The abundance of oscillatory activity in neuronal circuits has stimulated both theoretical and experimental neuroscience over many decades. Questions about the role of oscillations in the representation of information in the brain has ever since been a source of great inspiration to neuroscientists. This special issue of Network: Computation in Neural Systems is aimed to review recent successes of computational models of neuronal oscillations as well as insights into their computational/functional roles. The special issue includes original contributions and reviews on several aspects of computation in oscillating neural networks, from phase coding to the biophysics of neuronal oscillations.
In the study by Graham and Spera a detailed model of a hippocampal CA1 pyramidal cell is used to suggest a biophysical cellular mechanism for a classical hypothesis of hippocampal function: the separation of a theta cycle into a coding and encoding phase. The paper by Erdi et al. focuses on the hippocampal theta rhythm as well, but this time from clinical perspective. Employing a computational model of neuron populations that generate septo-hippocampal theta, the authors suggest an explanation of how anxiolytic drugs affect this hippocampal activity pattern. The paper by Tikidji-Hamburyan et al. discusses the interplay of distinct rhythms and the effect of heterogeneity and noise on cross-frequency, phase-phase, and phase-amplitude coupling. These coupling mechanisms may be relevant to theta-gamma locking observed in the hippocampus, as well as in many other instances of cross frequency coupling in the brain. Gamma frequency feedback inhibition in V1 is at the center of the paper by Lisman. Here it is argued that the recently discovered change of orientation tuning with gamma phase can be explained by simple winner-take-all like rule that describes the effect of inhibition on the population of principal cells. Again addressing the visual cortex, a paradigm to use neuronal oscillators for perceptual grouping of visual scenes is suggested in the paper by Meier et al. There excitatory and inhibitory couplings between the oscillators result in distinct phase entrained subpopulations that identify distinct visual objects. Finally, a paper by Masquelier reviews a general paradigm by which, thanks to oscillations, slowly changing stimuli can be encoded in precise relative spike times. Information transmitted in the relative spike times could then be extracted by downstream neurons if the system is equipped with Spike-Timing-Dependent Plasticity (STDP).
The papers in this issue cover a broad selection of research trends in the field of computation of oscillating neural networks, with some bias on the hippocampus. We feel there is considerable potential that many of the ideas will inspire new experimental approaches.