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#4124. Connecting Biological Detail With Neural Computation: Application to the Cerebellar Granule–Golgi Microcircuit
| September 2026 | publication date |
| Proposal available till | 24-05-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Linguistics and Language; Experimental and Cognitive Psychology; Human-Computer Interaction; Cognitive Neuroscience; Artificial Intelligence; |
| place 1 | place 2 | place 3 | place 4 |
| Free | Free | Free | Free |
| 2350 $ | 1200 $ | 1050 $ | 900 $ |
Contract №4124.1   | Contract №4124.2 | Contract №4124.3 | Contract №4124.4 |
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
Neurophysiology and neuroanatomy constrain the set of possible computations that can be performed in a brain circuit. In this paper, we present multiple extensions to the neural engineering framework (NEF), which simplify the integration of low-level constraints such as Dales 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. 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.
Keywords:
Biologically plausible spiking neural networks; Cerebellum; Dales principle; Eyeblink conditioning; Legendre delay network; Neural engineering framework
Contacts :
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
Neurophysiology and neuroanatomy constrain the set of possible computations that can be performed in a brain circuit. In this paper, we present multiple extensions to the neural engineering framework (NEF), which simplify the integration of low-level constraints such as Dales 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. 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.
Keywords:
Biologically plausible spiking neural networks; Cerebellum; Dales principle; Eyeblink conditioning; Legendre delay network; Neural engineering framework
Contacts :
