Not the original poster, but: - Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems - Peter Dayan, L. F. Abbott (2001) is quite

Not the original poster, but: - Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems - Peter Dayan, L. F. Abbott (2001) is quite good, more mathematical than computational. - Neuronal dynamics, available here: https://neuronaldynamics.epfl.ch/ https://neuronaldynamics.epfl.ch/ is also quite good, and free to read. Has python exercises as well. If I recall correctly, it mostly goes into simulations of singular neurons, and not so much entire networks and what we can do with them, but it does a good job at bridging the chemistry / biology / math to computation. If we're talking about papers, one I mentioned in my other comment: - Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations, https://doi.org/10.1162/089976602760407955 https://doi.org/10.1162/089976602760407955 - Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons, by Nicolas Brunel (Don't have a DOI on hand for this one) - Spiking Neural Networks and Their Applications: A Review, https://doi.org/10.3390/brainsci12070863 https://doi.org/10.3390/brainsci12070863 , is a very nice review of methods and does some nice explaining on concepts. If you're looking for keywords on the topic: - Leaky Integrate and Fire (LIF) neurons - Spiking neural networks - Liquid State Machines (LSM) - Synaptic plasticity (Models of synaptic plasticity) - Spike-based synaptic plasticity