| Download | - View author's version: Connecting biological detail with neural computation: application to the cerebellar Granule–Golgi microcircuit (PDF, 1.6 MiB)
|
|---|
| DOI | Resolve DOI: https://doi.org/10.1111/tops.12536 |
|---|
| Author | Search for: Stöckel, Andreas; Search for: Stewart, Terrence C.1; Search for: Eliasmith, Chris |
|---|
| Affiliation | - National Research Council of Canada. Digital Technologies
|
|---|
| Format | Text, Article |
|---|
| Conference | “Best of Papers" from the 18th International Conference on Cognitive Modeling, July 20-31, 2021, Held Online |
|---|
| Subject | biologically plausible spiking neural networks; Dale's principle; Neural Engineering Framework; delay network; cerebellum; NengoBio |
|---|
| Abstract | 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. |
|---|
| Publication date | 2021-06-19 |
|---|
| Publisher | Wiley |
|---|
| In | |
|---|
| Related data | |
|---|
| Language | English |
|---|
| Peer reviewed | Yes |
|---|
| Export citation | Export as RIS |
|---|
| Report a correction | Report a correction (opens in a new tab) |
|---|
| Record identifier | b4cdb505-e740-4ea9-b254-50f54621185e |
|---|
| Record created | 2021-09-10 |
|---|
| Record modified | 2021-09-13 |
|---|
