Climbing fiber inputs to Purkinje cells are thought to be involved in generating the instructive signals that drive cerebellar learning. To investigate how these instructive signals are encoded, we ...recorded the activity of individual climbing fibers during cerebellum-dependent eyeblink conditioning in mice. We found that climbing fibers signaled both the unexpected delivery and the unexpected omission of the periocular airpuff that served as the instructive signal for eyeblink conditioning. In addition, we observed that climbing fibers activated by periocular airpuffs also responded to stimuli from other sensory modalities if those stimuli were novel or if they predicted that the periocular airpuff was about to be presented. This pattern of climbing fiber activity is markedly similar to the responses of dopamine neurons during reinforcement learning, which have been shown to encode a particular type of instructive signal known as a temporal difference prediction error.
Supervised learning plays a key role in the operation of many biological and artificial neural networks. Analysis of the computations underlying supervised learning is facilitated by the relatively ...simple and uniform architecture of the cerebellum, a brain area that supports numerous motor, sensory, and cognitive functions. We highlight recent discoveries indicating that the cerebellum implements supervised learning using the following organizational principles: (
a
) extensive preprocessing of input representations (i.e., feature engineering), (
b
) massively recurrent circuit architecture, (
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) linear input-output computations, (
d
) sophisticated instructive signals that can be regulated and are predictive, (
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) adaptive mechanisms of plasticity with multiple timescales, and (
f
) task-specific hardware specializations. The principles emerging from studies of the cerebellum have striking parallels with those in other brain areas and in artificial neural networks, as well as some notable differences, which can inform future research on supervised learning and inspire next-generation machine-based algorithms.
Highlights ► The activity of Purkinje cells changes during sensori-motor calibration. ► Purkinje cell activity may be used to estimate future sensory or motor events. ► Purkinje cell activity may be ...used as an instructive signal. ► Purkinje cell activity may be used to modify the motor command. ► Purkinje cells switch modes of operation at various stages of the calibration process.
Premise
Plants synthesize information from multiple environmental stimuli when determining their direction of growth. Gravity, being ubiquitous on Earth, plays a major role in determining the ...direction of growth and overall architecture of the plant. Here, we utilized the microgravity environment on board the International Space Station (ISS) to identify genes involved influencing growth and development of phototropically stimulated seedlings of Arabidopsis thaliana.
Methods
Seedlings were grown on the ISS, and RNA was extracted from 7 samples (pools of 10–15 plants) grown in microgravity (μg) or Earth gravity conditions (1‐g). Transcriptomic analyses via RNA sequencing (RNA‐seq) of differential gene expression was performed using the HISAT2‐Stringtie‐DESeq2 RNASeq pipeline. Differentially expressed genes were further characterized by using Pathway Analysis and enrichment for Gene Ontology classifications.
Results
For 296 genes that were found significantly differentially expressed between plants in microgravity compared to 1‐g controls, Pathway Analysis identified eight molecular pathways that were significantly affected by reduced gravity conditions. Specifically, light‐associated pathways (e.g., photosynthesis‐antenna proteins, photosynthesis, porphyrin, and chlorophyll metabolism) were significantly downregulated in microgravity.
Conclusions
Gene expression in A. thaliana seedlings grown in microgravity was significantly altered compared to that of the 1‐g control. Understanding how plants grow in conditions of microgravity not only aids in our understanding of how plants grow and respond to the environment but will also help to efficiently grow plants during long‐range space missions.
Purkinje cells (PCs) of the cerebellar cortex are necessary for controlling movement with precision, but a mechanistic explanation of how the activity of these inhibitory neurons regulates motor ...output is still lacking. We used an optogenetic approach in awake mice to show for the first time that transiently suppressing spontaneous activity in a population of PCs is sufficient to cause discrete movements that can be systematically modulated in size, speed, and timing depending on how much and how long PC firing is suppressed. We further demonstrate that this fine control of movement kinematics is mediated by a graded disinhibition of target neurons in the deep cerebellar nuclei. Our results prove a long-standing model of cerebellar function and provide the first demonstration that suppression of inhibitory signals can act as a powerful mechanism for the precise control of behavior.
The hypothesis of cerebellar learning proposes that complex spikes in Purkinje cells engage mechanisms of plasticity in the cerebellar cortex; in turn, changes in the cerebellum depress the ...simple-spike response of Purkinje cells to a given stimulus and cause the adaptive modification of a motor behavior. Many elements of this hypothesis have been supported by prior experiments, and correlations have been found corrected between complex spikes, simple-spike plasticity and behavior corrected during the learning process. We carried out a trial-by-trial analysis of Purkinje cell responses in awake-behaving monkeys and found evidence for a causal role for complex spikes in the induction of cerebellar plasticity during a simple motor learning task. We found that the presence of a complex spike on one learning trial was linked to a substantial depression of simple-spike responses on the subsequent trial, at a time when behavioral learning was expressed.
Analysis of the neural code for sensory-motor latency in smooth pursuit eye movements reveals general principles of neural variation and the specific origin of motor latency. The trial-by-trial ...variation in neural latency in MT comprises a shared component expressed as neuron-neuron latency correlations and an independent component that is local to each neuron. The independent component arises heavily from fluctuations in the underlying probability of spiking, with an unexpectedly small contribution from the stochastic nature of spiking itself. The shared component causes the latency of single-neuron responses in MT to be weakly predictive of the behavioral latency of pursuit. Neural latency deeper in the motor system is more strongly predictive of behavioral latency. A model reproduces both the variance of behavioral latency and the neuron-behavior latency correlations in MT if it includes realistic neural latency variation, neuron-neuron latency correlations in MT, and noisy gain control downstream of MT.
•Sensory neural latency determines behavioral latency•Independent latency variation is present in underlying probability of spiking•Shared sensory neural latency variation explains much of behavioral variation•Downstream noisy gain control contributes to behavioral latency variation
Latency variation is correlated across sensory neurons and is a major contributor to variation in motor latency. Neurons also have independent variation in the latency of the underlying probability of spiking; each neuron’s irregular spiking causes surprisingly little independent variation.
The brain generates negative prediction error (NPE) signals to trigger extinction, a type of inhibitory learning that is responsible for suppressing learned behaviors when they are no longer useful. ...Neurons encoding NPE have been reported in multiple brain regions. Here, we use an optogenetic approach to demonstrate that GABAergic cerebello-olivary neurons can generate a powerful NPE signal, capable of causing extinction of conditioned motor responses on its own.
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