Motor-like neural dynamics in two parietal areas during arm reaching

Diomedi, Stefano; F, Vaccari; Galletti, Claudio; Hadjidimitrakis, Kostas; Fattori, Patrizia

doi:10.1016/j.pneurobio.2021.102116

Cited by 18 publications

(34 citation statements)

References 63 publications

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“…Third, rotational dynamics were observed not only in MC, but also in somatosensory cortex during the posture perturbation and reaching tasks. Rotational dynamics were observed in S1 (areas 3 a and 1), A2 and A5, and a recent study has even identified rotational dynamics in the rostral areas of posterior parietal cortex (V6a, Diomedi et al, 2021 ). These areas reflect important components of frontoparietal circuits involved in the planning and execution of arm motor function ( Chowdhury et al, 2020 ; Kalaska, 1996 ; Kalaska et al, 1990 ; Omrani et al, 2016 ; Takei et al, 2021 ).…”

Section: Discussionmentioning

confidence: 94%

Rotational dynamics in motor cortex are consistent with a feedback controller

Kalidindi

Cross

Lillicrap

et al. 2021

eLife

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Recent studies have identified rotational dynamics in motor cortex (MC), which many assume arise from intrinsic connections in MC. However, behavioral and neurophysiological studies suggest that MC behaves like a feedback controller where continuous sensory feedback and interactions with other brain areas contribute substantially to MC processing. We investigated these apparently conflicting theories by building recurrent neural networks that controlled a model arm and received sensory feedback from the limb. Networks were trained to counteract perturbations to the limb and to reach toward spatial targets. Network activities and sensory feedback signals to the network exhibited rotational structure even when the recurrent connections were removed. Furthermore, neural recordings in monkeys performing similar tasks also exhibited rotational structure not only in MC but also in somatosensory cortex. Our results argue that rotational structure may also reflect dynamics throughout the voluntary motor system involved in online control of motor actions.

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Section: Discussionmentioning

confidence: 94%

Rotational dynamics in motor cortex are consistent with a feedback controller

Kalidindi

Cross

Lillicrap

et al. 2021

eLife

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show abstract

“…The fitted model parameters include transition probabilities, which define the likelihood of transitioning from any given state to any other state, and emission probabilities, which govern the generation of observable data within each state. Classic frequentist HMMs applied to neural data have used a univariate multinomial distribution for emission probabilities (Bollimunta et al, 2012; Mazurek et al, 2018; Diomedi et al, 2021), encoding which of N neurons is spiking in each time bin, and choosing a random neuron if multiple neurons spike in the same bin. Such an approach does not capture the multivariate nature of neural population activity measured with high density recordings, nor the statistical distribution of neural spiking, and breaks down as larger neural populations are recorded from.…”

Section: Methodsmentioning

confidence: 99%

Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex

Kirchherr

Moraga

Coudé

et al. 2023

Eur J of Neuroscience

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Neural populations, rather than single neurons, may be the fundamental unit of cortical computation. Analysing chronically recorded neural population activity is challenging not only because of the high dimensionality of activity but also because of changes in the signal that may or may not be due to neural plasticity. Hidden Markov models (HMMs) are a promising technique for analysing such data in terms of discrete latent states, but previous approaches have not considered the statistical properties of neural spiking data, have not been adaptable to longitudinal data, or have not modelled condition‐specific differences. We present a multilevel Bayesian HMM addresses these shortcomings by incorporating multivariate Poisson log‐normal emission probability distributions, multilevel parameter estimation and trial‐specific condition covariates. We applied this framework to multi‐unit neural spiking data recorded using chronically implanted multi‐electrode arrays from macaque primary motor cortex during a cued reaching, grasping and placing task. We show that, in line with previous work, the model identifies latent neural population states which are tightly linked to behavioural events, despite the model being trained without any information about event timing. The association between these states and corresponding behaviour is consistent across multiple days of recording. Notably, this consistency is not observed in the case of a single‐level HMM, which fails to generalise across distinct recording sessions. The utility and stability of this approach is demonstrated using a previously learned task, but this multilevel Bayesian HMM framework would be especially suited for future studies of long‐term plasticity in neural populations.

show abstract

Section: Methodsmentioning

confidence: 99%

Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex

Kirchherr

Moraga

Coudé

et al. 2022

Preprint

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Neural populations, rather than single neurons, may be the fundamental unit of cortical computation. Analyzing chronically recorded neural population activity is challenging not only because of the high dimensionality of activity in many neurons, but also because of changes in the recorded signal that may or may not be due to neural plasticity. Hidden Markov models (HMMs) are a promising technique for analyzing such data in terms of discrete, latent states, but previous approaches have either not considered the statistical properties of neural spiking data, have not been adaptable to longitudinal data, or have not modeled condition specific differences. We present a multilevel Bayesian HMM which addresses these shortcomings by incorporating multivariate Poisson log-normal emission probability distributions, multilevel parameter estimation, and trial-specific condition covariates. We applied this framework to multi-unit neural spiking data recorded using chronically implanted multi-electrode arrays from macaque primary motor cortex during a cued reaching, grasping, and placing task. We show that the model identifies latent neural population states which are tightly linked to behavioral events, despite the model being trained without any information about event timing. We show that these events represent specific spatiotemporal patterns of neural population activity and that their relationship to behavior is consistent over days of recording. The utility and stability of this approach is demonstrated using a previously learned task, but this multilevel Bayesian HMM framework would be especially suited for future studies of long-term plasticity in neural populations.

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Motor-like neural dynamics in two parietal areas during arm reaching

Cited by 18 publications

References 63 publications

Rotational dynamics in motor cortex are consistent with a feedback controller

Rotational dynamics in motor cortex are consistent with a feedback controller

Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex

Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex

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