ELL Medium Ganglion cell (Muller et al 2019)

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"Distributing learning across multiple layers has proven extremely powerful in artificial neural networks. However, little is known about how multi-layer learning is implemented in the brain. Here, we provide an account of learning across multiple processing layers in the electrosensory lobe (ELL) of mormyrid fish and report how it solves problems well known from machine learning. Because the ELL operates and learns continuously, it must reconcile learning and signaling functions without switching its mode of operation. We show that this is accomplished through a functional compartmentalization within intermediate layer neurons in which inputs driving learning differentially affect dendritic and axonal spikes. We also find that connectivity based on learning rather than sensory response selectivity assures that plasticity at synapses onto intermediate-layer neurons is matched to the requirements of output neurons. The mechanisms we uncover have relevance to learning in the cerebellum, hippocampus, and cerebral cortex, as well as in artificial systems."
1 . Muller SZ, Zadina AN, Abbott LF, Sawtell NB (2019) Continual Learning in a Multi-Layer Network of an Electric Fish. Cell [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Axon; Dendrite; Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): ELL Medium Ganglion cell;
Gap Junctions:
Transmitter(s): Gaba; Glutamate;
Simulation Environment: Python; NEURON;
Model Concept(s):
Implementer(s): Muller, Salomon Z [szm2106 at columbia.edu];
Search NeuronDB for information about:  Gaba; Glutamate;
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