Spontaneous dynamics of neural networks in deep layers of prefrontal cortex

Abstract

Cortical systems maintain and process information through the sustained activation of recurrent local networks of neurons. Layer 5 is known to have a major role in generating the recurrent activation associated with these functions, but relatively little is known about its intrinsic dynamics at the mesoscopic level of large numbers of neighboring neurons. Using calcium imaging, we measured the spontaneous activity of networks of deep-layer medial prefrontal cortical neurons in an acute slice model. Inferring the simultaneous activity of tens of neighboring neurons, we found that while the majority showed only sporadic activity, a subset of neurons engaged in sustained delta frequency rhythmic activity. Spontaneous activity under baseline conditions was weakly correlated between pairs of neurons, and rhythmic neurons showed little coherence in their oscillations. However, we consistently observed brief bouts of highly synchronous activity that must be attributed to network activity. NMDA-mediated stimulation enhanced rhythmicity, synchrony, and correlation within these local networks. These results characterize spontaneous prefrontal activity at a previously unexplored spatiotemporal scale and suggest that medial prefrontal cortex can act as an intrinsic generator of delta oscillations.

Document Details

Document Type
Pub Defense Publication
Publication Date
Apr 01, 2017
Source ID
10.1152/jn.00295.2016

Entities

People

  • A. V. Nurmikko
  • Andrew S. Blaeser
  • Barry W. Connors

Organizations

  • Brown University
  • Office of Emerging Frontiers and Multidisciplinary Activities

Tags

Fields of Study

  • Biology

Readers

  • Neural Network Machine Learning.
  • Neuroscience

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks