Description: Behavioral Context Determines Network State and Variability Dynamics in Monkey Motor Cortex

This title appears in the Scientific Report : 2018

Behavioral Context Determines Network State and Variability Dynamics in Monkey Motor Cortex

Variability of spiking activity is ubiquitous throughout the brain but little is known about its contextual dependance. Trial-to-trial spike count variability, estimated by the Fano Factor (FF), and within-trial spike time irregularity, quantified by the coefficient of variation (CV), reflect variab...

Personal Name(s):	Riehle, Alexa (Corresponding author)
	Brochier, Thomas / Nawrot, Martin / Grün, Sonja
Contributing Institute:	Computational and Systems Neuroscience; INM-6 Jara-Institut Brain structure-function relationships; INM-10 Theoretical Neuroscience; IAS-6
Published in:	Frontiers in neural circuits, 12 (2018) S. 52
Imprint:	Lausanne Frontiers Research Foundation 2018
PubMed ID:	30050415
DOI:	10.3389/fncir.2018.00052
Document Type:	Journal Article
Research Program:	Human Brain Project Specific Grant Agreement 2 Human Brain Project Specific Grant Agreement 1 Supercomputing and Modelling for the Human Brain Brain-inspired multiscale computation in neuromorphic hybrid systems Connectivity and Activity The Human Brain Project
Link:	Get full text Get full text OpenAccess OpenAccess
	Publikationsportal JuSER

Please use the identifier: http://hdl.handle.net/2128/19561 in citations.
Please use the identifier: http://dx.doi.org/10.3389/fncir.2018.00052 in citations.

Variability of spiking activity is ubiquitous throughout the brain but little is known about its contextual dependance. Trial-to-trial spike count variability, estimated by the Fano Factor (FF), and within-trial spike time irregularity, quantified by the coefficient of variation (CV), reflect variability on long and short time scales, respectively. We co-analyzed FF and the local coefficient of variation (CV2) in monkey motor cortex comparing two behavioral contexts, movement preparation (wait) and execution (movement). We find that the FF significantly decreases from wait to movement, while the CV2 increases. The more regular firing (expressed by a low CV2) during wait is related to an increased power of local field potential (LFP) beta oscillations and phase locking of spikes to these oscillations. In renewal processes, a widely used model for spiking activity under stationary input conditions, both measures are related as FF ≈ CV2. This expectation was met during movement, but not during wait where FF ≫ CV22. Our interpretation is that during movement preparation, ongoing brain processes result in changing network states and thus in high trial-to-trial variability (expressed by a high FF). During movement execution, the network is recruited for performing the stereotyped motor task, resulting in reliable single neuron output. Our interpretation is in the light of recent computational models that generate non-stationary network conditions.