Signatures of synchrony in pairwise count correlations

Tchumatchenko, Tatjana; Geisel, Theo; Volgushev, Maxim; Wolf, Fred

doi:10.3389/neuro.10.001.2010

Cited by 55 publications

(116 citation statements)

References 42 publications

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“…When correcting for errors in movement, the degree of response takes into account the uncertainty intrinsic to each type of feedback characterizing the movement itself (40, 41). Feedback with higher fidelity (less uncertainty) is trusted more.…”

Section: Resultsmentioning

confidence: 99%

Illusory movement perception improves motor control for prosthetic hands

et al. 2018

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To effortlessly complete an intentional movement, the brain needs feedback from the body regarding the movement’s progress. This largely non-conscious kinesthetic sense helps the brain to learn relationships between motor commands and outcomes to correct movement errors. Prosthetic systems for restoring function have predominantly focused on controlling motorized joint movement. Without the kinesthetic sense, however, these devices do not become intuitively controllable. Here we report a method for endowing human amputees with a kinesthetic perception of dexterous robotic hands. Vibrating the muscles used for prosthetic control via a neural-machine interface produced the illusory perception of complex grip movements. Within minutes, three amputees integrated this kinesthetic feedback and improved movement control. Combining intent, kinesthesia, and vision instilled participants with a sense of agency over the robotic movements. This feedback approach for closed-loop control opens a pathway to seamless integration of minds and machines.

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Section: Resultsmentioning

confidence: 99%

Illusory movement perception improves motor control for prosthetic hands

et al. 2018

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“…The multivariate Gaussian assumption is simple enough to allow a thorough analysis (Burak et al, 2009;Tchumatchenko et al, 2010) while we demonstrate that it accurately describes the data. If the actual distribution significantly deviates from a multivariate Gaussian, it is likely that other forms of higher-order interactions will be present among the spike trains.…”

Section: Discussionmentioning

confidence: 99%

“…The DG model only requires event rates and pairwise correlations to approximate those pattern probabilities, which can be obtained within relatively short durations (Ͻ30 min in our analysis), and, importantly, those durations are independent of system size. The DG model is also simple enough to be treated analytically (Amari et al, 2003;Burak et al, 2009;Tchumatchenko et al, 2010;Macke et al, 2011), which allows for direct calculation of pattern probabilities. This advantage holds even for small systems (n ϭ 10 elements) as shown in Figure 12 A.…”

Section: Efficient Characterization Of Population Activitymentioning

confidence: 99%

Higher-Order Interactions Characterized in Cortical Activity

Yu¹,

Yang²,

Nakahara³

et al. 2011

J. Neurosci.

204

267

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In the cortex, the interactions among neurons give rise to transient coherent activity patterns that underlie perception, cognition, and action. Recently, it was actively debated whether the most basic interactions, i.e., the pairwise correlations between neurons or groups of neurons, suffice to explain those observed activity patterns. So far, the evidence reported is controversial. Importantly, the overall organization of neuronal interactions and the mechanisms underlying their generation, especially those of high-order interactions, have remained elusive. Here we show that higher-order interactions are required to properly account for cortical dynamics such as ongoing neuronal avalanches in the alert monkey and evoked visual responses in the anesthetized cat. A Gaussian interaction model that utilizes the observed pairwise correlations and event rates and that applies intrinsic thresholding identifies those higher-order interactions correctly, both in cortical local field potentials and spiking activities. This allows for accurate prediction of large neuronal population activities as required, e.g., in brain-machine interface paradigms. Our results demonstrate that higher-order interactions are inherent properties of cortical dynamics and suggest a simple solution to overcome the apparent formidable complexity previously thought to be intrinsic to those interactions.

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“…Previous research has shown that certain types of variability and covariability can be directly related to the spike train auto-and crosscorrelation functions (Bair et al 2001;Nawrot 2010;Tchumatchenko et al 2010). While existing models for the analysis of population spike trains allow specification of autoand crosscorrelation functions (Krumin and Shoham 2009;Macke et al 2009;Gutnisky and Josić 2010;Lyamzin et al 2010) the parameters of these models have not yet been explicitly linked to the different measures of variability and covariability described above.…”

Section: Introductionmentioning

confidence: 95%

Analysis and modelling of variability and covariability of population spike trains across multiple time scales

Lyamzin

Garcia-Lazaro

Lesica

2012

Network: Computation in Neural Systems

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As multi-electrode and imaging technology begin to provide us with simultaneous recordings of large neuronal populations, new methods for modelling such data must also be developed. We present a model of responses to repeated trials of a sensory stimulus based on thresholded Gaussian processes that allows for analysis and modelling of variability and covariability of population spike trains across multiple time scales. The model framework can be used to specify the values of many different variability measures including spike timing precision across trials, coefficient of variation of the interspike interval distribution, and Fano factor of spike counts for individual neurons, as well as signal and noise correlations and correlations of spike counts across multiple neurons. Using both simulated data and data from different stages of the mammalian auditory pathway, we demonstrate the range of possible independent manipulations of different variability measures, and explore how this range depends on the sensory stimulus. The model provides a powerful framework for the study of experimental and surrogate data and for analyzing dependencies between different statistical properties of neuronal populations.

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Signatures of synchrony in pairwise count correlations

Cited by 55 publications

References 42 publications

Illusory movement perception improves motor control for prosthetic hands

Illusory movement perception improves motor control for prosthetic hands

Higher-Order Interactions Characterized in Cortical Activity

Analysis and modelling of variability and covariability of population spike trains across multiple time scales

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