T Achakulvisut, T Ruangrong, P Mineault, TP Vogels, MAK Peters, P Poirazi, C Rozell, B Wyble, DFM Goodman, KP Kording. Towards Democratizing and Automating Online Conferences: Lessons from the Neuromatch Conferences. Trends Cogn. Sci, 2020.
DW Jia, RP Costa, TP Vogels. Developmental depression-facilitation shift controls excitation-inhibition balance. bioRxiv, 2020.
EJ Agnes, AI Luppi, TP Vogels. Complementary Inhibitory Weight Profiles Emerge from Plasticity and Allow Flexible Switching of Receptive Fields. J. Neurosci., 2020.
PA Bozelos, TP Vogels. Talking science, online. Nat. Neurosci., 2020.
PJ Gonçalves, JM Lueckmann, M Deistler, M Nonnenmacher, K Öcal, G Bassetto, C Chintaluri, WF Podlaski, SA Haddad, TP Vogels, DS Greenberg, JH Macke. Training deep neural density estimators to identify mechanistic models of neural dynamics. eLife, 2020.
PA Bozelos, TP Vogels. Making (neuro) science accessible world-wide: Online seminars for the globe. eLife Innovation, 2020.
B Confavreux, F Zenke, E Agnes, T Lillicrap, TP Vogels. A meta-learning approach to (re) discover plasticity rules that carve a desired function into a neural network. Adv. Neural Inf. Process. Syst., 2020.
F Zenke, TP Vogels. The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks. bioRxiv, 2020.
WF Podlaski, EJ Agnes, TP Vogels. Context-modular memory networks support high-capacity, flexible, and robust associative memories. bioRxiv, 2020.
CB Currin, PN Khoza, AD Antrobus, PE Latham, TP Vogels, JV Raimondo. Think: Theory for Africa. PLoS Comput. Biol., 2019.
SG Manohar, N Zokaei, SJ Fallon, TP Vogels, M Husain. Neural mechanisms of attending to items in working memory. Neurosci. Biobehav. Rev., 2019.
JP Stroud, MA Porter, G Hennequin, TP Vogels. Motor primitives in space and time via targeted gain modulation in cortical networks. Nat. Neurosci, 2018.
JP Stroud, TP Vogels. Cortical Signal Propagation: Balance, Amplify, Transmit. Neuron, 2018.
SG Manohar, N Zokaei, SJ Fallon, TP Vogels, M. Husain. A neural model of working memory. bioRxiv, 2017.
RP Costa, IAM Assael, B Shillingford, N de Freitas, TP Vogels. Cortical microcircuits as gated-recurrent neural networks. Adv. Neural Inf. Process. Syst., 2017.
RP Costa, Z Padamsey, JA D’Amour, NJ Emptage, RC Froemke, TP Vogels. Synaptic Transmission Optimization Predicts Expression Loci of Long-Term Plasticity. Neuron, 2017.
G Hennequin, EJ Agnes, TP Vogels. Inhibitory Plasticity: Balance, Control, and Codependence. Annu. Rev. Neurosci., 2017.
HC Barron, TP Vogels, TE Behrens, M Ramaswami. Inhibitory engrams in perception and memory. Proc. Natl. Acad. Sci. U.S.A., 2017.
TP Vogels, LC Griffith. Editorial overview: Neurobiology of learning and plasticity 2017. Curr. Opin. Neurobiol., 2017.
WF Podlaski, A Seeholzer, LN Groschner, G Miesenböck, R Ranjan, TP Vogels. Mapping the function of neuronal ion channels in model and experiment. eLife, 2017.
C Clopath, TP Vogels, RC Froemke, H Sprekeler. Receptive field formation by interacting excitatory and inhibitory synaptic plasticity. bioRxiv, 2016.
HC Barron, TP Vogels, UE Emir, TR Makin, J O’Shea, S Clare, S Jbabdi, RJ Dolan, TEJ Behrens. Unmasking Latent Inhibitory Connections in Human Cortex to Reveal Dormant Cortical Memories. Neuron, 2016.
C Tomm, M Avermann, C Petersen, W Gerstner, TP Vogels. Connection-type-specific biases make uniform random network models consistent with cortical recordings. J. Neurophysiol., 2014.
G Hennequin, TP Vogels*, W Gerstner*. Optimal control of transient dynamics in balanced networks supports generation of complex movements. Neuron, 2014.
R Araya, TP Vogels, R Yuste. Activity-dependent dendritic spine neck changes are correlated with synaptic strength. Proc. Natl. Acad. Sci. U.S.A., 2014.
TP Vogels, RC Froemke, N Doyon, M Gilson, JS Haas, R Liu, A Maffei, P Miller, CJ Wierenga, MA Woodin, F Zenke, H Sprekeler. Inhibitory synaptic plasticity: spike timing-dependence and putative network function. Front. Neural Circuits, 2013.
G Hennequin, TP Vogels, W Gerstner. Non-normal amplification in random balanced neuronal networks. Phys. Rev. E, 2012.
TP Vogels*, H Sprekeler*, F Zenke, C Clopath, W Gerstner. Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks. AAAS, 2011.
AR Woodruff, LM McGarry, TP Vogels, M Inan, SA Anderson, R Yuste. State-Dependent Function of Neocortical Chandelier Cells. J. Neurosci., 2011.
TP Vogels, LF Abbott. Gating multiple signals through detailed balance of excitation and inhibition in spiking networks. Nat. Neurosci., 2009.
TP Vogels, LF Abbott. Gating deficits in model networks: a path to schizophrenia?. Pharmacopsychiatry, 2007.
TP Vogels, K Rajan, LF Abbott. Neural network dynamics. Annu. Rev. Neurosci., 2005.
TP Vogels, LF Abbott. Signal propagation and logic gating in networks of integrate-and-fire neurons. J. Neurosci., 2005.
JP Stroud, et al., Nature Neuroscience, 2018.
Matlab code for creating ‘stability-optimised circuits’ and learning new network outputs through gain modulation. Available at:
RP Costa, et al., Neuron, 2017.
A graphical interface for the model can be accessed at ModelDB.
The datasets analyzed and respective estimations reported in this paper have been deposited to Mendeley Data and are available at:
RP Costa, et al., Adv. Neural Inf. Process. Syst., 2017.
3rd party code implementation available at GitHub.
WF Podlaski, et al., eLife, 2017.
ICGenealogy code packages are available at GitHub.
TP Vogels, et al., AAAS, 2011.
R Brette, et al., Journal of computational neuroscience, 2007.
TP Vogels, et al., Journal of neuroscience, 2005.
This package provides a series of codes that simulate networks of spiking neurons (excitatory and inhibitory, integrate-and-fire or Hodgkin-Huxley type, current-based or conductance-based synapses; some of them are event-based). The same networks are implemented in different simulators (NEURON, GENESIS, NEST, NCS, CSIM, XPP, SPLIT, MVAspike; there is also a couple of implementations in SciLab and C++).