| Title |
Computational Architecture of the Granular Layer of Cerebellum-Like Structures
|
|---|---|
| Published in |
The Cerebellum, January 2016
|
| DOI | 10.1007/s12311-016-0759-z |
| Pubmed ID | |
| Authors |
Peter Bratby, James Sneyd, John Montgomery |
| Abstract |
In the adaptive filter model of the cerebellum, the granular layer performs a recoding which expands incoming mossy fibre signals into a temporally diverse set of basis signals. The underlying neural mechanism is not well understood, although various mechanisms have been proposed, including delay lines, spectral timing and echo state networks. Here, we develop a computational simulation based on a network of leaky integrator neurons, and an adaptive filter performance measure, which allows candidate mechanisms to be compared. We demonstrate that increasing the circuit complexity improves adaptive filter performance, and relate this to evolutionary innovations in the cerebellum and cerebellum-like structures in sharks and electric fish. We show how recurrence enables an increase in basis signal duration, which suggest a possible explanation for the explosion in granule cell numbers in the mammalian cerebellum. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 20 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 30% |
| Researcher | 4 | 20% |
| Professor | 3 | 15% |
| Student > Master | 2 | 10% |
| Other | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 3 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 4 | 20% |
| Agricultural and Biological Sciences | 3 | 15% |
| Engineering | 3 | 15% |
| Medicine and Dentistry | 2 | 10% |
| Physics and Astronomy | 1 | 5% |
| Other | 2 | 10% |
| Unknown | 5 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 January 2016.
All research outputs
#19,495,804
of 23,975,976 outputs
Outputs from The Cerebellum
#659
of 957 outputs
Outputs of similar age
#294,321
of 402,236 outputs
Outputs of similar age from The Cerebellum
#14
of 17 outputs
Altmetric has tracked 23,975,976 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 957 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 7th percentile – i.e., 7% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 402,236 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.