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Found 11 papers, 5 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.
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.
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.
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.
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 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.
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 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.
