Cone I, Shouval HZ. (2021). Learning precise spatiotemporal sequences via biophysically realistic learning rules in a modular, spiking network. eLife. 10 [PubMed]

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

Dorman DB, Jędrzejewska-Szmek J, Blackwell KT. (2018). Inhibition enhances spatially-specific calcium encoding of synaptic input patterns in a biologically constrained model. eLife. 7 [PubMed]

• Striatal Spiny Projection Neuron, inhibition enhances spatial specificity (Dorman et al 2018) [Model]

Jędrzejewska-Szmek J, Damodaran S, Dorman DB, Blackwell KT. (2017). Calcium dynamics predict direction of synaptic plasticity in striatal spiny projection neurons. The European journal of neuroscience. 45 [PubMed]

• Striatal Spiny Projection Neuron (SPN) plasticity rule (Jedrzejewska-Szmek et al 2016) [Model]

Kato A, Morita K. (2016). Forgetting in Reinforcement Learning Links Sustained Dopamine Signals to Motivation. PLoS computational biology. 12 [PubMed]

• Reinforcement Learning with Forgetting: Linking Sustained Dopamine to Motivation (Kato Morita 2016) [Model]

Lindroos R et al. (2018). Basal Ganglia Neuromodulation Over Multiple Temporal and Structural Scales-Simulations of Direct Pathway MSNs Investigate the Fast Onset of Dopaminergic Effects and Predict the Role of Kv4.2. Frontiers in neural circuits. 12 [PubMed]

• Striatal D1R medium spiny neuron, including a subcellular DA cascade (Lindroos et al 2018) [Model]

Lytton WW et al. (2017). Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis. IBM Journal of Research and Development (Computational Neuroscience special issue). 61(2/3)

• Motor system model with reinforcement learning drives virtual arm (Dura-Bernal et al 2017) [Model]