Analytical insights on theta-gamma coupled neural oscillators

International audienceIn this paper we study the dynamics of a quadratic integrate-and-fire neuron, spiking in the gamma (30-100 Hz) range, coupled to a delta/theta frequency (1-8 Hz) neural oscillator. Using analytical and semi-analytical methods we were able to derive characteristic spiking times for the system in two distinct regimes (depending on parameter values): one regime where the gamma neuron is intrinsically oscillating in the absence of theta input, and a second one in which gamma spiking is directly gated by theta input, i.e. windows of gamma activity alternate with silence periods depending on the underlying theta phase. In the former case we transform the equations such that the system becomes analogous to the Mathieu differential equation. By solving this equation we can compute numerically the time to the first gamma spike and then use singular perturbation theory to find successive spike times. On the other hand in the excitable condition we make direct use of singular perturbation theory to obtain an approximation of the time to first gamma spike, and then extend the result to calculate ensuing gamma spikes in a recursive fashion. We thereby give explicit formulas for the onset and offset of gamma spike burst during a theta cycle, and provide an estimation of the total number of spikes per theta cycle both for excitable and oscillator regimes

Neuromatch Academy: a 3-week, online summer school in computational neuroscience

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function

θ-Band and β-Band Neural Activity Reflects Independent Syllable Tracking and Comprehension of Time-Compressed Speech

Recent psychophysics data suggest that speech perception is not limited by the capacity of the auditory system to encode fast acoustic variations through neural γ activity, but rather by the time given to the brain to decode them. Whether the decoding process is bounded by the capacity of θ rhythm to follow syllabic rhythms in speech, or constrained by a more endogenous top-down mechanism, e.g., involving β activity, is unknown. We addressed the dynamics of auditory decoding in speech comprehension by challenging syllable tracking and speech decoding using comprehensible and incomprehensible time-compressed auditory sentences. We recorded EEGs in human participants and found that neural activity in both θ and γ ranges was sensitive to syllabic rate. Phase patterns of slow neural activity consistently followed the syllabic rate (4-14 Hz), even when this rate went beyond the classical θ range (4-8 Hz). The power of θ activity increased linearly with syllabic rate but showed no sensitivity to comprehension. Conversely, the power of β (14-21 Hz) activity was insensitive to the syllabic rate, yet reflected comprehension on a single-trial basis. We found different long-range dynamics for θ and β activity, with β activity building up in time while more contextual information becomes available. This is consistent with the roles of θ and β activity in stimulus-driven versus endogenous mechanisms. These data show that speech comprehension is constrained by concurrent stimulus-driven θ and low-γ activity, and by endogenous β activity, but not primarily by the capacity of θ activity to track the syllabic rhythm.SIGNIFICANCE STATEMENT Speech comprehension partly depends on the ability of the auditory cortex to track syllable boundaries with θ-range neural oscillations. The reason comprehension drops when speech is accelerated could hence be because θ oscillations can no longer follow the syllabic rate. Here, we presented subjects with comprehensible and incomprehensible accelerated speech, and show that neural phase patterns in the θ band consistently reflect the syllabic rate, even when speech becomes too fast to be intelligible. The drop in comprehension, however, is signaled by a significant decrease in the power of low-β oscillations (14-21 Hz). These data suggest that speech comprehension is not limited by the capacity of θ oscillations to adapt to syllabic rate, but by an endogenous decoding process

The contribution of frequency-specific activity to hierarchical information processing in the human auditory cortex

The fact that feed-forward and top-down propagation of sensory information use distinct frequency bands is an appealing assumption for which evidence remains scarce. Here we obtain human depth recordings from two auditory cortical regions in both hemispheres, while subjects listen to sentences, and show that information travels in each direction using separate frequency channels. Bottom-up and top-down propagation dominates in γ- and δ-β (<40 Hz) bands, respectively. The predominance of low frequencies for top-down information transfer is confirmed by cross-regional frequency coupling, which indicates that the power of γ-activity in A1 is modulated by the phase of δ-β activity sampled from association auditory cortex (AAC). This cross-regional coupling effect is absent in the opposite direction. Finally, we show that information transfer does not proceed continuously but by time windows where bottom-up or top-down processing alternatively dominates. These findings suggest that the brain uses both frequency- and time-division multiplexing to optimize directional information transfer