-
Notifications
You must be signed in to change notification settings - Fork 0
/
CITATION.cff
66 lines (65 loc) · 2.54 KB
/
CITATION.cff
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: >-
Random Graph Models of a neocortical column in a
rat's brain and their topological statistical
distributions
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Kieran
family-names: Barber
affiliation: 'Department of Mathematics, Uppsala University'
- affiliation: 'Department of Mathematics, Uppsala University'
given-names: Raazesh
family-names: Sainudiin
orcid: 'https://orcid.org/0000-0003-3265-5565'
identifiers:
- type: url
value: 'https://github.com/lamastex/TDAOnABrainNetwork'
description: Source Codes
repository: 'https://github.com/lamastex/TDAOnABrainNetwork/'
abstract: >-
There has been work done over the years to
understand what exactly are the driving forces
behind how the brain functions. Recently, with the
advent of cloud computing and being able to store
large volumes of data, reconstructing neocortical
columns from the brain has become not only
possible, but much easier. The Blue Brain Project
(BBP) created multiple digital reconstructions of a
rat's neocortical microcircuitry that closely
resembles the biological features that include the
numbers, types and densities of neurons and their
synaptic connectivity that followed anatomical and
physiological data obtained from experiments. What
followed were sets of microconnectomes that were
represented as directed graphs. After applying
stimuli to the microconnectomes, realisations of
connectivity were observed. One of these
observations has been identified as the Bio-M
microconnectome (MC). To determine the complexity
of the Bio-M MC, we use topological statistics on
the local level as well as the global level. We
study here, five models, each with an added level
of complexity ranging from the simplest random
graph of \ER to those that account for known
biological characteristics such as
distance-dependence and neocortical layers. From
our models, we observed that there was a lack of
complexity in the connectivity shown at the local
level, however we found surprisingly higher levels
of connectivity on the global level than first
expected. All codes needed to process the data and
implement the models are made available in this
repository under a permissive license.
keywords:
- brain
- rat
- random graph models
- topological data analysis
- statistics
license: Unlicense