Cellular-resolution mapping uncovers spatial adaptive filtering at the rat cerebellum input stage

Abstract

Long-term synaptic plasticity is thought to provide the substrate for adaptive computation in brain circuits but very little is known about its spatiotemporal organization. Here, we combined multi-spot two-photon laser microscopy in rat cerebellar slices with realistic modeling to map the distribution of plasticity in multi-neuronal units of the cerebellar granular layer. The units, composed by ~300 neurons activated by ~50 mossy fiber glomeruli, showed long-term potentiation concentrated in the core and long-term depression in the periphery. This plasticity was effectively accounted for by an NMDA receptor and calcium-dependent induction rule and was regulated by the inhibitory Golgi cell loops. Long-term synaptic plasticity created effective spatial filters tuning the time-delay and gain of spike retransmission at the cerebellum input stage and provided a plausible basis for the spatiotemporal recoding of input spike patterns anticipated by the motor learning theory.

The effects of the general anesthetic sevoflurane on neurotransmission: an experimental and computational study

“…In conclusion, these results identify important changes in cerebellar granule cell synaptic activation and excitation in the presence of the general anesthetic, sevoflurane, implying that changes will reverberate on local computation in the granular layer 15 . In cascade, this will alter adaptive filtering at the input stage and reverberate onto the entire cerebellar network.…”

“…The cerebellar cortical circuit is an ideal benchmark for the analysis of the effects of anesthetics on neurotransmission since GrCs show the unique characteristic among neurons of having a low number of dendrites (4.6 on average 14 ), a very well detailed set of ionic channels and synaptic receptors, a compact electrotonic structure allowing stable electrophysiological recordings and the development of reliable computational models 15 .…”

Real-Time Simulation of a Cerebellar Scaffold Model on Graphics Processing Units

“…Moreover, multi-compartment neuron models, such as a PC model (De Schutter and Bower, 1994a , b ; Masoli et al, 2015 ; Masoli and D'Angelo, 2017 ), a GoC model (Solinas et al, 2007a , b ), a GrC model (Diwakar et al, 2009 ; Dover et al, 2016 ; Masoli et al, 2020 ), and IO models (Schweighofer et al, 1999 ; De Gruijl et al, 2012 ), will be integrated. Integrating these elaborated models would allow us investigate more detailed network dynamics including synaptic plasticity (Casali et al, 2020 ) as well as intracellular dynamics simultaneously.…”

Modeling Neurotransmission: Computational Tools to Investigate Neurological Disorders

“…A major limitation in abstract modeling derives from the difficulty to account for connections in the circuit architecture. Until advanced imaging methods will provide well-resolved data [ 116 , 117 ], the approach based on random connectivity is nowadays employed in abstract models to investigate how small changes in the connectivity matrix can lead to behavioral phenotypes. For example, random networks show epileptiform activity like in cultured neuronal networks [ 118 , 119 ] and network topology can be altered to reproduce non-physiological behaviors by acting on the number of connections [ 119 ].…”