Topic: chaotic attractors

Networks of interconnected neurons display diverse patterns of activity. Relating these patterns to the structure of the network is a central goal of theoretical neuroscience. Classic neural field and rate models have been powerful tools for this purpose due to their analytical tractability. Here, we show that the recently-developed combinatorial threshold-linear network (CTLN) model is a mean-field theory for excitatory-inhibitory Hawkes networks, with clustered connectivity, in an inhibition-stabilized regime. This mapping allows us to leverage powerful analytical results for CTLN networks

Coherent oscillations of neural activity are ubiquitous across brain spatial and temporal scales and have been associated with the formation of sensory or behavioral representations [1]. Refined experimental techniques report today a new complexity of this oscillatory activity, especially in the gamma (30-100 Hz) band. Indeed, the frequency of these oscillations shows a large variability through time [2], questioning the classical view of γ oscillations being separated into different frequency bands. Oscillations appear instead in bursts characterised by rapid, synchronous neuronal firing. Thi