Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity
Abstract
Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in exp...Continue Reading
References
Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning
Citations
Software Mentioned
Related Concepts
Related Feeds
Cell Checkpoints & Regulators
Cell cycle checkpoints are a series of complex checkpoint mechanisms that detect DNA abnormalities and ensure that DNA replication and repair are complete before cell division. They are primarily regulated by cyclins, cyclin-dependent kinases, and the anaphase-promoting complex/cyclosome. Here is the latest research.