Speaker
Description
A model for NMDA-receptor-mediated synaptic currents generating persistent activity proposed by Wang and Brunel [1-3] has been widely adopted in computational neuroscience, both for spiking-neuron and mean-field models [1-4]. The model describes synaptic dynamics by a phenomenological two-dimensional nonlinear ODE system for the gating variable S(t). Due to the nonlinearity, the pre-synaptic gating variables of a post-synaptic neuron cannot be simulated in aggregated form. Numerically efficient solutions are only feasible for fully connected networks with identical, short delays (see e.g. [5]).
We derive a linear approximation to Wang’s model which allows us to integrate all NMDA input currents to a neuron in aggregate form as for linear synapses. Using a reference implementation in NEST, we show that the approximation is accurate and that a network model based on the approximation shows the same decision making dynamics as one using Wang’s original model. For an example network with around 8000 neurons, the approximation is about 30 times faster, and scales sublinearly with the number of synapses.
Exploiting the flexibility and performance gained through the approximation, we investigate the dynamics of a binary decision-making network with sparse connectivity and randomized delays.
References
[1]: Wang, X.-J. (1999). Synaptic Basis of Cortical Persistent Activity: The Importance of NMDA Receptors to Working Memory. Journal of Neuroscience, 19(21), 9587–9603. https://doi.org/10.1523/JNEUROSCI.19-21-09587.1999
[2]: Brunel, N., & Wang, X.-J. (2001). Effects of Neuromodulation in a Cortical Network Model of Object Working Memory Dominated by Recurrent Inhibition. Journal of Computational Neuroscience, 11(1), 63–85. https://doi.org/10.1023/A:1011204814320
[3]: Wang, X.-J. (2002). Probabilistic Decision Making by Slow Reverberation in Cortical Circuits. Neuron, 36(5), 955-968. https://doi.org/10.1016/S0896-6273(02)01092-9
[4]: Deco, G., & Jirsa, V. K. (2012). Ongoing Cortical Activity at Rest: Criticality, Multistability, and Ghost Attractors. The Journal of Neuroscience, 32(10), 3366–3375. https://doi.org/10.1523/JNEUROSCI.2523-11.2012
Acknowledgements
Acknowledgements
This project has received funding from the European Union’s Horizon 2020 Framework Program for Research and Innovation under Specific Grant Agreement 945539 (HBP SGA3). HEP is grateful to the Käte Hamburger Kolleg: Cultures of Research (c:o/re) at RWTH Aachen for financial support and hospitality (funded by the Federal Ministry of Education and Research under funding code 01UK2104) and to NMBU for sabbatical leave.
| Preferred form of presentation | Talk (& optional poster) |
|---|---|
| Topic area | Models and applications |
| Keywords | NMDA, leaky integrate-and-fire |
| Speaker time zone | UTC+2 |
| I agree to the copyright and license terms | Yes |
| I agree to the declaration of honor | Yes |
