Emergence of self-sustaining states in the two-population neural field model triggered by a comprehensive external input

In this work, a two-population neural field (TPNF) model is investigated under the influence of a more comprehensive spatiotemporal external input. The symmetric and stationary solutions (bump solutions) of the TPNF model serve as persistent activity states (working memory) in the brain. The emergence of these persistent activities in TPNF model is investigated under the influence of external inputs with an emphasis on temoral evolution. Earlier studies have used triangular and smooth alpha-type spatiotemporal external inputs, derived from experimental observations to study the emergence of bump solutions. In this work, we formulated a more comprehensive spatiotemporal external input (piecewise) that retains the characteristics of earlier inputs with some additional features that makes it more comprehensive. It is found that relative inhibition time constant \(\tau\) is very significant in shaping the emergence of self-sustained activity states. Specific phases of the external input play crucial role for evoking these network activity states, e.g., total duration, amplitude, stimulus onset and peak time duration of the external input. It is discovered that the minimal amplitude and active duration to evoke network activity are lower than those found in earlier studies. The results in this work are computed numerically using RK-4 method.