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Found 5 papers, 2 papers with code
How connectivity structure shapes rich and lazy learning in neural circuits
Through both empirical and theoretical analyses, we discover that high-rank initializations typically yield smaller network changes indicative of lazier learning, a finding we also confirm with experimentally-driven initial connectivity in recurrent neural networks.
Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators
Finally, we derive an in-silico implementation of ModProp that could serve as a low-complexity and causal alternative to backpropagation through time.
Does the neuronal noise in cortex help generalization?
Neural activity is highly variable in response to repeated stimuli.
Convolutional neural networks with extra-classical receptive fields
To address these issues, we combine traditional supervised learning via backpropagation with a specialized unsupervised learning rule to learn lateral connections between neurons within a convolutional neural network.
High resolution neural connectivity from incomplete tracing data using nonnegative spline regression
We demonstrate the efficacy of a low rank version on visual cortex data and discuss the possibility of extending this to a whole-brain connectivity matrix at the voxel scale.
