4.3 Article

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

Journal

FRONTIERS IN NEURAL CIRCUITS
Volume 12, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fncir.2018.00052

Keywords

monkey motor cortex; spike time irregularity; spike count variability; behavioral context; renewal processes

Categories

Funding

  1. Collaborative Research Agreement CNRS-RIKEN
  2. Collaborative Research Agreement CNRS-FZ Julich
  3. Agence National de la Recherche [ANR-11-BSV4-0026]
  4. BrainScaleS (EU) [269912]
  5. International Associate Laboratory (LIA) Vision-for-Action''
  6. Helmholtz Portfolio Supercomputing and Modeling for the Human Brain (SMHB)''
  7. EU [720270, 604102]
  8. German Israeli Foundation grant Multiple time scales of signals and noise in the motor hierarchy'' (GIF) [I-1224-396.13/2012]
  9. Agence Nationale de la Recherche (ANR) [ANR-11-BSV4-0026] Funding Source: Agence Nationale de la Recherche (ANR)

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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 approximate to CV2. This expectation was met during movement, but not during wait where FF >> CV2(2). 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.

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