Emergence of Physiological Oscillation Frequencies in a Computer Model of Neocortex

• ¹ SUNY Downstate/NYU-Poly Joint Biomedical Engineering Program, Brooklyn, NY, USA
• ² Neural and Behavioral Science Program, SUNY Downstate, Brooklyn, NY, USA
• ³ Center for Neural Science, New York University, New York, NY, USA
• ⁴ Department of Physiology and Pharmacology, SUNY Downstate, Brooklyn, NY, USA
• ⁵ Department of Neurology, SUNY Downstate, Brooklyn, NY, USA
• ⁶ Kings County Hospital, Brooklyn, NY, USA

Coordination of neocortical oscillations has been hypothesized to underlie the “binding” essential to cognitive function. However, the mechanisms that generate neocortical oscillations in physiological frequency bands remain unknown. We hypothesized that interlaminar relations in neocortex would provide multiple intermediate loops that would play particular roles in generating oscillations, adding different dynamics to the network. We simulated networks from sensory neocortex using nine columns of event-driven rule-based neurons wired according to anatomical data and driven with random white-noise synaptic inputs. We tuned the network to achieve realistic cell firing rates and to avoid population spikes. A physiological frequency spectrum appeared as an emergent property, displaying dominant frequencies that were not present in the inputs or in the intrinsic or activated frequencies of any of the cell groups. We monitored spectral changes while using minimal dynamical perturbation as a methodology through gradual introduction of hubs into individual layers. We found that hubs in layer 2/3 excitatory cells had the greatest influence on overall network activity, suggesting that this subpopulation was a primary generator of theta/beta strength in the network. Similarly, layer 2/3 interneurons appeared largely responsible for gamma activation through preferential attenuation of the rest of the spectrum. The network showed evidence of frequency homeostasis: increased activation of supragranular layers increased firing rates in the network without altering the spectral profile, and alteration in synaptic delays did not significantly shift spectral peaks. Direct comparison of the power spectra with experimentally recorded local field potentials from prefrontal cortex of awake rat showed substantial similarities, including comparable patterns of cross-frequency coupling.