>there is a lot of work on biologically plausible spiking
I ask you kindly to share the list (or even better brief review) of most insightful books/papers in your opinion with neuroscience inspired algorithms concepts/implementation details.
I ask you kindly to share the list (or even better brief review) of most insightful books/papers in your opinion with neuroscience inspired algorithms concepts/implementation details.
- 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/ 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
- 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 , 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
Maass 2002, Real-time computing without stable states: https://pubmed.ncbi.nlm.nih.gov/12433288/
Sussillo & Abbott 2009, Generating Coherent Patterns of Activity from Chaotic Neural Networks https://pmc.ncbi.nlm.nih.gov/articles/PMC2756108/
Abbott et al 2016, Building functional networks of spiking model neurons https://pubmed.ncbi.nlm.nih.gov/26906501/
Zenke & Ganguli 2018, SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks https://ganguli-gang.stanford.edu/pdf/17.superspike.pdf
Bellec et al 2020, A solution to the learning dilemma for recurrent networks of spiking neurons https://www.nature.com/articles/s41467-020-17236-y
Payeur et al 2021, Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits https://www.nature.com/articles/s41593-021-00857-x
Cimesa et al 2023, Geometry of population activity in spiking networks with low-rank structure https://journals.plos.org/ploscompbiol/article?id=10.1371/jo...
Ororbia 2024, Contrastive signal–dependent plasticity: Self-supervised learning in spiking neural circuits https://www.science.org/doi/10.1126/sciadv.adn6076 Kudithipudi et al 2025, Neuromorphic computing at scale (review) https://www.nature.com/articles/s41586-024-08253-8