Model Exhibits

Models are presented in ngc-learn’s model museum in the form of “exhibits”, which are effectively model-specific walkthroughs and analyses, based on the relevant, referenced publicly available ngc-learn simulation code. (Note that there are more model exhibits in the actual museum repository than the number of detailed walkthroughs presented in the table of contents below.)

Neuroscience Models

• Sparse Coding and Iterative Thresholding (Olshausen & Field; 1996)
• Hierarchical Predictive Coding for Reconstruction (Rao & Ballard; 1999)
• Excitatory-Inhibitory Recurrent Neural Network (Song et al.; 2016)
• The Diehl and Cook Spiking Neuronal Network (Diehl & Cook; 2015)
• Event-based Spike-Timing-Dependent Plasticity (Tavanaei et al.; 2018)
• Reinforcement Learning through a Spiking Controller (Chevtchenko et al.; 2020)

NeuroAI / Neuro-mimetic Models

• Discriminative Predictive Coding (Whittington & Bogacz; 2017)
• Spiking Neural Networks: Learning with Broadcast Feedback Alignment (Samadi et al.; 2017)
• Harmoniums and Contrastive Divergence (Hinton; 1999)
• Sparse Identification of Non-linear Dynamical Systems (SINDy; Brunton et al.; 2016)