Long-Term Synaptic Plasticity Tunes the Gain of Information Channels through the Cerebellum Granular Layer

Abstract

A central hypothesis on brain functioning is that long-term potentiation (LTP) and depression (LTD) regulate the signals transfer function by modifying the efficacy of synaptic transmission. In the cerebellum, granule cells have been shown to control the gain of signals transmitted through the mossy fiber pathway by exploiting synaptic inhibition in the glomeruli. However, the way LTP and LTD control signal transformation at the single-cell level in the space, time and frequency domains remains unclear. Here, the impact of LTP and LTD on incoming activity patterns was analyzed by combining patch-clamp recordings in acute cerebellar slices and mathematical modeling. LTP reduced the delay, increased the gain and broadened the frequency bandwidth of mossy fiber burst transmission, while LTD caused opposite changes. These properties, by exploiting NMDA subthreshold integration, emerged from microscopic changes in spike generation in individual granule cells such that LTP anticipated the emission of spikes and increased their number and precision, while LTD sorted the opposite effects. Thus, akin with the expansion recoding process theoretically attributed to the cerebellum granular layer, LTP and LTD could implement selective filtering lines channeling information toward the molecular and Purkinje cell layers for further processing.

New clues for the role of cerebellum in schizophrenia and the associated cognitive impairment

“…This notion is in line with the dysmetria of thought theory, proposing that the cerebellum participates in cognitive operations by fine-tuning the timing and coordination of mental processes (Andreasen and Pierson, 2008;Yeganeh-Doost et al, 2011;Bernard and Mittal, 2015;Cao and Cannon, 2019;Schmahmann, 2019). Growing evidence suggests that cerebellar abnormalities play a central role in the pathophysiology of SZ ( Andreasen and Pierson, 2008;Yeganeh-Doost et al, 2011;Bernard and Mittal, 2015;Cao and Cannon, 2019) by influencing cortical processing (Andreasen and Pierson, 2008;Yeganeh-Doost et al, 2011;D'Mello et al, 2015;Mapelli J. et al, 2022).…”

“…GABA A R containing α6 subunits (α6GABAARs) are located at cerebellar GoCs-GrCs synapses and extra-synaptic sites, where they regulate the precision of inputs required for cerebellar timing (Mapelli et al, 2014;Mapelli J. et al, 2022). These receptors have an impact on motor activity and are involved in cognitive processing and adequate responses to external stimuli in the cerebellum, eventually implicated in CIAS (Lee et al, 2022).…”

Artificial cerebellum on FPGA: Realistic real-time cerebellar spiking neural network model capable of real-world adaptive motor control

“…The former has problems of instability in execution and difficulty to reconstruct the hardware design. Thus, it would be difficult to implement LTD/LTP of Purkinje cell, other synaptic plasticity discovered (Gao et al, 2012;Mapelli et al, 2022;Bonnan et al, 2023) and gap junctions (Ito, 2012;De Zeeuw et al, 2021) in the cerebellum. In addition, they are generally specialized for random networks or deep neural networks and are not necessarily the efficient implementation method for shallow neural networks such as that of the cerebellum.…”

Biologically plausible information propagation in a complementary metal-oxide semiconductor integrate-and-fire artificial neuron circuit with memristive synapses

“…Therefore, the amount of transferred information can be estimated by looking at the input-output relationship, which is computed by analyzing the stimulus patterns and the neural responses. This dependency can be formalized in several ways, like tuning [9,10], gain [11], and selectivity curves [12,13], and these approaches allow us to provide quantitative estimates of the information content independently from the neural code. The language employed by neurons to communicate can be cracked by adopting parameters of communication and information theory [14].…”