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Article ; Online: Defending against cerebellar disease.

Shadmehr, Reza

2022 Volume 128, Issue 6, Page(s) 1466–1468

Abstract: A hedge fund billionaire's children are suffering from cerebellar disease. He invited a group of neuroscientists to plan a search for therapies. What resulted is the outline of an implantable neural emulator that might electronically replace the damaged ... ...

Abstract	A hedge fund billionaire's children are suffering from cerebellar disease. He invited a group of neuroscientists to plan a search for therapies. What resulted is the outline of an implantable neural emulator that might electronically replace the damaged part of the brain.
MeSH term(s)	Male ; Child ; Humans ; Purkinje Cells ; Cerebellum ; Cerebellar Diseases
Language	English
Publishing date	2022-11-09
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	80161-6
ISSN	1522-1598 ; 0022-3077
ISSN (online)	1522-1598
ISSN	0022-3077
DOI	10.1152/jn.00437.2022
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Article ; Online: Population coding in the cerebellum: a machine learning perspective.

Shadmehr, Reza

Journal of neurophysiology

2020 Volume 124, Issue 6, Page(s) 2022–2051

Abstract: The cere 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 ... ...

Abstract	The cere 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 with 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 cannot 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.
MeSH term(s)	Action Potentials/physiology ; Animals ; Cerebellar Nuclei/physiology ; Cerebellum/physiology ; Conditioning, Classical/physiology ; Eye Movements/physiology ; Humans ; Learning/physiology ; Machine Learning ; Motor Activity/physiology ; Purkinje Cells/physiology
Language	English
Publishing date	2020-10-28
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S. ; Review
ZDB-ID	80161-6
ISSN	1522-1598 ; 0022-3077
ISSN (online)	1522-1598
ISSN	0022-3077
DOI	10.1152/jn.00449.2020
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Article ; Online: How the cerebellum learns to build a sequence.

Shadmehr, Reza

eLife

2018 Volume 7

Abstract: Rabbits can learn the biological analogue of a simple recursive function by relying only on the neurons of the cerebellum. ...

Abstract	Rabbits can learn the biological analogue of a simple recursive function by relying only on the neurons of the cerebellum.
MeSH term(s)	Animals ; Cerebellum ; Learning ; Neurons ; Rabbits
Language	English
Publishing date	2018-08-23
Publishing country	England
Document type	Journal Article ; Comment
ZDB-ID	2687154-3
ISSN	2050-084X ; 2050-084X
ISSN (online)	2050-084X
ISSN	2050-084X
DOI	10.7554/eLife.40660
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Article ; Online: Motor Learning: A Cortical System for Adaptive Motor Control.

Shadmehr, Reza

Current biology : CB

2018 Volume 28, Issue 14, Page(s) R793–R795

Abstract: Neurons in various areas of the frontal and parietal lobes can be distinguished based on their preference for the direction of reach errors. Stimulation of these neurons corrects for those errors, uncovering a cortical system for adaptive motor control. ...

Abstract	Neurons in various areas of the frontal and parietal lobes can be distinguished based on their preference for the direction of reach errors. Stimulation of these neurons corrects for those errors, uncovering a cortical system for adaptive motor control.
MeSH term(s)	Neurons ; Parietal Lobe
Language	English
Publishing date	2018-07-24
Publishing country	England
Document type	Journal Article ; Comment
ZDB-ID	1071731-6
ISSN	1879-0445 ; 0960-9822
ISSN (online)	1879-0445
ISSN	0960-9822
DOI	10.1016/j.cub.2018.05.071
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Article ; Online: Slowing of Movements in Healthy Aging as a Rational Economic Response to an Elevated Effort Landscape.

Summerside, Erik M / Courter, Robert J / Shadmehr, Reza / Ahmed, Alaa A

The Journal of neuroscience : the official journal of the Society for Neuroscience

2024 Volume 44, Issue 15

Abstract: Why do we move slower as we grow older? The reward circuits of the brain, which tend to invigorate movements, decline with aging, raising the possibility that reduced vigor is due to the diminishing value that our brain assigns to movements. However, as ... ...

Abstract	Why do we move slower as we grow older? The reward circuits of the brain, which tend to invigorate movements, decline with aging, raising the possibility that reduced vigor is due to the diminishing value that our brain assigns to movements. However, as we grow older, it also becomes more effortful to make movements. Is age-related slowing principally a consequence of increased effort costs from the muscles, or reduced valuation of reward by the brain? Here, we first quantified the cost of reaching via metabolic energy expenditure in human participants (male and female), and found that older adults consumed more energy than the young at a given speed. Thus, movements are objectively more costly for older adults. Next, we observed that when reward increased, older adults, like the young, responded by initiating their movements earlier. Yet, unlike the young, they were unwilling to increase their movement speed. Was their reluctance to reach quicker for rewards due to the increased effort costs, or because they ascribed less value to the movement? Motivated by a mathematical model, we next made the young experience a component of aging by making their movements more effortful. Now the young responded to reward by reacting faster but chose not to increase their movement speed. This suggests that slower movements in older adults are partly driven by an adaptive response to an elevated effort landscape. Moving slower may be a rational economic response the brain is making to mitigate the elevated effort costs that accompany aging.
MeSH term(s)	Humans ; Male ; Female ; Aged ; Healthy Aging ; Movement/physiology ; Reward ; Hypokinesia ; Motivation ; Decision Making/physiology
Language	English
Publishing date	2024-04-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	604637-x
ISSN	1529-2401 ; 0270-6474
ISSN (online)	1529-2401
ISSN	0270-6474
DOI	10.1523/JNEUROSCI.1596-23.2024
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Article: A software tool for at-home measurement of sensorimotor adaptation.

Jang, Jihoon / Shadmehr, Reza / Albert, Scott T

bioRxiv : the preprint server for biology

2023

Abstract: Sensorimotor adaptation is traditionally studied in well-controlled laboratory settings with specialized equipment. However, recent public health concerns such as the COVID-19 pandemic, as well as a desire to recruit a more diverse study population, have ...

Abstract	Sensorimotor adaptation is traditionally studied in well-controlled laboratory settings with specialized equipment. However, recent public health concerns such as the COVID-19 pandemic, as well as a desire to recruit a more diverse study population, have led the motor control community to consider at-home study designs. At-home motor control experiments are still rare because of the requirement to write software that can be easily used by anyone on any platform. To this end, we developed software that runs locally on a personal computer. The software provides audiovisual instructions and measures the ability of the subject to control the cursor in the context of visuomotor perturbations. We tested the software on a group of at-home participants and asked whether the adaptation principles inferred from in-lab measurements were reproducible in the at-home setting. For example, we manipulated the perturbations to test whether there were changes in adaptation rates (savings and interference), whether adaptation was associated with multiple timescales of memory (spontaneous recovery), and whether we could selectively suppress subconscious learning (delayed feedback, perturbation variability) or explicit strategies (limited reaction time). We found remarkable similarity between in-lab and at-home behaviors across these experimental conditions. Thus, we developed a software tool that can be used by research teams with little or no programming experience to study mechanisms of adaptation in an at-home setting.
Language	English
Publishing date	2023-12-13
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.12.12.571359
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Article ; Online: Effects of reward and effort history on decision making and movement vigor during foraging.

Sukumar, Shruthi / Shadmehr, Reza / Ahmed, Alaa A

Journal of neurophysiology

2023 Volume 131, Issue 4, Page(s) 638–651

Abstract: During foraging, animals explore a site and harvest reward and then abandon that site and travel to the next opportunity. One aspect of this behavior involves decision making, and the other involves movement control. These two aspects of behavior may be ... ...

Abstract	During foraging, animals explore a site and harvest reward and then abandon that site and travel to the next opportunity. One aspect of this behavior involves decision making, and the other involves movement control. These two aspects of behavior may be linked via an underlying desire to maximize a single normative utility: the sum of all rewards acquired, minus all efforts expended, divided by time. According to this theory, the history of rewards, and not just its immediate availability, should dictate how long one should stay and harvest reward and how vigorously one should travel to the next opportunity. We tested this theory in a series of experiments in which humans used their hand to harvest tokens at a reward patch and then used their arm to reach toward another patch. After a history of high rewards, the subjects not only shortened their harvest duration but also moved more vigorously toward the next reward opportunity. In contrast, after a history of high effort they lengthened their harvest duration but reduced their movement vigor, reaching more slowly to the next reward site. Thus, a history of high reward or low effort biased decisions by promoting early abandonment of the reward site and biased movements by promoting vigor.
MeSH term(s)	Humans ; Reaction Time ; Reward ; Movement ; Hand ; Decision Making
Language	English
Publishing date	2023-12-06
Publishing country	United States
Document type	Journal Article
ZDB-ID	80161-6
ISSN	1522-1598 ; 0022-3077
ISSN (online)	1522-1598
ISSN	0022-3077
DOI	10.1152/jn.00092.2023
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Article ; Online: How the cerebellum learns to build a sequence

Reza Shadmehr

eLife, Vol

2018 Volume 7

Abstract: Rabbits can learn the biological analogue of a simple recursive function by relying only on the neurons of the cerebellum. ...

Abstract	Rabbits can learn the biological analogue of a simple recursive function by relying only on the neurons of the cerebellum.
Keywords	learning ; sequence learning ; cerebellum ; rabbit ; Medicine ; R ; Science ; Q ; Biology (General) ; QH301-705.5
Language	English
Publishing date	2018-08-01T00:00:00Z
Publisher	eLife Sciences Publications Ltd
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Learning to Predict and Control the Physics of Our Movements.

Shadmehr, Reza

The Journal of neuroscience : the official journal of the Society for Neuroscience

2017 Volume 37, Issue 7, Page(s) 1663–1671

Abstract: When we hold an object in our hand, the mass of the object alters the physics of our arm, changing the relationship between motor commands that our brain sends to our arm muscles and the resulting motion of our hand. If the object is unfamiliar to us, ... ...

Abstract	When we hold an object in our hand, the mass of the object alters the physics of our arm, changing the relationship between motor commands that our brain sends to our arm muscles and the resulting motion of our hand. If the object is unfamiliar to us, our first movement will exhibit an error, producing a trajectory that is different from the one we had intended. This experience of error initiates learning in our brain, making it so that on the very next attempt our motor commands partially compensate for the unfamiliar physics, resulting in smaller errors. With further practice, the compensation becomes more complete, and our brain forms a model that predicts the physics of the object. This model is a motor memory that frees us from having to relearn the physics the next time that we encounter the object. The mechanism by which the brain transforms sensory prediction errors into corrective motor commands is the basis for how we learn the physics of objects with which we interact. The cerebellum and the motor cortex appear to be critical for our ability to learn physics, allowing us to use tools that extend our capabilities, making us masters of our environment.
MeSH term(s)	Action Potentials/physiology ; Animals ; Brain/cytology ; Brain/diagnostic imaging ; Brain/physiology ; Hand/physiology ; Humans ; Learning/physiology ; Models, Neurological ; Movement/physiology ; Neuroimaging ; Neurons/physiology
Language	English
Publishing date	2017-02-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	604637-x
ISSN	1529-2401 ; 0270-6474
ISSN (online)	1529-2401
ISSN	0270-6474
DOI	10.1523/JNEUROSCI.1675-16.2016
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Article ; Online: Distinct neural circuits for control of movement vs. holding still.

Shadmehr, Reza

Journal of neurophysiology

2017 Volume 117, Issue 4, Page(s) 1431–1460

Abstract: In generating a point-to-point movement, the brain does more than produce the transient commands needed to move the body part; it also produces the sustained commands that are needed to hold the body part at its destination. In the oculomotor system, ... ...

Abstract	In generating a point-to-point movement, the brain does more than produce the transient commands needed to move the body part; it also produces the sustained commands that are needed to hold the body part at its destination. In the oculomotor system, these functions are mapped onto two distinct circuits: a premotor circuit that specializes in generating the transient activity that displaces the eyes and a "neural integrator" that transforms that transient input into sustained activity that holds the eyes. Different parts of the cerebellum adaptively control the motor commands during these two phases: the oculomotor vermis participates in fine tuning the transient neural signals that move the eyes, monitoring the activity of the premotor circuit via efference copy, whereas the flocculus participates in controlling the sustained neural signals that hold the eyes, monitoring the activity of the neural integrator. Here, I review the oculomotor literature and then ask whether this separation of control between moving and holding is a design principle that may be shared with other modalities of movement. To answer this question, I consider neurophysiological and psychophysical data in various species during control of head movements, arm movements, and locomotion, focusing on the brain stem, motor cortex, and hippocampus, respectively. The review of the data raises the possibility that across modalities of motor control, circuits that are responsible for producing commands that change the sensory state of a body part are distinct from those that produce commands that maintain that sensory state.
MeSH term(s)	Attention/physiology ; Brain/physiology ; Brain Mapping ; Humans ; Movement/physiology ; Neural Pathways/physiology ; Posture/physiology
Language	English
Publishing date	2017-01-04
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	80161-6
ISSN	1522-1598 ; 0022-3077
ISSN (online)	1522-1598
ISSN	0022-3077
DOI	10.1152/jn.00840.2016
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