Simulation parameters.

<div><p>Theoretical neuroscientists and machine learning researchers have proposed a variety of learning rules to enable artificial neural networks to effectively perform both supervised and unsupervised learning tasks. It is not always clear, however, how these theoretically-derived rul...

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Main Author:	Toviah Moldwin (10866708) (author)
Other Authors:	Li Shay Azran (20636768) (author), Idan Segev (32620) (author)
Published:	2025
Subjects:	Biophysics Cell Biology Neuroscience Physiology Science Policy Infectious Diseases Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified provide supervisory signals like neuron model derived rules relate calcium control hypothesis machine learning researchers relates synaptic plasticity implement homeostatic plasticity different rules might simple neuron model div >< p unsupervised learning tasks theoretical learning algorithms learning rules unsupervised recognition theoretical neuroscientists different contexts calcium source calcium influx shot learning plasticity thresholds plasticity protocols dependent plasticity wide range synapses ), synapse ), study shows study bridges postsynaptic spike mechanistically implemented local </ inspired one heterosynaptic </ four sources excitatory synapse effectively perform diverse array dependent </ dendritic spines biological mechanisms biological counterparts always clear
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Simulation parameters.

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