Revisiting a theory of cerebellar cortex

Yamazaki, Tadashi; Lennon, William C.

doi:10.1016/j.neures.2019.03.001

Cited by 26 publications

(26 citation statements)

References 61 publications

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“…A number of previous studies also showed that CF activities contain information about directional errors in reaching but they didn't tell an agent how to correct the errors explicitly in terms of joint torques or muscle contractions [18], [20]. In this sense, CF is believed to convey evaluative feedback and cerebellar cortex as a RL machine.…”

Section: A Cerebellar Model Architecturementioning

confidence: 95%

“…Using mechanistic model, they predicted that cerebellar damage indirectly impaired the RL capability by increasing motor noise. Recently, Yamazaki et al [18] extended the Marr-Albus-Ito model and implemented the cerebellar circuit with RL algorithm successfully. The major limitation of this approach is that there is no direct experimental evidence or rigorous mathematical justification on the feedback inhibition term which is assumed to be fed by the molecular layer interneurons (MLIs).…”

Section: Introductionmentioning

confidence: 99%

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A Biologically Constrained Cerebellar Model With Reinforcement Learning for Robotic Limb Control

et al. 2020

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Section: A Cerebellar Model Architecturementioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

A Biologically Constrained Cerebellar Model With Reinforcement Learning for Robotic Limb Control

et al. 2020

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“…Although the present model is built based on known electrophysiological and anatomical data (Yamazaki and Nagao, 2012) and the size is unprecedented, several important features are missing. In the cerebellum, the plasticity at parallel fiber-Purkinje cell synapses plays prominent roles (Marr, 1969;Albus, 1971;Ito, 2001;Ito et al, 2014;Yamazaki et al, 2015;Yamazaki and Lennon, 2019). Moreover, there are various forms of synaptic plasticity distributed within the cerebellum (D'Angelo, 2014).…”

Section: Current Limitations and Future Extensionsmentioning

confidence: 99%

“…Particularly, the cerebral cortex, cerebellum, and basal ganglia are considered unsupervised, supervised, and reinforcement learning system, respectively (Doya, 1999(Doya, , 2000. Recently, Yamazaki and Lennon (2019) have proposed that the cerebellum is a reinforcement learning machine. These studies suggest that multiple learning systems (supervised and reinforcement learning systems) are driven in parallel in the cerebellum.…”

Section: Toward Building a Whole-brain Network Modelmentioning

confidence: 99%

Simulation of a Human-Scale Cerebellar Network Model on the K Computer

Yamaura

Igarashi

Yamazaki

2020

Front. Neuroinform.

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Computer simulation of the human brain at an individual neuron resolution is an ultimate goal of computational neuroscience. The Japanese flagship supercomputer, K, provides unprecedented computational capability toward this goal. The cerebellum contains 80% of the neurons in the whole brain. Therefore, computer simulation of the human-scale cerebellum will be a challenge for modern supercomputers. In this study, we built a human-scale spiking network model of the cerebellum, composed of 68 billion spiking neurons, on the K computer. As a benchmark, we performed a computer simulation of a cerebellum-dependent eye movement task known as the optokinetic response. We succeeded in reproducing plausible neuronal activity patterns that are observed experimentally in animals. The model was built on dedicated neural network simulation software called MONET (Millefeuille-like Organization NEural neTwork), which calculates layered sheet types of neural networks with parallelization by tile partitioning. To examine the scalability of the MONET simulator, we repeatedly performed simulations while changing the number of compute nodes from 1,024 to 82,944 and measured the computational time. We observed a good weak-scaling property for our cerebellar network model. Using all 82,944 nodes, we succeeded in simulating a human-scale cerebellum for the first time, although the simulation was 578 times slower than the wall clock time. These results suggest that the K computer is already capable of creating a simulation of a human-scale cerebellar model with the aid of the MONET simulator.

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“…These in turn determine the specificity of the cerebellar nuclei output. “What the beam passes on to the cerebellar nuclei is a sequence of signals produced by selected Purkinje cells at times specified by the moving wave of excitation.” Particularly in the sensory domain, different experimental models were instrumental in depicting theories on cellular mechanisms for prediction of sensory events (Mauk and Ohyama, 2004; D’Angelo and Casali, 2013; Yamazaki and Lennon, 2019).…”

Section: Introductionmentioning

confidence: 99%

The Implementation of Predictions During Sequencing

Molinari

Masciullo

2019

Front. Cell. Neurosci.

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Optimal control mechanisms require prediction capabilities. If one cannot predict the consequences of a motor act or behavior, one will continually collide with walls or become a social pariah. “Looking into the future” is thus one of the most important prerequisites for smooth movements and social interactions. To achieve this goal, the brain must constantly predict future events. This principle applies to all domains of information processing, including motor and cognitive control, as well as the development of decision-making skills, theory of mind, and virtually all cognitive processes. Sequencing is suggested to support the predictive capacity of the brain. To recognize that events are related, the brain must discover links among them in the spatiotemporal domain. To achieve this, the brain must often hold one event in working memory and compare it to a second one, and the characteristics of the two must be compared and correctly placed in space and time. Among the different brain structures involved in sequencing, the cerebellum has been proposed to have a central function. We have suggested that the operational mode of the cerebellum is based on “sequence detection” and that this process is crucial for prediction. Patterns of temporally or spatially structured events are conveyed to the cerebellum via the pontine nuclei and compared with actual ones conveyed through the climbing fibers olivary inputs. Through this interaction, data on previously encountered sequences can be obtained and used to generate internal models from which predictions can be made. This mechanism would allow the cerebellum not only to recognize sequences but also to detect sequence violations. Cerebellar pattern detection and prediction would thus be a means to allow feedforward control based on anticipation. We will argue that cerebellar sequencing allows implementation of prediction by setting the correct excitatory levels in defined brain areas to implement the adaptive response for a given pattern of stimuli that embeds sufficient information to be recognized as a previously encountered template. Here, we will discuss results from human and animal studies and correlate them with the present understanding of cerebellar function in cognition and behavior.

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Revisiting a theory of cerebellar cortex

Cited by 26 publications

References 61 publications

A Biologically Constrained Cerebellar Model With Reinforcement Learning for Robotic Limb Control

A Biologically Constrained Cerebellar Model With Reinforcement Learning for Robotic Limb Control

Simulation of a Human-Scale Cerebellar Network Model on the K Computer

The Implementation of Predictions During Sequencing

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