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• Discrimination on behavioral time-scales mediated by reaction-diffusion in dendrites (Bhalla 2017) [Model]

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• Sequence learning via biophysically realistic learning rules (Cone and Shouval 2021) [Model]

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• Learning spatiotemporal sequences using recurrent spiking NN that discretizes time (Maes et al 2020) [Model]

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• Single neuron properties shape chaos and signal transmission in random NNs (Muscinelli et al 2019) [Model]

Rössert C, Dean P, Porrill J. (2015). At the Edge of Chaos: How Cerebellar Granular Layer Network Dynamics Can Provide the Basis for Temporal Filters. PLoS computational biology. 11 [PubMed]

• Basis for temporal filters in the cerebellar granular layer (Roessert et al. 2015) [Model]

Spreizer S, Aertsen A, Kumar A. (2019). From space to time: Spatial inhomogeneities lead to the emergence of spatiotemporal sequences in spiking neuronal networks. PLoS computational biology. 15 [PubMed]

• Emergence of spatiotemporal sequences in spiking neuronal networks (Spreizer et al 2019) [Model]

Wilson CJ, Beverlin B, Netoff T. (2011). Chaotic desynchronization as the therapeutic mechanism of deep brain stimulation. Frontiers in systems neuroscience. 5 [PubMed]