Volume 15 Supplement 1

Abstracts from the Twenty Third Annual Computational Neuroscience Meeting: CNS*2014

Open Access

Network-level effects of optogenetic stimulation in a computer model of macaque primary motor cortex

  • Cliff C Kerr1, 2Email author,
  • Daniel J O'Shea3,
  • Werapong Goo4,
  • Salvador Dura-Bernal1,
  • Joseph T Francis1,
  • Ilka Diester5,
  • Paul Kalanithi6,
  • Karl Deisseroth4,
  • Krishna V Shenoy4 and
  • William W Lytton1
BMC Neuroscience201415(Suppl 1):P107


Published: 21 July 2014

Optogenetics is a potent tool for performing precise perturbations to ongoing cortical dynamics in behaving primates. However, only small numbers of neurons can be recorded simultaneously. In this work, we present a biomimetic spiking network model of macaque primary motor cortex (M1) in order to explore network-level effects of optogenetic stimulation – namely, how synaptic connections modulate the optogenetic stimulus response, and how optogenetic stimulation affects interlaminar information flow. Experimental data were recorded from M1 in a male macaque. Optogenetic stimulation targeted excitatory neurons, likely preferentially affecting deeper layers, via the excitatory opsin C1V1TT, with either continuous (200 ms duration) or periodic (20, 40, or 80 Hz) pulses of green light (561 nm) [1]. The network model consisted of 24,800 spiking Izhikevich neurons [2], consisting of regular-firing and bursting pyramidal neurons and fast-spiking and low-threshold-spiking interneurons, with connectivities and proportions of each cell type across each cortical layer drawn from empirical mammalian M1 literature. Opsin channel properties were based on empirical estimates. The network model was calibrated to reproduce experimentally observed firing rates and dynamics.

Experimentally, optogenetic stimulation was found to increase firing rates by up to 300 spikes/sec, with higher firing rates observed close to the optrode; firing returned to baseline values between 4 and 5 mm laterally from the optrode. A similar response was observed in the model. Manipulating the strength of synaptic connectivity in the model elucidated several noteworthy aspects of the response. First, the decrease in peak firing rates very near the optrode appears to be due to increased recruitment of inhibitory neurons. Second, even though all excitatory neurons in the model had identical biophysical properties and received identical current input from the opsin, heterogeneities in connectivity were sufficient to account for the broad range of observed firing rates. Third, all of the high-firing excitatory cells (>100 spikes/sec) were opsin-expressing; no indirectly activated excitatory cells fired above 60 spikes/sec.

Applying spectral Granger causality to the LFPs produced by the different cortical layers of the model showed that the strongest projection without stimulation was from layer 2/3 to layer 5A, consistent with the hypothesis that descending excitation is the primary driver of dynamics in the motor cortex. Strong Granger causality from layer 5A to layer 5B was also observed. Across all layer pairs, Granger causality showed a pronounced peak in the mu rhythm band (~9 Hz), with a small, broad bump in the gamma band (~40 Hz) observed in pathways from layer 2/3 to other layers. Optogenetic stimulation in the model increased Granger causality from layer 5 to other layers in a narrow band near the stimulation frequency. It also increased the amplitude of Granger causality from layer 2/3 to other layers in the mu rhythm band, while decreasing it in the gamma band.

In summary, this work demonstrates that (1) synaptic connections play an important role in determining the network-level response to optogenetic stimulation, and (2) optogenetic stimulation may be used to enhance and suppress information flow in particular frequency bands and between particular cortical layers.

Authors’ Affiliations

Department of Physiology and Pharmacology, SUNY Downstate Medical Center
Complex Systems Group, School of Physics, University of Sydney
Neurosciences Program, Stanford University
Department of Bioengineering, Stanford University
Ernst Strüngmann Institute
Department of Neurosurgery, Stanford University


  1. Mattis J, Tye KM, Ferenczi EA, Ramakrishnan C, O'Shea DJ, Prakash R, Gunaydin LA, Hyun M, Fenno LE, Gradinaru V, Yizhar O, Deisseroth K: Principles for applying optogenetic tools derived from direct comparative analysis of microbial opsins. Nat Methods. 2011, 9: 159-172. 10.1038/nmeth.1808.PubMed CentralView ArticlePubMedGoogle Scholar
  2. Izhikevich EM, Edelman GM: Large-scale model of mammalian thalamocortical systems. Proc Natl Acad Sci USA. 2008, 105: 3593-3598. 10.1073/pnas.0712231105.PubMed CentralView ArticlePubMedGoogle Scholar


© Kerr et al; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.