Simulation Environment: Brian 2

  1. A full-scale cortical microcircuit spiking network model (Shimoura et al 2018)
  2. CN bushy, stellate neurons (Rothman, Manis 2003) (Brian 2)
  3. eLIF and mAdExp: energy-based integrate-and-fire neurons (Fardet and Levina 2020)
  4. Excitation Properties of Computational Models of Unmyelinated Peripheral Axons (Pelot et al., 2021)
  5. Healthy and Epileptic Hippocampal Circuit (Aussel et al 2022)
  6. Inhibitory microcircuits for top-down plasticity of sensory representations (Wilmes & Clopath 2019)
  7. LIP and FEF rhythmic attention model (Aussel et al. 2023)
  8. MEC PV-positive fast-spiking interneuron network generates theta-nested fast oscillations
  9. Mesoscopic dynamics from AdEx recurrent networks (Zerlaut et al JCNS 2018)
  10. Model of the hippocampus over the sleep-wake cycle using Hodgkin-Huxley neurons (Aussel et al 2018)
  11. Modeling dendritic spikes and plasticity (Bono and Clopath 2017)
  12. Modelling the effects of short and random proto-neural elongations (de Wiljes et al 2017)
  13. Myelin dystrophy impairs signal transmission and working memory in a multiscale model of the aging prefrontal cortex (Ibañez, Sengupta et al., 2024)
  14. Neuromuscular network model of gut motility (Barth et al 2017)
  15. Physiological noise facilitates multiplexed coding of vibrotactile signals in somatosensory cortex
  16. PING, ING and CHING network models for Gamma oscillations in cortex (Susin and Destexhe 2021)
  17. PLS-framework (Tikidji-Hamburyan and Colonnese 2021)
  18. Response to correlated synaptic input for HH/IF point neuron vs with dendrite (Górski et al 2018)
  19. Robust modulation of integrate-and-fire models (Van Pottelbergh et al 2018)
  20. Single neuron models of four types of L1 mouse Interneurons: Canpy, NGFC, alpha7 and VIP cells
  21. Syn Plasticity Regulation + Information Processing in Neuron-Astrocyte Networks (Vuillaume et al 21)
  22. Time-dependent homeostatic mechanisms underlie Brain-Derived Neurotrophic Factor action on neural circuitry (O'Neill, 2023)
https://brian2.readthedocs.io/en/stable/index.html

"Brian is a simulator for spiking neural networks. It is written in the Python programming language and is available on almost all platforms. We believe that a simulator should not only save the time of processors, but also the time of scientists. Brian is therefore designed to be easy to learn and use, highly flexible and easily extensible. ..."
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