Further Observations on the Rotational Structure in Neural Data

Kuzmina, Ekaterina; Kriukov, Dmitrii; Lebedev, Mikhail

doi:10.1109/dcna56428.2022.9923322

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We can see from the eigenvector expression that it contains a sinusoidal function with an increasing period as the corresponding eigenvalue index increases. Plotting the first and second eigenvectors of the tridiagonal covariance matrix provides insight into the nature of rotational dynamics in naive model-derived data [73], where we observe a cyclic pattern, also known as a “horseshoe” [70], generated by two orthogonal sinusoidal vectors with different periods. Consequently, the minimal model with the simplest covariance structure (conditioned by the travelling wave) generates oscillating eigenvectors (Figure 3B), explaining the rotational structure of projected data.…”

Section: Resultsmentioning

confidence: 99%

“…Additionally, the two leading hypotheses, the dynamical model and the representational model, continue to coexist even though they offer very different interpretations. The former model posits that “the rotations of neural state space are an indicator of the dynamical system” [20], while the latter considers rotational dynamics as derivatives of representations [61, 70, 73].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

On the Rotational Structure in Neural Data

Kuzmina,

Kriukov,

Lebedev

2023

Preprint

Self Cite

View full text Add to dashboard Cite

Spatiotemporal properties of the activity of neuronal populations in cortical motor areas have been the subject of many experimental and theoretical investigations, which generated numerous interpretations regarding the mechanisms of preparing and executing limb movements. Two competing models, namely representational and dynamical models, strive to explain the temporal course of neuronal activity and its relationship with different paramaters of movements. One proposed dynamical model employs the jPCA method, a dimensionality reduction technique, to holistically characterize oscillatory activity in a population of neurons by maximizing rotational dynamics that are present in the data. Different interpretations have been proposed for the rotational dynamics revealed with jPCA approach in various brain areas. Yet, the nature of such dynamics remains poorly understood. Here we conducted a comprehensive analysis of several neuronal-population datasets. We demonstrated that rotational dynamics were consistently accounted for by a travelling wave pattern. To quantify the rotation strength, we developed a complex-valued measure termed the gyration number. Additionally, we identify the parameters influencing the extent of rotation in the data. Overall, our findings suggest that rotational dynamics and travelling waves are the same phenomena, which requires reevaluation of the previous interpretations where they were considered as separate entities.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

On the Rotational Structure in Neural Data

Kuzmina,

Kriukov,

Lebedev

2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…There has been a lot of debate around the origins and validity of rotational dynamics of M1 activity [11,15,17,[71][72][73][74]. A previous study suggested that proprioceptive signaling can give rise to rotational "dynamics" in both sensory feedback and in the activity of somatosensory cortex and M1 [11].…”

Section: Disentangling Different Notions Of Relevant Neural Dynamicsmentioning

confidence: 99%

Feedback control of recurrent dynamics constrains learning timescales during motor adaptation

Gurnani,

Liu,

Brunton

2024

Preprint

View full text Add to dashboard Cite

Latent dynamical models of the primary motor cortex (M1) have revealed fundamental neural computations underlying motor control; however, such models often overlook the impact of sensory feedback, which can continually update cortical dynamics and correct for external perturbations. This suggests a critical need to model the interaction between sensory feedback and intrinsic dynamics. Such models would also benefit the design of brain-computer interfaces (BCIs) that decode neural activity in real time, where both user learning and proficient control require feedback. Here we investigate the flexible feedback modulation of cortical dynamics and demonstrate its impact on BCI task performance and short-term learning. By training recurrent network models with real-time sensory feedback on a simple 2D reaching task, analogous to BCI cursor control, we show how previously reported M1 activity patterns can be reinterpreted as arising from feedback-driven dynamics. Next, by incorporating adaptive controllers upstream of M1, we make a testable prediction that short-term learning for a new BCI decoder is facilitated by plasticity of inputs to M1, including remapping of sensory feedback, beyond the plasticity of recurrent connections within M1. This input-driven dynamical structure also determines the speed of adaptation and learning outcomes, and explains a continuous form of learning variability. Thus, our work highlights the need to model input-dependent latent dynamics for motor control and clarifies how constraints on learning arise from both the statistical characteristics and the underlying dynamical structure of neural activity.

show abstract

Neuronal travelling waves explain rotational dynamics in experimental datasets and modelling

Kuzmina,

Kriukov,

Lebedev

2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

Spatiotemporal properties of neuronal population activity in cortical motor areas have been subjects of experimental and theoretical investigations, generating numerous interpretations regarding mechanisms for preparing and executing limb movements. Two competing models, representational and dynamical, strive to explain the relationship between movement parameters and neuronal activity. A dynamical model uses the jPCA method that holistically characterizes oscillatory activity in neuron populations by maximizing the data rotational dynamics. Different rotational dynamics interpretations revealed by the jPCA approach have been proposed. Yet, the nature of such dynamics remains poorly understood. We comprehensively analyzed several neuronal-population datasets and found rotational dynamics consistently accounted for by a traveling wave pattern. For quantifying rotation strength, we developed a complex-valued measure, the gyration number. Additionally, we identified parameters influencing rotation extent in the data. Our findings suggest that rotational dynamics and traveling waves are typically the same phenomena, so reevaluation of the previous interpretations where they were considered separate entities is needed.

show abstract

Further Observations on the Rotational Structure in Neural Data

Cited by 3 publications

References 0 publications

On the Rotational Structure in Neural Data

On the Rotational Structure in Neural Data

Feedback control of recurrent dynamics constrains learning timescales during motor adaptation

Neuronal travelling waves explain rotational dynamics in experimental datasets and modelling

Contact Info

Product

Resources

About