Frances Skinner

Abstract Despite being prevalent, the causes, mechanisms, and progression of epilepsy, a chronic neurological disorder with unprovoked seizures, are not well understood, complicating drug development for treatment. This study used a comprehensive mouse epilepsy kindling model dataset to investigate frequency modulation (chirp) as a potential indicator of distinct states of epilepsy (early evoked discharge, late evoked discharge, spontaneous recurrent seizure, and drug state).

Abstract The distinct inhibitory cell types that participate in cognitively relevant nested brain rhythms require that model circuits reflect this diversity. The co-expression of theta and gamma rhythms may represent a general coding scheme and particular changes in these coupled rhythms occur in disease states.

The amount of data that can be gathered about the human brain has been growing exponentially in recent years, but it could be argued that relatively little progress has been made in actually understanding how the brain works. While there may be sociological and philosophical reasons for this lack of progress (see, for example, Thompson, 2021), a main reason is the low level of interactions between the experimental and theoretical/modeling communities in neuroscience (Marder, 2015).