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Found 14 papers, 2 papers with code
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.
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.
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.
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.
Spatial Transformer K-Means
This enables (i) the reduction of intrinsic nuisances associated with the data, reducing the complexity of the clustering task and increasing performances and producing state-of-the-art results, (ii) clustering in the input space of the data, leading to a fully interpretable clustering algorithm, and (iii) the benefit of convergence guarantees.
A Normative and Biologically Plausible Algorithm for Independent Component Analysis
The brain effortlessly solves blind source separation (BSS) problems, but the algorithm it uses remains elusive.
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.
Interpretable Image Clustering via Diffeomorphism-Aware K-Means
We design an interpretable clustering algorithm aware of the nonlinear structure of image manifolds.
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.
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.
A Similarity-preserving 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.
Online Representation Learning with Single and Multi-layer Hebbian Networks for Image Classification
Recently, a new class of Hebbian-like and local unsupervised learning rules for neural networks have been developed that minimise a similarity matching cost-function.
