Connecting Biological Detail With Neural Computation: Application to the Cerebellar Granule-Golgi Microcircuit.
Biologically plausible spiking neural networks
Cerebellum
Dale's principle
Eyeblink conditioning
Legendre delay network
Neural engineering framework
Journal
Topics in cognitive science
ISSN: 1756-8765
Titre abrégé: Top Cogn Sci
Pays: United States
ID NLM: 101506764
Informations de publication
Date de publication:
07 2021
07 2021
Historique:
revised:
21
04
2021
received:
02
11
2020
accepted:
22
04
2021
pubmed:
20
6
2021
medline:
6
11
2021
entrez:
19
6
2021
Statut:
ppublish
Résumé
Neurophysiology and neuroanatomy constrain the set of possible computations that can be performed in a brain circuit. While detailed data on brain microcircuits is sometimes available, cognitive modelers are seldom in a position to take these constraints into account. One reason for this is the intrinsic complexity of accounting for biological mechanisms when describing cognitive function. In this paper, we present multiple extensions to the neural engineering framework (NEF), which simplify the integration of low-level constraints such as Dale's principle and spatially constrained connectivity into high-level, functional models. We focus on a model of eyeblink conditioning in the cerebellum, and, in particular, on systematically constructing temporal representations in the recurrent granule-Golgi microcircuit. We analyze how biological constraints impact these representations and demonstrate that our overall model is capable of reproducing key properties of eyeblink conditioning. Furthermore, since our techniques facilitate variation of neurophysiological parameters, we gain insights into why certain neurophysiological parameters may be as observed in nature. While eyeblink conditioning is a somewhat primitive form of learning, we argue that the same methods apply for more cognitive models as well. We implemented our extensions to the NEF in an open-source software library named "NengoBio" and hope that this work inspires similar attempts to bridge low-level biological detail and high-level function.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
515-533Informations de copyright
© 2021 Cognitive Science Society LLC.
Références
Bekolay, T., Bergstra, J., Hunsberger, E., DeWolf, T., Stewart, T. C., Rasmussen, D., Choo, X., Voelker, A. R., & Eliasmith, C. (2014). Nengo: A Python tool for building large-scale functional brain models. Frontiers in Neuroinformatics, 7, 48.
Buckner, R. L. (2013). The Cerebellum and Cognitive Function: 25 Years of Insight from Anatomy and Neuroimaging. Neuron, 80(3), 807-815.
Chadderton, P., Margrie, T. W., & Häusser, M. (2004). Integration of quanta in cerebellar granule cells during sensory processing. Nature, 428(6985), 856-860.
D'Angelo, E., Solinas, S., Mapelli, J., Gandolfi, D., Mapelli, L., & Prestori, F. (2013). The cerebellar Golgi cell and spatiotemporal organization of granular layer activity. Frontiers in Neural Circuits, 7, 93.
de Jong, J., Voelker, A. R., van Rijn, H., Stewart, T. C., & Eliasmith, C. (2019). Flexible timing with delay networks-The scalar property and neural scaling. In 17th annual meeting of the international conference on cognitive modelling (ICCM). Austin, TX: Cognitive Science Society.
Dean, P., Porrill, J., Ekerot, C.-F., & Jörntell, H. (2010). The cerebellar microcircuit as an adaptive filter: Experimental and computational evidence. Nature Reviews Neuroscience, 11(1), 30-43.
Dieudonné, S. (1998). Submillisecond kinetics and low efficacy of parallel fibre-Golgi cell synaptic currents in the rat cerebellum. Journal of Physiology, 510(3), 845-866.
Eliasmith, C. (2013). How to build a brain: A neural architecture for biological cognition. Oxford Series on Cognitive Models and Architectures. Oxford, England: Oxford University Press.
Eliasmith, C., & Anderson, C. H. (2003). Neural engineering: Computation, representation, and dynamics in neurobiological systems. Cambridge, MA: MIT Press.
Eliasmith, C., & Kolbeck, C. (2015). Marr's attacks: On reductionism and vagueness. topiCS, 7, 1-13.
Fujita, M. (1982). Adaptive filter model of the cerebellum. Biological Cybernetics, 45(3), 195-206.
Heiney, S. A., Wohl, M. P., Chettih, S. N., Ruffolo, L. I., & Medina, J. F. (2014). Cerebellar-dependent expression of motor learning during eyeblink conditioning in head-fixed mice. Journal of Neuroscience, 34(45), 14845-14853.
Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359-366.
Ito, M. (2010). Cerebellar cortex. In G. Shepherd & S. Grillner (Eds.), Handbook of brain microcircuits (1st ed., pp. 293-300). Oxford, England: Oxford University Press.
Jakab, R. L., & Hámori, J. (1988). Quantitative morphology and synaptology of cerebellar glomeruli in the rat. Anatomy and Embryology, 179(1), 81-88.
Johansson, F., Jirenhed, D.-A., Rasmussen, A., Zucca, R., & Hesslow, G. (2014). Memory trace and timing mechanism localized to cerebellar Purkinje cells. Proceedings of the National Academy of Sciences of the United States of America, 111(41), 14930-14934. https://doi.org/10.1073/pnas.1415371111
Kanichay, R. T., & Silver, R. A. (2008). Synaptic and cellular properties of the feedforward inhibitory circuit within the input layer of the cerebellar cortex. Journal of Neuroscience, 28(36), 8955-8967.
Keren-Happuch, E., Chen, S.-H. A., Ho, M.-H. R., & A (2014). A meta-analysis of cerebellar contributions to higher cognition from PET and fMRI studies. Human Brain Mapping, 35(2), 593-615.
Korbo, L., Andersen, B. B., Ladefoged, O., & Møller, A. (1993). Total numbers of various cell types in rat cerebellar cortex estimated using an unbiased stereological method. Brain Research, 609(1), 262-268.
Llinás, R. R. (2010). Olivocerebellar system. In G. Shepherd & S. Grillner (Eds.), Handbook of brain microcircuits (1st ed.). Oxford, England: Oxford University Press.
MacNeil, D., & Eliasmith, C. (2011). Fine-tuning and the stability of recurrent neural networks. PLoS One, 6(9), e22885.
Marr, D. (1969). A theory of cerebellar cortex. Journal of Physiology, 202(2), 437-470.
Marr, D., & Poggio, T. (1976). From understanding computation to understanding neural circuitry (AI Memo 357). Cambridge, MA: Massachusetts Institute of Technology.
McCormick, D. A., Lavond, D. G., Clark, G. A., Kettner, R. E., Rising, C. E., & Thompson, R. F. (1981). The engram found? Role of the cerebellum in classical conditioning of nictitating membrane and eyelid responses. Bulletin of the Psychonomic Society, 18(3), 103-105.
O'Reilly, J. X., Mesulam, M. M., & Nobre, A. C. (2008). The cerebellum predicts the timing of perceptual events. Journal of Neuroscience, 28(9), 2252-2260.
Palkovits, M., Magyar, P., & Szentágothai, J. (1972). Quantitative histological analysis of the cerebellar cortex in the cat. IV. Mossy fiber-purkinje cell numerical transfer. Brain Research, 45(1), 15-29. https://doi.org/10.1016/0006-8993(72)90213-2
Parisien, C., Anderson, C. H., & Eliasmith, C. (2008). Solving the problem of negative synaptic weights in cortical models. Neural Computation, 20, 1473-1494.
Rössert, C., Dean, P., & Porrill, J. (2015). At the edge of chaos: How cerebellar granular layer network dynamics can provide the basis for temporal filters. PLoS Computational Biology, 11(10), e1004515.
Stöckel, A. (2021a). Constructing dampened LTI systems generating polynomial bases. Available at: https://arxiv.org/abs/2103.00051
Stöckel, A. (2021b). Discrete function bases and convolutional neural networks. Available at: https://arxiv.org/abs/2103.05609
Stöckel, A. & Eliasmith, C. (2021). Passive nonlinear dendritic interactions as a computational resource in spiking neural networks. Neural Computation, 33(1), 96-128. https://doi.org/10.1162/neco_a_01338
Stöckel, A., Stewart, T. C., & Eliasmith, C. (2020a). A biologically plausible spiking neural model of eyeblink conditioning in the cerebellum. In 42nd annual meeting of the Cognitive Science Society (pp. 1614-1620). Austin, TX: Cognitive Science Society.
Stöckel, A., Stewart, T. C., & Eliasmith, C. (2020b). Connecting biological detail with neural computation: Application to the cerebellar granule-Golgi microcircuit. In 18th annual meeting of the International Conference on Cognitive Modelling (pp. 277-282). Society for Mathematical Psychology.
Sullivan, E. V. (2010). Cognitive functions of the cerebellum. Neuropsychology Review, 20(3), 227-228.
Voelker, A. R., & Eliasmith, C. (2018). Improving spiking dynamical networks: Accurate delays, higher-order synapses, and time cells. Neural Computation, 30(3), 569-609.
Voelker, A. R., Kajić, I., & Eliasmith, C. (2019). Legendre memory units: Continuous-time representation in recurrent neural networks. In Advances in NeurIPS, Vancouver, Canada.