Speaker
Description
In the project ‘MatCo: Material constraints enabling human cognition’, we use neurobiologically informed network models of cognition and language. These networks implement macroscopic cortical areas and their connectivity along with microscopic ones addressing the connectivity, functionality and plasticity of neurons[1]. Such brain constrained models enable studying cognition, natural language and their relationship to basic neuroscience principles and non-cognitive sensorimotor processes. The version discussed here, called ‘MatCo12’, implements 12 fronto-temporo-occipital areas relevant for language and cognition, and offers neurobiological accounts for example for neural changes following sensory deprivation[2] and for the learning of concrete and abstract concepts[3].
The MatCo12 model was built with the FELIX simulator[4,5]. To make MatCo12 accessible to a wider audience and allow for faster and larger simulations, we implemented its core building blocks in NEST[6]. The neuron model is a point neuron with an internal adaption. The Hebbian synaptic learning rule[7] determines weight changes based on low-pass filtered activity of the presynaptic neuron and the membrane potential of the postsynaptic neuron at every time step. Consequently, long-term potentiation or long-term heterosynaptic or homosynaptic depression take place. We present results showing the functionality of Hebbian learning in the NEST implementation and show first results of large-scale network simulations.
Acknowledgements
This work was supported by the European Research Council through the Advanced Grant “Material constraints enabling human cognition, MatCo” (ERC-2019-ADG 883811).
References
[1] Pulvermüller, F., Tomasello, R., Henningsen-Schomers, M. R., & Wennekers, T. (2021). Biological constraints on neural network models of cognitive function. Nat Rev Neurosci, 22(8), 488-502. doi: 10.1038/s41583-021-00473-5
[2] Tomasello, R., Wennekers, T., Garagnani, M., & Pulvermüller, F. (2019). Visual cortex recruitment during language processing in blind individuals is explained by Hebbian learning. Sci Rep, 9(1), 3579. doi: 10.1038/s41598-019-39864-1
[3] Henningsen-Schomers, M.R., Pulvermüller, F. (2021). Modelling concrete and abstract concepts using brain-constrained deep neural networks. Psychological Research. doi: 10.1007/s00426-021-01591-6
[4] Wennekers, T. (1999). Synchronisation und Assoziation in Neuronalen Netzwerken. (PhD thesis, in German). Shaker Verlag, Aachen.
[5] Wennekers, T., Garagnani, M., & Pulvermüller, F. (2006). Language models based on Hebbian cell assemblies. J Physiol Paris, 100, 16-30. doi: 10.1016/j.jphysparis.2006.09.007
[6] https://github.com/sepehrmn/nest-simulator/tree/matco_learning
[7] Artola, A., Bröcher, S., & Singer, W. (1990). Different voltage-dependent thresholds for inducing long-term depression and long-term potentiation in slices of rat visual cortex. Nature, 347, 69-72. doi: 10.1038/347069a0
| Preferred form of presentation | Poster & advertising flash talk |
|---|---|
| Topic area | models and applications |
| Keywords | Hebbian learning, LTP, LTD, MatCo, natural language, cognition, spiking neural network |
| Speaker time zone | UTC+2 |
| I agree to the copyright and license terms | Yes |
| I agree to the declaration of honor | Yes |
