Baladron J, Nambu A, Hamker FH. (2019). The subthalamic nucleus-external globus pallidus loop biases exploratory decisions towards known alternatives: a neuro-computational study. The European journal of neuroscience. 49 [PubMed]

Chadderdon GL, Neymotin SA, Kerr CC, Lytton WW. (2012). Reinforcement learning of targeted movement in a spiking neuronal model of motor cortex. PloS one. 7 [PubMed]

• Reinforcement learning of targeted movement (Chadderdon et al. 2012) [Model]

Clopath C, Pedrosa V. (2017). The role of neuromodulators in cortical plasticity. A computational perspective. Front. Synaptic Neurosci.. 8

• A simple model of neuromodulatory state-dependent synaptic plasticity (Pedrosa and Clopath, 2016) [Model]

Cui Y et al. (2016). Endocannabinoid dynamics gate spike-timing dependent depression and potentiation. eLife. 5 [PubMed]

• Endocannabinoid dynamics gate spike-timing dependent depression and potentiation (Cui et al 2016) [Model]

Evans RC et al. (2012). The effects of NMDA subunit composition on calcium influx and spike timing-dependent plasticity in striatal medium spiny neurons. PLoS computational biology. 8 [PubMed]

• NMDA subunit effects on Calcium and STDP (Evans et al. 2012) [Model]

Gurney KN, Humphries MD, Redgrave P. (2015). A new framework for cortico-striatal plasticity: behavioural theory meets in vitro data at the reinforcement-action interface. PLoS biology. 13 [PubMed]

• Cortico-striatal plasticity in medium spiny neurons (Gurney et al 2015) [Model]

Hjorth J, Blackwell KT, Kotaleski JH. (2009). Gap junctions between striatal fast-spiking interneurons regulate spiking activity and synchronization as a function of cortical activity. The Journal of neuroscience : the official journal of the Society for Neuroscience. 29 [PubMed]

• Gap junction coupled network of striatal fast spiking interneurons (Hjorth et al. 2009) [Model]

Humphries MD, Lepora N, Wood R, Gurney K. (2009). Capturing dopaminergic modulation and bimodal membrane behaviour of striatal medium spiny neurons in accurate, reduced models. Frontiers in computational neuroscience. 3 [PubMed]

• Dopamine-modulated medium spiny neuron, reduced model (Humphries et al. 2009) [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]

Kim B, Hawes SL, Gillani F, Wallace LJ, Blackwell KT. (2013). Signaling pathways involved in striatal synaptic plasticity are sensitive to temporal pattern and exhibit spatial specificity. PLoS computational biology. 9 [PubMed]

• Gq coupled signaling pathways involved in striatal synaptic plasticity (Kim et al. 2013) [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]

Morita K, Kato A. (2014). Striatal dopamine ramping may indicate flexible reinforcement learning with forgetting in the cortico-basal ganglia circuits. Frontiers in neural circuits. 8 [PubMed]

• Striatal dopamine ramping: an explanation by reinforcement learning with decay (Morita & Kato, 2014) [Model]

Moustafa AA, Cohen MX, Sherman SJ, Frank MJ. (2008). A role for dopamine in temporal decision making and reward maximization in parkinsonism. The Journal of neuroscience : the official journal of the Society for Neuroscience. 28 [PubMed]

Nakano T, Yoshimoto J, Doya K. (2013). A model-based prediction of the calcium responses in the striatal synaptic spines depending on the timing of cortical and dopaminergic inputs and post-synaptic spikes. Frontiers in computational neuroscience. 7 [PubMed]

• Calcium response prediction in the striatal spines depending on input timing (Nakano et al. 2013) [Model]

Neymotin SA, Chadderdon GL, Kerr CC, Francis JT, Lytton WW. (2013). Reinforcement learning of two-joint virtual arm reaching in a computer model of sensorimotor cortex. Neural computation. 25 [PubMed]

• Sensorimotor cortex reinforcement learning of 2-joint virtual arm reaching (Neymotin et al. 2013) [Model]

Salimi-Badr A, Ebadzadeh MM, Darlot C. (2017). A possible correlation between the basal ganglia motor function and the inverse kinematics calculation. Journal of computational neuroscience. 43 [PubMed]

• Basal ganglia motor function and the inverse kinematics calculation (Salimi-Badr et al 2017) [Model]

Ursino M, Baston C. (2018). Aberrant learning in Parkinson's disease: A neurocomputational study on bradykinesia. The European journal of neuroscience. 47 [PubMed]

• A basal ganglia model of aberrant learning (Ursino et al. 2018) [Model]

Ursino M et al. (2020). Mathematical modeling and parameter estimation of levodopa motor response in patients with parkinson disease. PloS one. 15 [PubMed]

• Basal Ganglia and Levodopa Pharmacodynamics model for parameter estimation in PD (Ursino et al 2020) [Model]