Stimulus detection rate and latency, firing rates and 1-40Hz oscillatory power are modulated by infra-slow fluctuations in a bistable attractor network model
2013 (English)In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 83, 458-471 p.Article in journal (Refereed) Published
Recordings of membrane and field potentials, firing rates, and oscillation amplitude dynamics show that neuronal activity levels in cortical and subcortical structures exhibit infra-slow fluctuations (ISFs) on time scales from seconds to hundreds of seconds. Similar ISFs are salient also in blood-oxygenation-level dependent (BOLD) signals as well as in psychophysical time series. Functional consequences of ISFs are not fully understood. Here, they were investigated along with dynamical implications of ISFs in large-scale simulations of cortical network activity. For this purpose, a biophysically detailed hierarchical attractor network model displaying bistability and operating in an oscillatory regime was used. ISFs were imposed as slow fluctuations in either the amplitude or frequency of fast synaptic noise. We found that both mechanisms produced an ISF component in the synthetic local field potentials (LFPs) and modulated the power of 1-40. Hz oscillations. Crucially, in a simulated threshold-stimulus detection task (TSDT), these ISFs were strongly correlated with stimulus detection probabilities and latencies. The results thus show that several phenomena observed in many empirical studies emerge concurrently in the model dynamics, which yields mechanistic insight into how infra-slow excitability fluctuations in large-scale neuronal networks may modulate fast oscillations and perceptual processing. The model also makes several novel predictions that can be experimentally tested in future studies.
Place, publisher, year, edition, pages
2013. Vol. 83, 458-471 p.
Attractor network, Computational model, Detection rate, Oscillation, Slow fluctuation, Threshold-stimulus detection task
Bioinformatics (Computational Biology)
IdentifiersURN: urn:nbn:se:su:diva-93436DOI: 10.1016/j.neuroimage.2013.06.080ISI: 000326953700042OAI: oai:DiVA.org:su-93436DiVA: diva2:646593