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Synapses learn to utilize stochastic pre-synaptic release for the prediction of postsynaptic dynamics
by
Kappel, David
, Tetzlaff, Christian
in
Animals
/ Biology and Life Sciences
/ Brain
/ Cell membranes
/ Closed loops
/ Computational Biology
/ Computer and Information Sciences
/ Dynamics
/ Energy consumption
/ Feedback control
/ Humans
/ Learning
/ Learning - physiology
/ Long-term potentiation
/ Medicine and Health Sciences
/ Models, Neurological
/ Nerve Net - physiology
/ Neural networks
/ Neuronal Plasticity - physiology
/ Neurons
/ Neurons - metabolism
/ Neurons - physiology
/ Noise levels
/ Pattern classification
/ Physiological aspects
/ Social Sciences
/ Stochastic Processes
/ Synapses
/ Synapses - metabolism
/ Synapses - physiology
/ Synaptic Transmission - physiology
/ Uncertainty
2024
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Synapses learn to utilize stochastic pre-synaptic release for the prediction of postsynaptic dynamics
by
Kappel, David
, Tetzlaff, Christian
in
Animals
/ Biology and Life Sciences
/ Brain
/ Cell membranes
/ Closed loops
/ Computational Biology
/ Computer and Information Sciences
/ Dynamics
/ Energy consumption
/ Feedback control
/ Humans
/ Learning
/ Learning - physiology
/ Long-term potentiation
/ Medicine and Health Sciences
/ Models, Neurological
/ Nerve Net - physiology
/ Neural networks
/ Neuronal Plasticity - physiology
/ Neurons
/ Neurons - metabolism
/ Neurons - physiology
/ Noise levels
/ Pattern classification
/ Physiological aspects
/ Social Sciences
/ Stochastic Processes
/ Synapses
/ Synapses - metabolism
/ Synapses - physiology
/ Synaptic Transmission - physiology
/ Uncertainty
2024
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Synapses learn to utilize stochastic pre-synaptic release for the prediction of postsynaptic dynamics
by
Kappel, David
, Tetzlaff, Christian
in
Animals
/ Biology and Life Sciences
/ Brain
/ Cell membranes
/ Closed loops
/ Computational Biology
/ Computer and Information Sciences
/ Dynamics
/ Energy consumption
/ Feedback control
/ Humans
/ Learning
/ Learning - physiology
/ Long-term potentiation
/ Medicine and Health Sciences
/ Models, Neurological
/ Nerve Net - physiology
/ Neural networks
/ Neuronal Plasticity - physiology
/ Neurons
/ Neurons - metabolism
/ Neurons - physiology
/ Noise levels
/ Pattern classification
/ Physiological aspects
/ Social Sciences
/ Stochastic Processes
/ Synapses
/ Synapses - metabolism
/ Synapses - physiology
/ Synaptic Transmission - physiology
/ Uncertainty
2024
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Synapses learn to utilize stochastic pre-synaptic release for the prediction of postsynaptic dynamics
Journal Article
Synapses learn to utilize stochastic pre-synaptic release for the prediction of postsynaptic dynamics
2024
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Overview
Synapses in the brain are highly noisy, which leads to a large trial-by-trial variability. Given how costly synapses are in terms of energy consumption these high levels of noise are surprising. Here we propose that synapses use noise to represent uncertainties about the somatic activity of the postsynaptic neuron. To show this, we developed a mathematical framework, in which the synapse as a whole interacts with the soma of the postsynaptic neuron in a similar way to an agent that is situated and behaves in an uncertain, dynamic environment. This framework suggests that synapses use an implicit internal model of the somatic membrane dynamics that is being updated by a synaptic learning rule, which resembles experimentally well-established LTP/LTD mechanisms. In addition, this approach entails that a synapse utilizes its inherently noisy synaptic release to also encode its uncertainty about the state of the somatic potential. Although each synapse strives for predicting the somatic dynamics of its postsynaptic neuron, we show that the emergent dynamics of many synapses in a neuronal network resolve different learning problems such as pattern classification or closed-loop control in a dynamic environment. Hereby, synapses coordinate themselves to represent and utilize uncertainties on the network level in behaviorally ambiguous situations.
Publisher
Public Library of Science,Public Library of Science (PLoS)
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