Emergence of Physiological Oscillation Frequencies in a Computer Model of Neocortex

Abstract

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.

Power-efficient simulation of detailed cortical microcircuits on SpiNNaker

“…5(a)) shows global-average firing rate to oscillate around 4 Hz, with the majority of cells (Fig. 5(b)) firing somewhat more frequently than suggested by Neymotin et al (2011) but nevertheless close to biologically-plausible rates. The firing rate for the simulation without weights (not shown) was largely constant over time but showed some variation due to the low-quality pseudo-random numbers used for the background currents.…”

“…Ananthanarayanan et al use 2 16 processors in simulation, suggesting a value P m = 10 W · 2 16 ≈ 655 kW. They model 1.6 · 10 9 of the ≈10 10 neurons in the cortex (Braitenberg and Schüz, 1991) (S c /S m ≈ 10) at a rate of "643 seconds for one second of simulation per Hz of [spiking] activity" implying T m = 1286 at a global average 2 Hz (Neymotin et al, 2011) neural spiking rate. Consequently…”

Neocortical Simulation for Epilepsy Surgery Guidance: Localization and Intervention

“…To allow for dependence on V m , synaptic inputs changed Baseline wiring and number of cells per layer were similar to those used previously ( Fig. 22.1) [22]. Thirteen cell types were arranged in 4 layers: 2/3, 4, 5, and 6.…”

“…Prediction in dynamical systems is notoriously difficult [22]. In dynamical systems, A pushes on B at the same time as B pushes on A.…”

A new regime for highly robust gamma oscillation with co-exist of accurate and weak synchronization in excitatory–inhibitory networks

“…Real neural circuits, for example neural circuits in V1 cortex, consist of many types of neurons (Neymotin et al 2011). These neurons organized into two groups, excitatory neuron group and inhibitory neuron group.…”