Interacting synapses stabilise both learning and neuronal dynamics in biological networks

Agnes, Everton J. and Vogels, Tim P.. (2021) Interacting synapses stabilise both learning and neuronal dynamics in biological networks.

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Official URL: https://edoc.unibas.ch/85031/

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Distinct synapses influence one another when they undergo changes, with unclear consequences for neuronal dynamics and function. Here we show that synapses can interact such that excitatory currents are naturally normalised and balanced by inhibitory inputs. This happens when classical spike-timing dependent synaptic plasticity rules are extended by additional mechanisms that incorporate the influence of neighbouring synaptic currents and regulate the amplitude of their efficacy changes accordingly. The resulting control of excitatory plasticity by inhibitory activation, and vice versa, gives rise to quick and long-lasting memories as seen experimentally in receptive field plasticity paradigms. In models with additional dendritic structure, we observe experimentally reported clustering of co-active synapses that depends on initial connectivity and morphology. Finally, in recurrent neuronal networks, rich and stable dynamics with high input sensitivity emerge, providing transient activity that resembles recordings from motor cortex. Our model provides a general framework for codependent plasticity that frames individual synaptic modifications in the context of population-wide changes, allowing us to connect micro-level physiology with network-wide phenomena.
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Neurobiology > Theoretical and computational neuroscience (Agnes)
UniBasel Contributors:Agnes, Everton Joao
Item Type:Preprint
Number of Pages:19
Note:Publication type according to Uni Basel Research Database: Discussion paper / Internet publication
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Last Modified:10 Nov 2021 07:44
Deposited On:10 Nov 2021 07:44

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