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  • Neuronal mechanisms underlying beta/gamma oscillations (20-80 Hz) are not completely understood. Here, we show that in vivo beta/gamma oscillations in the cat visual cortex sometimes exhibit remarkably stable frequency even when inputs fluctuate dramatically. Enhanced frequency stability is associated with stronger oscillations measured in individual units and larger power in the local field potential. Simulations of neuronal circuitry demonstrate that membrane properties of inhibitory interneurons strongly determine the characteristics of emergent oscillations. Exploration of networks containing either integrator or resonator inhibitory interneurons revealed that: (i) Resonance, as opposed to integration, promotes robust oscillations with large power and stable frequency via a mechanism called RING (Resonance INduced Gamma); resonance favors synchronization by reducing phase delays between interneurons and imposes bounds on oscillation cycle duration; (ii) Stability of frequency and robustness of the oscillation also depend on the relative timing of excitatory and inhibitory volleys within the oscillation cycle; (iii) RING can reproduce characteristics of both Pyramidal INterneuron Gamma (PING) and INterneuron Gamma (ING), transcending such classifications; (iv) In RING, robust gamma oscillations are promoted by slow but are impaired by fast inputs. Results suggest that interneuronal membrane resonance can be an important ingredient for generation of robust gamma oscillations having stable frequency.
    Data Types:
    • Tabular Data
  • Synchronization of neuronal activity in the visual cortex at low (30-70 Hz) and high gamma band frequencies (> 70 Hz) has been associated with distinct visual processes, but mechanisms underlying high-frequency gamma oscillations remain unknown. In rat visual cortex slices, kainate and carbachol induce high-frequency gamma oscillations (fast-gamma; peak frequency approximately 80 Hz at 37 degrees C) that can coexist with low-frequency gamma oscillations (slow-gamma; peak frequency approximately 50 Hz at 37 degrees C) in the same column. Current-source density analysis showed that fast-gamma was associated with rhythmic current sink-source sequences in layer III and slow-gamma with rhythmic current sink-source sequences in layer V. Fast-gamma and slow-gamma were not phase-locked. Slow-gamma power fluctuations were unrelated to fast-gamma power fluctuations, but were modulated by the phase of theta (3-8 Hz) oscillations generated in the deep layers. Fast-gamma was spatially less coherent than slow-gamma. Fast-gamma and slow-gamma were dependent on gamma-aminobutyric acid (GABA)(A) receptors, alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors and gap-junctions, their frequencies were reduced by thiopental and were weakly dependent on cycle amplitude. Fast-gamma and slow-gamma power were differentially modulated by thiopental and adenosine A(1) receptor blockade, and their frequencies were differentially modulated by N-methyl-D-aspartate (NMDA) receptors, GluK1 subunit-containing receptors and persistent sodium currents. Our data indicate that fast-gamma and slow-gamma both depend on and are paced by recurrent inhibition, but have distinct pharmacological modulation profiles. The independent co-existence of fast-gamma and slow-gamma allows parallel processing of distinct aspects of vision and visual perception. The visual cortex slice provides a novel in vitro model to study cortical high-frequency gamma oscillations.
    Data Types:
    • Tabular Data
  • Values are means ± SD, number of animals with oscillations is in parentheses. OE, olfactory epithelium; OB, olfactory bulb.
    Data Types:
    • Tabular Data
  • All 12 neurons were given a sinusoidal current but only 7 of the 12 neurons were given a sine wave with changing frequency [impedance amplitude profile (ZAP) input]. The parameters evaluated by sine and ZAP inputs were not different from each other in a given (rest or depolarized) state. n = 12 for sine input [10 rest (2 cells showed no resonant peak), 12 depolarized]; n = 7 for ZAP input [5 rest (2 cells showed no resonant peak); n = 7 depolarized]. ↵* P < 0.01, ↵† P < 0.05, paired Wilcoxon, rest value different from depolarized value.
    Data Types:
    • Tabular Data
  • Many oscillatory networks involve neurons that exhibit intrinsic rhythmicity but possess a large variety of voltage-gated currents that interact in a complex fashion, making it difficult to determine which factors control frequency. Yet these neurons often have preferred (resonance) frequencies that can be close to the network frequency. Because the preferred frequency results from the dynamics of ionic currents, it can be assumed to depend on parameters that determine the neuron's oscillatory waveform shape. The pyloric network frequency in the crab Cancer borealis is correlated with the preferred frequency of its bursting pacemaker neurons anterior burster and pyloric dilator (PD). We measured the preferred frequency of the PD neuron in voltage clamp, which allows control of the oscillation voltage range and waveforms (sine waves and realistic oscillation waveforms), and showed that (1) the preferred frequency depends on the voltage range of the oscillating voltage waveform; (2) the slope of the waveform near its peak has a strongly negative correlation with the preferred frequency; and (3) correlations between parameters of the PD neuron oscillation waveform and its preferred frequency can be used to predict shifts in the network frequency. As predicted by these results, dynamic clamp shifts of the upper or lower voltage limits of the PD neuron waveform during ongoing oscillations changed the network frequency, consistent with the predictions from the preferred frequency. These results show that the voltage waveform of oscillatory neurons can be predictive of their preferred frequency and thus the network oscillation frequency.
    Data Types:
    • Tabular Data
  • Definition of symbols: 0, no change; + and ++, moderate and large increase; − and —, moderate and large decrease ↵* The fact that the frequency decreases with both a 50% increase and 50% decrease in these parameter values indicates that the starting value is near a local maximum in the dependence of frequency on these parameters
    Data Types:
    • Tabular Data
  • The first row shows the voltage threshold for spike generation , which has been adjusted to yield approximately the same firing rate response to the background synaptic bombardment in canonical and r-matched neurons. The second and third rows show the single-neuron statistics to the background synaptic bombardment alone. The fourth and subsequent rows show single-neuron and ensemble statistics measured in network simulations. , single-neuron firing rate; ISI CV, coefficient of variation of inter-spike intervals; , frequency of network oscillations; , mean phase coherence.
    Data Types:
    • Tabular Data
  • The thalamocortical network is modelled using the Wilson-Cowan equations for neuronal population activity. We show that this population model with biologically derived parameters possesses intrinsic nonlinear oscillatory dynamics, and that the frequency of oscillation lies within the spindle range. Spindle oscillations are an early sleep oscillation characterized by high-frequency bursts of action potentials followed by a period of quiescence, at a frequency of 7-14 Hz. Spindles are generally regarded as being generated by intrathalamic circuitry, as decorticated thalamic slices and the isolated thalamic reticular nucleus exhibit spindles. However, the role of cortical feedback has been shown to regulate and synchronize the oscillation. Previous modelling studies have mainly used conductance-based models and hence the mechanism relied upon the inclusion of ionic currents, particularly the T-type calcium current. Here we demonstrate that spindle-frequency oscillatory activity can also arise from the nonlinear dynamics of the thalamocortical circuit, and we use bifurcation analysis to examine the robustness of this oscillation in terms of the functional range of the parameters used in the model. The results suggest that the thalamocortical circuit has intrinsic nonlinear population dynamics which are capable of providing robust support for oscillatory activity within the frequency range of spindle oscillations.
    Data Types:
    • Tabular Data
  • Counts of high-frequency oscillations (HFOs) in each time epoch for dispersion analysis. Preictal and postictal epochs were defined as 10 min before and after seizures, respectively.
    Data Types:
    • Tabular Data
  • Means and standard errors are presented for frequency (Hz), amplitude (pA), and decay time (ms) of sEPSCsosc observed in young rats (PN 10-17), older rats (3 weeks-old), and adult mice (6 weeks-old) in aCSF containing different concentrations of Mg2+. Studies on older rats and mice were only done using a 0 Mg2+ concentration. The sEPSCosc is characterized by its large amplitude, slow decay time, and low frequency. These events are absent in 2 mM Mg2+ aCSF, present in 1 mM Mg2+ aCSF, and facilitated in 0 mM Mg2+ aCSF. Though less frequent and smaller in amplitude, the sEPSCosc is also present in older rats and mice. Like in young rats, the sEPSCosc is potentiated by the non-selective glutamate transporter inhibitor, TBOA, in both older rats (0.02±0.01 Hz; 789±69 ms decay time) and mice (0.01±0.003 Hz; 414±106 ms decay time). Abbreviations: n, number of cells; PN, age (postnatal).
    Data Types:
    • Tabular Data