Cerebellar Purkinje cells control eye movements with a rapid rate code that is invariant to spike irregularity

Payne, Hannah L.; French, Ranran L; Guo, Christine C.; Nguyen-Vu, TD Barbara; Manninen, Tiina; Raymond, Jennifer L.

doi:10.7554/elife.37102

Cited by 45 publications

(47 citation statements)

References 160 publications

(288 reference statements)

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“…However, it is unclear whether in the intact animal, control of DCN output relies on synchrony of simple spikes. Experimental results and simulations of Payne et al (2019) suggest that whereas the mean rate of simple spikes is critical for control of eye velocity during a pursuit task, the timing of individual spikes plays a less significant role. Irregular simple spike timing of a population of P-cells, though produced synchronously through optogenetic stimulation, did not result in eye velocities that were different than expected from the averaged firing rates of the P-cells.…”

Section: Discussionmentioning

confidence: 99%

Population coding in the cerebellum: a machine learning perspective

Shadmehr¹

2020

Journal of Neurophysiology

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The cerebellum resembles a feedforward, three-layer network of neurons in which the "hidden layer" consists of Purkinje cells (P-cells), and the output layer consists of deep cerebellar nucleus (DCN) neurons. In this analogy, the output of each DCN neuron is a prediction that is compared to the actual observation, resulting in an error signal that originates in the inferior olive. Efficient learning requires that the error signal reach the DCN neurons, as well as the P-cells that project onto them. However, this basic rule of learning is violated in the cerebellum: the olivary projections to the DCN are weak, particularly in adulthood. Instead, an extraordinarily strong signal is sent from the olive to the P-cells, producing complex spikes. Curiously, P-cells are grouped into small populations that converge onto single DCN neurons. Why are the P-cells organized in this way, and what is the membership criterion of each population? Here, I apply elementary mathematics from machine learning and consider the fact that P-cells that form a population exhibit a special property: they can synchronize their complex spikes, which in turn suppress activity of DCN neuron they project to. Thus, complex spikes can not only act as a teaching signal for a P-cell, but through complex spike synchrony a P-cell population may act as a surrogate teacher for the DCN neuron that produced the erroneous output. It appears that grouping of P-cells into small populations that share a preference for error satisfies a critical requirement of efficient learning: providing error information to the output layer neuron (DCN) that was responsible for the error, as well as the hidden layer neurons (P-cells) that contributed to it. This population coding may account for several remarkable features of behavior during learning, including multiple timescales, protection from erasure, and spontaneous recovery of memory.

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Section: Discussionmentioning

confidence: 99%

Population coding in the cerebellum: a machine learning perspective

Shadmehr¹

2020

Journal of Neurophysiology

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“…3, 4). Although the debate remains, a recent study meticulously shows the firing rates, but not the temporal patterns, of individual PNs dictate cerebellum-controlled movements [69]. Further inquiries are needed to answer whether a global change in the speed and/or a specific pattern of PN activity is crucial for the information processing in social behaviors.…”

Section: Discussionmentioning

confidence: 99%

Targeting inhibitory cerebellar circuitry to alleviate behavioral deficits in a mouse model for studying idiopathic autism

Chao

Velasco

Pathak

et al. 2020

Neuropsychopharmacol.

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Autism spectrum disorder (ASD) encompasses wide-ranging neuropsychiatric symptoms with unclear etiology. Although the cerebellum is a key region implicated in ASD, it remains elusive how the cerebellar circuitry is altered and whether the cerebellum can serve as a therapeutic target to rectify the phenotype of idiopathic ASD with polygenic abnormalities. Using a syndromic ASD model, e.g., Black and Tan BRachyury T + Itpr3 tf /J (BTBR) mice, we revealed that increased excitability of presynaptic interneurons (INs) and decreased intrinsic excitability of postsynaptic Purkinje neurons (PNs) resulted in low PN firing rates in the cerebellum. Knowing that downregulation of Kv1.2 potassium channel in the IN nerve terminals likely augmented their excitability and GABA release, we applied a positive Kv1.2 modulator to mitigate the presynaptic over-inhibition and social impairment of BTBR mice. Selective restoration of the PN activity by a new chemogenetic approach alleviated core ASD-like behaviors of the BTBR strain. These findings highlight complex mechanisms converging onto the cerebellar dysfunction in the phenotypic model and provide effective strategies for potential therapies of ASD.

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“…Nevertheless, optogenetics proved to be a valuable tool for studies in monkeys, not only in rodents. Indeed, an even more recent study used a similar approach to investigate the role of PC firing irregularity in cerebellar functions of rhesus monkeys [50]. It has been suggested, both for mice and monkeys, that the irregularity of interspike intervals in PCs might play a role in the information transfer from the cerebellar cortex to DCN.…”

Section: Eye Movements In Monkeysmentioning

confidence: 99%

The Optogenetic Revolution in Cerebellar Investigations

Prestori

Montagna

D’Angelo

et al. 2020

IJMS

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The cerebellum is most renowned for its role in sensorimotor control and coordination, but a growing number of anatomical and physiological studies are demonstrating its deep involvement in cognitive and emotional functions. Recently, the development and refinement of optogenetic techniques boosted research in the cerebellar field and, impressively, revolutionized the methodological approach and endowed the investigations with entirely new capabilities. This translated into a significant improvement in the data acquired for sensorimotor tests, allowing one to correlate single-cell activity with motor behavior to the extent of determining the role of single neuronal types and single connection pathways in controlling precise aspects of movement kinematics. These levels of specificity in correlating neuronal activity to behavior could not be achieved in the past, when electrical and pharmacological stimulations were the only available experimental tools. The application of optogenetics to the investigation of the cerebellar role in higher-order and cognitive functions, which involves a high degree of connectivity with multiple brain areas, has been even more significant. It is possible that, in this field, optogenetics has changed the game, and the number of investigations using optogenetics to study the cerebellar role in non-sensorimotor functions in awake animals is growing. The main issues addressed by these studies are the cerebellar role in epilepsy (through connections to the hippocampus and the temporal lobe), schizophrenia and cognition, working memory for decision making, and social behavior. It is also worth noting that optogenetics opened a new perspective for cerebellar neurostimulation in patients (e.g., for epilepsy treatment and stroke rehabilitation), promising unprecedented specificity in the targeted pathways that could be either activated or inhibited.

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Cerebellar Purkinje cells control eye movements with a rapid rate code that is invariant to spike irregularity

Cited by 45 publications

References 160 publications

Population coding in the cerebellum: a machine learning perspective

Population coding in the cerebellum: a machine learning perspective

Targeting inhibitory cerebellar circuitry to alleviate behavioral deficits in a mouse model for studying idiopathic autism

The Optogenetic Revolution in Cerebellar Investigations

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