Aussel A, Fiebelkorn IC, Kastner S, Kopell NJ, Pittman-Polletta BR. (2023). Interacting rhythms enhance sensitivity of target detection in a fronto-parietal computational model of visual attention eLife. 12 [PubMed]

• LIP and FEF rhythmic attention model (Aussel et al. 2023) [Model]

Hass J, Hertäg L, Durstewitz D. (2016). A Detailed Data-Driven Network Model of Prefrontal Cortex Reproduces Key Features of In Vivo Activity. PLoS computational biology. 12 [PubMed]

• A detailed data-driven network model of prefrontal cortex (Hass et al 2016) [Model]

Hayut I, Fanselow EE, Connors BW, Golomb D. (2011). LTS and FS inhibitory interneurons, short-term synaptic plasticity, and cortical circuit dynamics. PLoS computational biology. 7 [PubMed]

• Rate model of a cortical RS-FS-LTS network (Hayut et al. 2011) [Model]

Jones SR et al. (2009). Quantitative analysis and biophysically realistic neural modeling of the MEG mu rhythm: rhythmogenesis and modulation of sensory-evoked responses. Journal of neurophysiology. 102 [PubMed]

• Biophysically realistic neural modeling of the MEG mu rhythm (Jones et al. 2009) [Model]

Vierling-Claassen D, Cardin JA, Moore CI, Jones SR. (2010). Computational modeling of distinct neocortical oscillations driven by cell-type selective optogenetic drive: separable resonant circuits controlled by low-threshold spiking and fast-spiking interneurons. Frontiers in human neuroscience. 4 [PubMed]

• Engaging distinct oscillatory neocortical circuits (Vierling-Claassen et al. 2010) [Model]