Regulating Cortical Oscillations in an Inhibition-Stabilized Network

Abstract

Understanding the anatomical and functional architecture of the brain is essential for designing neurally inspired intelligent systems. Theoretical and empirical studies suggest a role for narrowband oscillations in shaping the functional architecture of the brain through their role in coding and communication of information. Such oscillations are ubiquitous signals in the electrical activity recorded from the brain. In the cortex, oscillations detected in the gamma range (30–80 Hz) are modulated by behavioral states and sensory features in complex ways. How is this regulation achieved? Although several underlying principles for the genesis of these oscillations have been proposed, a unifying account for their regulation has remained elusive. In a network of excitatory and inhibitory neurons operating in an inhibition-stabilized regime, we show that strongly superlinear responses of inhibitory neurons facilitate bidirectional regulation of oscillation frequency and power. In such a network, the balance of drives to the excitatory and inhibitory populations determines how the power and frequency of oscillations are modulated. The model accounts for the puzzling increase in their frequency with the salience of visual stimuli, and a decrease with their size. Oscillations in our model grow stronger as the mean firing level is reduced, accounting for the size dependence of visually evoked gamma rhythms, and suggesting a role for oscillations in improving the signal-to-noise ratio (SNR) of signals in the brain. Empirically testing such predictions is still challenging, and implementing the proposed coding and communication strategies in neuromorphic systems could assist in our understanding of the biological system.

Cortical oscillations arise from contextual interactions that regulate sparse coding

“…Although multiple phenomena may underlie the PING architecture itself (29), the noisy oscillations in our model are based on the phenomenon of limit cycles in an unstable regime of an inhibition-stabilized network (27). Elsewhere, we have used a rate model to further analyze the basis of the observed behavior in our spiking model from a dynamical systems perspective (36). An alternate model of narrowband increase in LFP power is based on quasi-cycles, which involve noise amplification of damped oscillations in the stable regime of the network (18,29,37,38) and merits further investigation in the context of recent data on visual gamma.…”

Abnormal Gamma Oscillations in N-Methyl-D-Aspartate Receptor Hypofunction Models of Schizophrenia

“…We recently explored the modulation of GBO power and frequency in a special case of Wilson-Cowan (81) oscillation model based on an Inhibition-Stabilized PING model (ISN-PING) with superlinear inhibition (82, 83). The strong inhibition in this model stabilizes the positive feedback in the population of pyramidal cells.…”

“…The model predicted rate-level oscillations, rather than spike to spike, with a broad distribution of ISI. In this regime, the power of GBO in the model is proportional to the ratio of stimulation to the local excitatory and inhibitory neurons, other factors remaining relatively unchanged; if this ratio increases, power in GBO increase (82, 83). The model predicts an increase in the GBO strength if the key effect of NMDAR hypofunction is captured as reduction in the excitability of INs (83).…”

Attentional Bias Through Oscillatory Coherence Between Excitatory Activity and Inhibitory Minima

“…The strong couplings are an essential ingredient in order to establish attracting states that correspond to stored Hebbian patterns (see Hertz, Krogh, & Palmer, 1991, for an introduction to the corresponding Cohen-GrossbergHopfield model; our system may be understood as a complex-valued generalization- Burwick, 2007Burwick, , 2008bBurwick, , 2011. Because of these strong couplings and also due to the incorporation of excitatory and inhibitory units, the model appears to have kinship with the strong-coupling regime of inhibitory stabilized models (Tsodyks et al, 1997; see also the recent discussion in Jadi & Sejnowski, 2014). There are, however, essential differences with respect to the considered parameter regimes.…”