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Found 12 papers, 4 papers with code
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.
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.
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 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.
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 Neural Network for Semi-Supervised Learning on Manifolds
Here, we propose a feed-forward neural network capable of semi-supervised learning on manifolds without using an explicit graph representation.
Clustering is semidefinitely not that hard: Nonnegative SDP for manifold disentangling
In solving hard computational problems, semidefinite program (SDP) relaxations often play an important role because they come with a guarantee of optimality.
