Computation Through Neural Population Dynamics

Vyas, Saurabh; Golub, Matthew D.; Sussillo, David; Shenoy, Krishna V.

doi:10.1146/annurev-neuro-092619-094115

Cited by 449 publications

(475 citation statements)

References 157 publications

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“…Overall, we do not see any principal controversy between the neural population dynamics framework (Vyas, Golub et al 2020) and our results pertaining to sequence-like responses. Yet, in our opinion, questions remain regarding the interpretation of low-dimensional representations of neuronal population data.…”

Section: Discussioncontrasting

confidence: 59%

“…For example, Figure 1C depicts state-space trajectories for the neurons with consistent responses. These trajectories resemble the state diagram for a pendulum (see Figure 1c in (Vyas, Golub et al 2020)), and even a dependence of the trajectory on the initial state can be noticed.…”

Section: Discussionmentioning

confidence: 60%

“…Here we conducted additional analyses in response to the comments of Vyas, Golub et al (2020) regarding our paper (Lebedev, Ossadtchi et al 2019). Although Vyas et al asserted that we proposed sequence-like responses as an alternative to dynamical models of population activity, this is not what we suggested.…”

Section: Discussionmentioning

confidence: 91%

“…We concluded that jPCA reveals sequence-like dynamics in neuronal population data, but we were skeptical regarding Churchland's claim that this method elucidates a fundamental mechanisms of cortical motor control. Recently, the Shenoy group published a new review on neural population dynamics (Vyas, Golub et al 2020) where they commented on our article: "While it is possible to obtain rotation-like features from non-dynamical models [e.g., sequencelike responses (Lebedev et al 2019)], such rotations are dissimilar from those observed empirically (Michaels et al 2016). Perhaps more critically, the hypothesis of sequence-like responses does not account for single-neuron responses, which are typically multiphasic and/or peak at different times for different conditions (e.g., Churchland et al 2012, figure 2).…”

Section: ̇=mentioning

confidence: 99%

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Rotational dynamics versus sequence-like responses

Lebedev

Ninenko

2020

Preprint

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In a recent review, Vyas et al. commented on our previous observations regarding the presence of response sequences in the activity of cortical neuronal population and the contribution of such sequences to rotational dynamics patterns revealed with jPCA. Vyas et al. suggested that rotations generated from sequence-like responses are different from the ones arising from empirical neuronal patterns, which are highly heterogeneous across motor conditions in terms of response timing and shape. Here we clarify our previous findings and present new results showing that empirical population data contain plentiful neuronal responses whose temporal structure persists across conditions. The more complex, heterogeneous responses also contain temporal structure that persists within clusters of conditions. Both consistent and heterogeneous responses contribute to rotational dynamics; therefore, these contributions should be considered when interpreting population rotations. Overall, we do not see any principal contradiction between the neural population dynamics framework and our results pertaining to sequence-like responses. Yet, questions remain regarding the conclusions that can be drawn from the analysis of low-dimensional representations of neuronal population data.

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Section: Discussioncontrasting

confidence: 59%

Section: Discussionmentioning

confidence: 60%

Section: Discussionmentioning

confidence: 91%

Section: ̇=mentioning

confidence: 99%

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Rotational dynamics versus sequence-like responses

Lebedev

Ninenko

2020

Preprint

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“…While the data presented here would seem most closely related to this latter hypothesis, in principle the kinds of computations performed by earlier more algorithmic information processing accounts of timing might well be embedded in the activity of and interactions between populations of neurons. This possibility is reflected in a recent suggestion that the brain performs many computations through dynamics (57). Veterinária, project approval 0421/000/000/2018).…”

Section: Resultsmentioning

confidence: 99%

Using temperature to analyse the neural basis of a time-based decision

Monteiro

Rodrigues

Pexirra

et al. 2020

Preprint

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Time is critical for brain function, yet its neural bases are poorly understood. One prominent hypothesis posits that networks of neurons intrinsically support timing function because time is implicit in the state of neuronal population activity as it evolves along reproducible trajectories in the space of possible firing patterns. We tested this hypothesis in the striatum, an input area of the basal ganglia, by using temperature to manipulate population activity and measuring the behavioral impact on rats performing categorical time judgments. Cooler temperatures caused dilation, and warmer temperatures contraction, of both neural activity and the pattern of judgments in time. In contrast, temperature did not cause consistent effects on low level timing of movements. These data demonstrate that the time-course of evolving striatal population activity dictates the speed of a latent timing process, operating above the lower levels in a hierarchy of behavioral control.

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Heterogeneity of dynamic synergetic configurations of salience network in children with autism spectrum disorder

Guo,

Zhai,

Liu

et al. 2023

Autism Research

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Atypical functional connectivity (FC) patterns have been identified in autism spectrum disorders (ASD), especially within salience network (SN) and between SN and default mode network (DMN) and central executive network (CEN). But whether the dynamic configuration of intra‐SN and inter‐SN (SN with DMN and CEN) FC in ASD is also heterogeneous remains unknown. Based on the resting‐state functional magnetic resonance imaging data from 105 ASD and 102 typically‐developing controls (TC), we calculated the time‐varying FC of intra‐SN and inter‐SN (SN with DMN and CEN). Then, the joint recurrence features for the time‐varying FC were calculated to assess how the SN dynamically recruits different configurations of network segregation and integration in ASD, that is, synergies, from the dynamical systems perspective. We analyzed the differences in synergetic patterns between ASD subtypes obtained by k‐means clustering algorithm based on the synergy of SN and TC, and investigated the relationships between synergy of SN and severity of clinical symptoms of ASD for ASD subtypes. Two ASD subtypes were revealed, where the synergy of SN in ASD subtype 1 has lower stability and periodicity compared to the TC, and ASD subtype 2 exhibits the opposite alteration. Synergy of SN for ASD subtype 1 and 2 was found to predict the severity of communication impairments and restricted and repetitive behaviors in ASD, respectively. These results suggest the existence of subtypes with distinct patterns of the synergy of SN in ASD, and provide insight into the complex pathophysiological mechanism of clinical manifestations of ASD.

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Computation Through Neural Population Dynamics

Cited by 449 publications

References 157 publications

Rotational dynamics versus sequence-like responses

Rotational dynamics versus sequence-like responses

Using temperature to analyse the neural basis of a time-based decision

Heterogeneity of dynamic synergetic configurations of salience network in children with autism spectrum disorder

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