Isolating Single Cycles of Neural Oscillations in Spiking Activity

Abstract

Neural oscillations are prominent features of brain activity, observable through frequency-specific power changes in electroencephalograms (EEG) and local field potentials (LFP). They also manifest as rhythmic coherence across brain regions. Although the identification of oscillations has primarily relied on EEG and LFP, the intrinsic relation between neural oscillations and neuronal spiking is noteworthy. We investigate the potential to detect individual cycles of neural rhythms solely through the spiking activity of neurons, leveraging recent advances in densely recording large populations of neurons within a local network. The numerous spikes from many neurons within a local network estimate the networks activity over time, enabling the identification of cyclic patterns. Here, we utilize a Long Short Term Memory (LSTM) network to effectively isolate and align individual cycles of neural oscillations from the spiking of a densely recorded population of neurons. We applied this network to robustly isolate specific cycles in different brain regions of mice across different time scales, from gamma to ultra-slow rhythms spanning durations of up to hundreds of seconds. These ultra-slow rhythms, which are usually cut off in the LFP, are also detected in behavioral measures of arousal, such as pupil size and mouse facial motion, and show delayed coherence with corresponding rhythms in the population spiking. We used the isolated neural cycles to investigate two questions: 1) With isolated gamma cycles driven by sensory input, we achieved a more precise alignment of the trials in sensory stimulation experiments in the primary visual cortex (V1) of mice. This alignment compensates for the biological variation in the transmission times of sensory signals from the retina to V1 across trials. As a result, we retrieve more accurate neural dynamics in response to sensory stimulation. 2) We used the distinct cycles in population spiking of simultaneously recorded regions to measure the correlated spiking across brain regions separately on different time scales. We observed that the delay in population spiking between brain regions varies according to brain regions and different time scales.