Frequency characteristics and intrinsic oscillations in a neuronal network (original) (raw)
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Cross-frequency coupling between neuronal oscillations
Trends in Cognitive Sciences, 2007
Electrophysiological recordings in animals, including humans, are modulated by oscillatory activities in several frequency bands. Little is known about how oscillations in various frequency bands interact. Recent findings from the human neocortex show that the power of fast gamma oscillations (30-150 Hz) is modulated by the phase of slower theta oscillations (5-8 Hz). Given that this coupling reflects a specific interplay between large ensembles of neurons, it is likely to have profound implications for neuronal processing.
The frequency preference of neurons and synapses in a recurrent oscillatory network
The Journal of neuroscience : the official journal of the Society for Neuroscience, 2014
A variety of neurons and synapses shows a maximal response at a preferred frequency, generally considered to be important in shaping network activity. We are interested in whether all neurons and synapses in a recurrent oscillatory network can have preferred frequencies and, if so, whether these frequencies are the same or correlated, and whether they influence the network activity. We address this question using identified neurons in the pyloric network of the crab Cancer borealis. Previous work has shown that the pyloric pacemaker neurons exhibit membrane potential resonance whose resonance frequency is correlated with the network frequency. The follower lateral pyloric (LP) neuron makes reciprocally inhibitory synapses with the pacemakers. We find that LP shows resonance at a higher frequency than the pacemakers and the network frequency falls between the two. We also find that the reciprocal synapses between the pacemakers and LP have preferred frequencies but at significantly l...
2017
Most neurons of the mammalian brain display intrinsic resonance with frequency selectivity (fR) for inputs within the theta-range (4-10 Hz). Variations in network oscillation along this range depend on the animal behavior; however, whether neurons can dynamically tune their fR has not been addressed. Using slice electrophysiology, dynamic clamping and computer modeling we studied three types of cortical neurons, finding that the input resistance (Rin) inversely sets fR into the theta range, following a power law. We demonstrate that physiological changes in Rin modulate fR and response phase, serving as a mechanism that instantaneously tunes oscillatory responses. Moreover, these modulations are translated into spiking regimes, modifying spike frequency and timing. Since synaptic inputs reduce Rin, this modulation provides a mean for adjusting the frequency and timing of firing of individual neurons in interplay with the network fluctuations. This might be a widespread property amon...
PloS one, 2014
Oscillations in electrical activity are a characteristic feature of many brain networks and display a wide variety of temporal patterns. A network may express a single oscillation frequency, alternate between two or more distinct frequencies, or continually express multiple frequencies. In addition, oscillation amplitude may fluctuate over time. The origin of this complex repertoire of activity remains unclear. Different cortical layers often produce distinct oscillation frequencies. To investigate whether interactions between different networks could contribute to the variety of oscillation patterns, we created two model networks, one generating on its own a relatively slow frequency (20 Hz; slow network) and one generating a fast frequency (32 Hz; fast network). Taking either the slow or the fast network as source network projecting connections to the other, or target, network, we systematically investigated how type and strength of inter-network connections affected target networ...
Neuronal networks for induced '40 Hz' rhythms
Trends in Neurosciences, 1996
A fast, coherent EEG rhythm, called a gamma or a '40 Hz' rhythm, has been implicated both in higher brain functions, such as the 'binding' of features that are detected by sensory cortices into perceived objects, and in lower level processes, such as the phase coding of neuronal activity. Computer simulations of several parts of the brain suggest that gamma rhythms can be generated by pools of excitatory neurones, networks of inhibitory neurones, or networks of both excitatory and inhibitory neurones. The strongest experimental evidence for rhythm generators has been shown for: (1) neocortical and thalamic neurones that are intrinsic '40 Hz' oscillators, although synchrony still requires network mechanisms; and (2) hippocampal and neocortical networks of mutually inhibitory interneurones that generate collective 40 Hz rhythms when excited tonically.
Emergence of Narrowband High Frequency Oscillations from Asynchronous, Uncoupled Neural Firing
Previous experimental studies have demonstrated the emergence of narrowband local field potential oscillations during epileptic seizures in which the underlying neural activity appears to be completely asynchronous. We derive a mathematical model explaining how this counterintuitive phenomenon may occur, showing that a population of independent, completely asynchronous neurons may produce narrow-band oscillations if each neuron fires quasi-periodically, without requiring any intrinsic oscillatory cells or feedback inhibition. This quasi-periodicity can occur through cells with similar frequency–current (f –I) curves receiving a similar, high amount of uncorrelated synaptic noise. Thus, this source of oscillatory behavior is distinct from the usual cases (pacemaker cells entraining a network, or oscillations being an inherent property of the network structure), as it requires no oscillatory drive nor any specific network or cellular properties other than cells that repetitively fire with continual stimulus. We also deduce bounds on the degree of variability in neural spike-timing which will permit the emergence of such oscillations, both for action potential-and postsynaptic potential-dominated LFPs. These results suggest that even an uncoupled network may generate collective rhythms, implying that the breakdown of inhibition and high synaptic input often observed during epileptic seizures may generate narrowband oscillations. We propose that this mechanism may explain why so many disparate epileptic and normal brain mechanisms can produce similar high frequency oscillations.
Proceedings of The National Academy of Sciences, 1991
In vitro neurons in mammalian cortical layer 4 exhibit intrinsic oscillatory activity in the 10to 50-Hz frequency range (persistent sodium conductance/conjunctive properties/cognition) ABSTRACT We report here the presence of fast subthreshold oscillatory potentials recorded in vitro from neurons within layer 4 of the guinea pig frontal cortex. Two types of oscillatory neurons were recorded: (i) One type exhibited subthreshold oscillations whose frequency increased with membrane depolarization and encompassed a range of 10-45 Hz.
Stimulus-Dependent Frequency Modulation of Information Transmission in Neural Systems
Cornell University - arXiv, 2015
Neural oscillations are universal phenomena and can be observed at different levels of neural systems, from single neuron to macroscopic brain. The frequency of those oscillations are related to the brain functions. However, little is know about how the oscillating frequency of neural system affects neural information transmission in them. In this paper, we investigated how the signal processing in single neuron is modulated by subthreshold membrane potential oscillation generated by upstream rhythmic neural activities. We found that the high frequency oscillations facilitate the transferring of strong signals, whereas slow oscillations the weak signals. Though the capacity of information convey for weak signal is low in single neuron, it is greatly enhanced when weak signals are transferred by multiple pathways with different oscillation phases. We provided a simple phase plane analysis to explain the mechanism for this stimulus-dependent frequency modulation in the leakage integrate-and-fire neuron model. Those results provided a basic understanding of how the brain could modulate its information processing simply through oscillating frequency.