Bringing the Dynamics of Movement under Control

Renart, Alfonso

doi:10.1016/j.neuron.2014.06.002

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…To address this question, we implemented a simple recurrent neural network (RNN), operating as a dynamical system, from which the velocity profiles required to complete the previously described center-out reaching task can be read out (Fig 3A; Methods). Recent studies have augmented the original findings of Churchland et al (2012) by generating biologically plausible RNNs that seek to produce complex activity patterns [20,32,33] and using cortical circuit models to explain population activity [34]. …”

Section: Results / Discussionmentioning

confidence: 99%

Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning

2016

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Recent models of movement generation in motor cortex have sought to explain neural activity not as a function of movement parameters, known as representational models, but as a dynamical system acting at the level of the population. Despite evidence supporting this framework, the evaluation of representational models and their integration with dynamical systems is incomplete in the literature. Using a representational velocity-tuning based simulation of center-out reaching, we show that incorporating variable latency offsets between neural activity and kinematics is sufficient to generate rotational dynamics at the level of neural populations, a phenomenon observed in motor cortex. However, we developed a covariance-matched permutation test (CMPT) that reassigns neural data between task conditions independently for each neuron while maintaining overall neuron-to-neuron relationships, revealing that rotations based on the representational model did not uniquely depend on the underlying condition structure. In contrast, rotations based on either a dynamical model or motor cortex data depend on this relationship, providing evidence that the dynamical model more readily explains motor cortex activity. Importantly, implementing a recurrent neural network we demonstrate that both representational tuning properties and rotational dynamics emerge, providing evidence that a dynamical system can reproduce previous findings of representational tuning. Finally, using motor cortex data in combination with the CMPT, we show that results based on small numbers of neurons or conditions should be interpreted cautiously, potentially informing future experimental design. Together, our findings reinforce the view that representational models lack the explanatory power to describe complex aspects of single neuron and population level activity.

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Section: Results / Discussionmentioning

confidence: 99%

Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning

2016

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“…Here, we have identified a candidate for this pathophysiological 'missing link' , in the attenuation of recurrent inhibitory intrinsic connections that normally govern the semantic appraisal network in the anterior temporal lobe. Tonic inhibitory GABAergic transmission is likely to be crucial for sharpening the activation of neural representations via close coupling to phasic excitatory mechanisms and may constitute a generic principle of normal neural network function, synchronising the operation of network elements and conferring network strength, efficiency and plasticity 39,46,47,61,62 . GABA-ergic inhibitory interneurons in the more superficial cortical laminae arborise widely within cortical columns 62,63 , providing a microanatomical substrate for the normal recurrent inhibitory control over regional temporal lobe circuitry-the circuitry that is targeted by pathogenic protein deposition in SD 24 .…”

Section: Discussionmentioning

confidence: 99%

The neurophysiological architecture of semantic dementia: spectral dynamic causal modelling of a neurodegenerative proteinopathy

Benhamou

Marshall

Russell

et al. 2020

Sci Rep

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The selective destruction of large-scale brain networks by pathogenic protein spread is a ubiquitous theme in neurodegenerative disease. Characterising the circuit architecture of these diseases could illuminate both their pathophysiology and the computational architecture of the cognitive processes they target. However, this is challenging using standard neuroimaging techniques. Here we addressed this issue using a novel technique—spectral dynamic causal modelling—that estimates the effective connectivity between brain regions from resting-state fMRI data. We studied patients with semantic dementia—the paradigmatic disorder of the brain system mediating world knowledge—relative to healthy older individuals. We assessed how the effective connectivity of the semantic appraisal network targeted by this disease was modulated by pathogenic protein deposition and by two key phenotypic factors, semantic impairment and behavioural disinhibition. The presence of pathogenic protein in SD weakened the normal inhibitory self-coupling of network hubs in both antero-mesial temporal lobes, with development of an abnormal excitatory fronto-temporal projection in the left cerebral hemisphere. Semantic impairment and social disinhibition were linked to a similar but more extensive profile of abnormally attenuated inhibitory self-coupling within temporal lobe regions and excitatory projections between temporal and inferior frontal regions. Our findings demonstrate that population-level dynamic causal modelling can disclose a core pathophysiological feature of proteinopathic network architecture—attenuation of inhibitory connectivity—and the key elements of distributed neuronal processing that underwrite semantic memory.

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Bringing the Dynamics of Movement under Control

Cited by 2 publications

References 15 publications

Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning

Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning

The neurophysiological architecture of semantic dementia: spectral dynamic causal modelling of a neurodegenerative proteinopathy

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