Learning multiple variable-speed sequences in striatum via cortical tutoring

Murray, James; Escola, G. Sean

doi:10.1101/110072

Cited by 33 publications

(64 citation statements)

References 100 publications

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“…It remains possible that some of the important mechanisms are actually implemented in a very different way. For example, the ramping activity might be a consequence of some biochemical processes that are present at the level of individual neurons or synapses in the biological brain 42,43 , but not explicitly modelled in the recurrent neural network, in which all the elements are simple rate neurons. This process can be imitated in the network by tuning the weights between neurons of canonical circuits that essentially are devoted to implementing a specific biochemical process.…”

Section: The Importance Of Ramping Activitymentioning

confidence: 99%

Low dimensional dynamics for working memory and time encoding

Cueva

Saez

Marcos

et al. 2018

Preprint

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Our decisions often depend on multiple sensory experiences separated by time delays. The brain can remember these experiences and, simultaneously, estimate the timing between events. To understand the mechanisms underlying working memory and time encoding we analyze neural activity recorded during delays in four experiments on non-human primates. To disambiguate potential mechanisms, we propose two analyses, namely, decoding the passage of time from neural data, and computing the cumulative dimensionality of the neural trajectory over time. Time can be decoded with high precision in tasks where timing information is relevant and with lower precision when irrelevant for performing the task. Neural trajectories are always observed to be low dimensional. These constraints rule out working memory models that rely on constant, sustained activity, and neural networks with high dimensional trajectories, like reservoir networks. Instead, recurrent networks trained with backpropagation capture the time encoding properties and the dimensionality observed in the data.

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Section: The Importance Of Ramping Activitymentioning

confidence: 99%

Low dimensional dynamics for working memory and time encoding

Cueva

Saez

Marcos

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Our results, showing that behaviorally relevant striatal dynamics are maintained after motor cortex lesions (Figure 4), show that this activity does not require input from motor cortex as is commonly assumed 75,76 . Recent modeling studies have suggested that sequence-associated neural dynamics in DLS could arise from recurrent activity within the striatum itself 77 . Since the striatum is an inhibitory structure, this would require a permissive excitatory drive which, in our motor cortex lesion rats, could be provided by either of its remaining inputs, from the thalamus or somatosensory cortex respectively 50,53 .…”

Section: The Nature Of the Control Signals In The Dlsmentioning

confidence: 99%

The basal ganglia can control learned motor sequences independently of motor cortex

Dhawale

Wolff

et al. 2019

Preprint

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How the basal ganglia contribute to the execution of learned motor skills has been thoroughly investigated. The two dominant models that have emerged posit roles for the basal ganglia in action selection and in the modulation of movement vigor. Here we test these models in rats trained to execute highly stereotyped and idiosyncratic task-specific motor sequences. Recordings and manipulations of neural activity in the striatum were not well explained by either model, and suggested that the basal ganglia, in particular its sensorimotor arm, are crucial for controlling the detailed kinematic structure of the learned behaviors. Importantly, the neural representations in the striatum, and the control functions they subserve, did not depend on the motor cortex. Taken together, these results extend our understanding of basal ganglia function, by suggesting that they can control and modulate lower-level subcortical motor circuits on a moment-by-moment basis to generate stereotyped learned motor sequences.

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“…But how does the DLS integrate into the larger motor network to serve these functions? A recent model of how cortical and thalamic inputs to striatum contribute to generating sequential behaviors suggested that learned sequences could be stored in intrastriatal synapses 106 . Because striatum is mostly an inhibitory structure, generating the requisite dynamics in its output neurons requires excitatory drive, which the DLS can receive from cortical, thalamic, and -for the direct pathway -nigral inputs 5,6,65 .…”

Section: Dls-projecting Thalamic Neurons Are Necessary For Task Execumentioning

confidence: 99%

Distinct roles for motor cortical and thalamic inputs to striatum during motor learning and execution

Wolff

Ölveczky

2019

Preprint

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The acquisition and execution of learned motor sequences are mediated by a distributed motor network, spanning cortical and subcortical brain areas. The sensorimotor striatum is an important cog in this network, yet how its two main inputs, from motor cortex and thalamus respectively, contribute to its role in motor learning and execution remains largely unknown. To address this, we trained rats in a task that produces highly stereotyped and idiosyncratic motor sequences. We found that motor cortical input to the sensorimotor striatum is critical for the learning process, but after the behaviors were consolidated, this corticostriatal pathway became dispensable. Functional silencing of striatal-projecting thalamic neurons, however, disrupted the execution of the learned motor sequences, causing rats to revert to behaviors produced early in learning and preventing them from re-learning the task. These results show that the sensorimotor striatum is a conduit through which motor cortical inputs can drive experiencedependent changes in subcortical motor circuits, likely at thalamostriatal synapses.

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Learning multiple variable-speed sequences in striatum via cortical tutoring

Cited by 33 publications

References 100 publications

Low dimensional dynamics for working memory and time encoding

Low dimensional dynamics for working memory and time encoding

The basal ganglia can control learned motor sequences independently of motor cortex

Distinct roles for motor cortical and thalamic inputs to striatum during motor learning and execution

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