs do not explain multi-compartmental neuronal structures and non-Hebbian forms of plasticity that are prevalent throughout the brain.
Adaptive whitening in neural populations with gain-modulating interneurons
Statistical whitening transformations play a fundamental role in many computational systems, and may also play an important role in biological sensory systems.
An online algorithm for contrastive Principal Component Analysis
However, the performance of cPCA is sensitive to hyper-parameter choice and there is currently no online algorithm for implementing cPCA.
Constrained Predictive Coding as a Biologically Plausible Model of the Cortical Hierarchy
The network we derive does not involve one-to-one connectivity or signal multiplexing, which the phenomenological models required, indicating that these features are not necessary for learning in the cortex.
Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation
To this end, we consider two mathematically tractable recurrent linear neural networks that statistically whiten their inputs -- one with direct recurrent connections and the other with interneurons that mediate recurrent communication.
Bridging the Gap: Point Clouds for Merging Neurons in Connectomics
To address this problem, we propose a novel method based on point cloud representations of neurons.
Neural optimal feedback control with local learning rules
This network performs system identification and Kalman filtering, without the need for multiple phases with distinct update rules or the knowledge of the noise covariances.
Neural circuits for dynamics-based segmentation of time series
In particular, the segmentation accuracy is similar to that obtained from oracle-like methods in which the ground-truth parameters of the autoregressive models are known.
A Neural Network with Local Learning Rules for Minor Subspace Analysis
However, no biologically plausible networks exist for minor subspace analysis (MSA), a fundamental signal processing task.
A Similarity-preserving Neural Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit
Unfortunately, it is difficult to map their model onto a biologically plausible neural network (NN) with local learning rules.
A biologically plausible neural network for local supervision in cortical microcircuits
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 of brain function.
Biologically plausible single-layer networks for nonnegative independent component analysis
To model how the brain performs this task, we seek a biologically plausible single-layer neural network implementation of a blind source separation algorithm.
A biologically plausible neural network for Slow Feature Analysis
Furthermore, when trained on naturalistic stimuli, SFA reproduces interesting properties of cells in the primary visual cortex and hippocampus, suggesting that the brain uses temporal slowness as a computational principle for learning latent features.
A simple normative network approximates local non-Hebbian learning in the cortex
Here, adopting a normative approach, we model these instructive signals as supervisory inputs guiding the projection of the feedforward data.
A biologically plausible neural network for multi-channel Canonical Correlation Analysis
For biological plausibility, we require that the network operates in the online setting and its synaptic update rules are local.
Neuroscience-inspired online unsupervised learning algorithms
Although the currently popular deep learning networks achieve unprecedented performance on some tasks, the human brain still has a monopoly on general intelligence.
Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching
Big data problems frequently require processing datasets in a streaming fashion, either because all data are available at once but collectively are larger than available memory or because the data intrinsically arrive one data point at a time and must be processed online.
Blind nonnegative source separation using biological neural networks
Blind source separation, i. e. extraction of independent sources from a mixture, is an important problem for both artificial and natural signal processing.
Why do similarity matching objectives lead to Hebbian/anti-Hebbian networks?
Modeling self-organization of neural networks for unsupervised learning using Hebbian and anti-Hebbian plasticity has a long history in neuroscience.
Self-calibrating Neural Networks for Dimensionality Reduction
Here we propose online algorithms where the threshold is self-calibrating based on the singular values computed from the existing observations.
Optimization theory of Hebbian/anti-Hebbian networks for PCA and whitening
Here, we focus on such workhorses of signal processing as Principal Component Analysis (PCA) and whitening which maximize information transmission in the presence of noise.
A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks
Here, we derive biologically plausible dimensionality reduction algorithms which adapt the number of output dimensions to the eigenspectrum of the input covariance matrix.
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, there is no commonly accepted algorithmic theory of neuronal function.
A Hebbian/Anti-Hebbian Network for Online Sparse Dictionary Learning Derived from Symmetric Matrix Factorization
Here, to overcome this problem, we derive sparse dictionary learning from a novel cost-function - a regularized error of the symmetric factorization of the input's similarity matrix.
A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data
Such networks learn the principal subspace, in the sense of principal component analysis (PCA), by adjusting synaptic weights according to activity-dependent learning rules.
A Neuron as a Signal Processing Device
Here we propose to view a neuron as a signal processing device that represents the incoming streaming data matrix as a sparse vector of synaptic weights scaled by an outgoing sparse activity vector.
Machine learning of hierarchical clustering to segment 2D and 3D images
We aim to improve segmentation through the use of machine learning tools during region agglomeration.
A mechanistic model of early sensory processing based on subtracting sparse representations
However, feedback inhibitory circuits are common in early sensory circuits and furthermore their dynamics may be nonlinear.
A lattice filter model of the visual pathway
Early stages of visual processing are thought to decorrelate, or whiten, the incoming temporally varying signals.
Neuronal Spike Generation Mechanism as an Oversampling, Noise-shaping A-to-D converter
If noise-shaping were used in neurons, it would introduce correlations in spike timing to reduce low-frequency (up to Nyquist) transmission error at the cost of high-frequency one (from Nyquist to sampling rate).
Over-complete representations on recurrent neural networks can support persistent percepts
A striking aspect of cortical neural networks is the divergence of a relatively small number of input channels from the peripheral sensory apparatus into a large number of cortical neurons, an over-complete representation strategy.
Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME)
One of the central problems in neuroscience is reconstructing synaptic connectivity in neural circuits.
