Neural network oscillations are rhythmic patterns of activity in artificial or biological neural networks.Neural network oscillations are periodic bursts of total electrical activity of large populations of neurons. They arise due to mutual excitation and inhibition between cells. Rhythms are distinguished by frequency: delta (0.5–4 Hz) — deep sleep, theta (4–8 Hz) — drowsy state, alpha (8–13 Hz) — relaxed wakefulness with eyes closed, beta (13–30 Hz) — active attention, gamma (30–100 Hz) — information processing. These oscillations are not just noise, but a functional tool: they synchronize remote areas for data exchange (the binding problem), as if two computers tuned to a common carrier frequency.
History began with the invention of electroencephalography. Hans Berger performed the first human EEG recording in 1924, but recognition came only after 1934 when Edgar Adrian repeated the experiments. Berger described alpha and beta rhythms. In the 1930s–50s, William Grey Walter and others studied the delta rhythm in brain tumors. In the 1960s, computer analysis of EEG allowed the extraction of evoked potentials. A breakthrough was the discovery of local field potentials (LFP) and the link between oscillations and cognitive processes: in the 1980s, Wolf Singer proposed the synchronization hypothesis for feature binding. Today, optogenetics and modern imaging methods allow targeted manipulation of oscillations.
Oscillations are maintained by a balance of excitation (via glutamatergic neurons) and inhibition (via GABAergic interneurons). The key mechanism is reverberating circuits: excitation from one group of neurons activates inhibitory cells, which after a short delay suppress the first group, after which the cycle repeats. The frequency of oscillations is determined by the time constants of synapses and the length of chains. For a global rhythm to arise, synchronization through gap junctions (electrical synapses) and a common synchronizing projection from subcortical structures, such as the thalamus, is necessary. Gamma rhythm (30–80 Hz) is locally generated by pyramidal neurons and fast-spiking interneurons, which create a temporal window for precise spike synchronization. Theta rhythm coordinates the interaction between the hippocampus and cortex, organizing temporal compression of sequences. From the perspective of nonlinear dynamics, the transition to a synchronized state is a phase transition described by order parameters.
Reading brain rhythms (EEG) helps diagnose epilepsy, control prostheses through brain-computer interfaces, and develop therapies for attention disorders.
In newborns, the alpha rhythm is almost absent and only develops by the age of three, while slow waves of deep sleep help 'wash out' toxic proteins accumulated during the day from the brain.













