Computational neuroscience


Ontology type: npg:Subject  | skos:Concept     


Concept Info

NAME

Computational neuroscience

DESCRIPTION

Computational neuroscience is the field of study in which mathematical tools and theories are used to investigate brain function. It can also incorporate diverse approaches from electrical engineering, computer science and physics in order to understand how the nervous system processes information.

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        "@value": "The Nature Subjects Taxonomy is a polyhierarchical categorization of scholarly subject areas which are used for the indexing of content by Springer Nature."
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      {
        "@language": "en", 
        "@value": "Nature Subjects Taxonomy"
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    ], 
    "id": "http://scigraph.springernature.com/ontologies/subjects/", 
    "sdDataset": "onto_subjects", 
    "skos:hasTopConcept": [
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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/ontologies/subjects/computational-neuroscience'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/ontologies/subjects/computational-neuroscience'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/ontologies/subjects/computational-neuroscience'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/ontologies/subjects/computational-neuroscience'


 

This table displays all metadata directly associated to this object as RDF triples.

203 TRIPLES      10 PREDICATES      18 URIs      5 LITERALS

Subject Predicate Object
1 sg:ontologies/subjects/computational-neuroscience sgo:license sg:explorer/license/
2 sgo:sdDataset onto_subjects
3 rdf:type npg:Subject
4 skos:Concept
5 rdfs:label Computational neuroscience
6 skos:altLabel Neuroinformatics
7 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
8 sg:ontologies/subjects/neuroscience
9 skos:definition Computational neuroscience is the field of study in which mathematical tools and theories are used to investigate brain function. It can also incorporate diverse approaches from electrical engineering, computer science and physics in order to understand how the nervous system processes information.
10 skos:inScheme sg:ontologies/subjects/
11 skos:narrower sg:ontologies/subjects/biophysical-models
12 sg:ontologies/subjects/dynamical-systems
13 sg:ontologies/subjects/learning-algorithms
14 sg:ontologies/subjects/network-models
15 sg:ontologies/subjects/neural-decoding
16 sg:ontologies/subjects/neural-encoding
17 skos:prefLabel Computational neuroscience
18 sg:ontologies/subjects/ dcterms:description The Nature Subjects Taxonomy is a polyhierarchical categorization of scholarly subject areas which are used for the indexing of content by Springer Nature.
19 dcterms:title Nature Subjects Taxonomy
20 sgo:sdDataset onto_subjects
21 rdf:type skos:ConceptScheme
22 skos:hasTopConcept sg:ontologies/subjects/DEPRECATED
23 sg:ontologies/subjects/biological-sciences
24 sg:ontologies/subjects/business-and-commerce
25 sg:ontologies/subjects/earth-and-environmental-sciences
26 sg:ontologies/subjects/health-sciences
27 sg:ontologies/subjects/humanities
28 sg:ontologies/subjects/physical-sciences
29 sg:ontologies/subjects/scientific-community-and-society
30 sg:ontologies/subjects/social-science
31 sg:ontologies/subjects/biophysical-models sgo:sdDataset onto_subjects
32 rdf:type npg:Subject
33 skos:Concept
34 rdfs:label Biophysical models
35 skos:broader sg:ontologies/subjects/computational-neuroscience
36 skos:definition A biophysical model is a simulation of a biological system using mathematical formalizations of the physical properties of that system. Such models can be used to predict the influence of biological and physical factors on complex systems.
37 skos:inScheme sg:ontologies/subjects/
38 skos:prefLabel Biophysical models
39 sg:ontologies/subjects/computational-biology-and-bioinformatics sgo:sdDataset onto_subjects
40 rdf:type npg:Subject
41 skos:Concept
42 rdfs:label Computational biology and bioinformatics
43 skos:altLabel Bio-Informatics
44 Bioinformatics
45 Computational Molecular Biology
46 skos:broader sg:ontologies/subjects/biological-sciences
47 skos:definition Computational biology and bioinformatics is an interdisciplinary field that develops and applies computational methods to analyse large collections of biological data, such as genetic sequences, cell populations or protein samples, to make new predictions or discover new biology. The computational methods used include analytical methods, mathematical modelling and simulation.
48 skos:inScheme sg:ontologies/subjects/
49 skos:narrower sg:ontologies/subjects/biochemical-reaction-networks
50 sg:ontologies/subjects/cellular-signalling-networks
51 sg:ontologies/subjects/classification-and-taxonomy
52 sg:ontologies/subjects/communication-and-replication
53 sg:ontologies/subjects/computational-models
54 sg:ontologies/subjects/computational-neuroscience
55 sg:ontologies/subjects/computational-platforms-and-environments
56 sg:ontologies/subjects/data-acquisition
57 sg:ontologies/subjects/data-integration
58 sg:ontologies/subjects/data-mining
59 sg:ontologies/subjects/data-processing
60 sg:ontologies/subjects/data-publication-and-archiving
61 sg:ontologies/subjects/databases
62 sg:ontologies/subjects/functional-clustering
63 sg:ontologies/subjects/gene-ontology
64 sg:ontologies/subjects/gene-regulatory-networks
65 sg:ontologies/subjects/genome-informatics
66 sg:ontologies/subjects/hardware-and-infrastructure
67 sg:ontologies/subjects/high-throughput-screening
68 sg:ontologies/subjects/image-processing
69 sg:ontologies/subjects/literature-mining
70 sg:ontologies/subjects/machine-learning
71 sg:ontologies/subjects/microarrays
72 sg:ontologies/subjects/network-topology
73 sg:ontologies/subjects/phylogeny
74 sg:ontologies/subjects/power-law
75 sg:ontologies/subjects/predictive-medicine
76 sg:ontologies/subjects/probabilistic-data-networks
77 sg:ontologies/subjects/programming-language-and-code
78 sg:ontologies/subjects/protein-analysis
79 sg:ontologies/subjects/protein-design
80 sg:ontologies/subjects/protein-folding
81 sg:ontologies/subjects/protein-function-predictions
82 sg:ontologies/subjects/protein-structure-predictions
83 sg:ontologies/subjects/proteome-informatics
84 sg:ontologies/subjects/quality-control
85 sg:ontologies/subjects/scale-invariance
86 sg:ontologies/subjects/sequence-annotation
87 sg:ontologies/subjects/software
88 sg:ontologies/subjects/standards
89 sg:ontologies/subjects/statistical-methods
90 sg:ontologies/subjects/virtual-drug-screening
91 skos:prefLabel Computational biology and bioinformatics
92 sg:ontologies/subjects/dynamical-systems sgo:sdDataset onto_subjects
93 rdf:type npg:Subject
94 skos:Concept
95 rdfs:label Dynamical systems
96 skos:broader sg:ontologies/subjects/computational-neuroscience
97 sg:ontologies/subjects/systems-biology
98 skos:definition A dynamical system is a particle or ensemble of particles whose state varies over time and thus obeys differential equations involving time derivatives. Analytical resolution of such equations or their integration over time through computer simulation facilitates the prediction of the future behaviour of the system.
99 skos:inScheme sg:ontologies/subjects/
100 skos:prefLabel Dynamical systems
101 sg:ontologies/subjects/learning-algorithms sgo:sdDataset onto_subjects
102 rdf:type npg:Subject
103 skos:Concept
104 rdfs:label Learning algorithms
105 skos:broader sg:ontologies/subjects/computational-neuroscience
106 skos:definition A learning algorithm is a mathematical framework or procedure that calculates the best output given a particular set of data. It does this by updating the calculation based on the difference between the actual and desired output. These algorithms are typically concerned with representation and generalization of the input data.
107 skos:inScheme sg:ontologies/subjects/
108 skos:prefLabel Learning algorithms
109 sg:ontologies/subjects/network-models sgo:sdDataset onto_subjects
110 rdf:type npg:Subject
111 skos:Concept
112 rdfs:label Network models
113 skos:altLabel Connectionist Model
114 Connectionist Models
115 Neural Network (Computer)
116 Neural Network Model
117 Neural Network Models
118 Neural Networks (Computer)
119 Perceptron
120 Perceptrons
121 skos:broader sg:ontologies/subjects/computational-neuroscience
122 skos:definition Network models are a computer architecture, implementable in either hardware or software, meant to simulate biological populations of interconnected neurons. These models, also known as perceptrons or multilayer connectionist models, process information based on the pattern and strength of the connections among the neuron-like units that compose them.
123 skos:inScheme sg:ontologies/subjects/
124 skos:prefLabel Network models
125 sg:ontologies/subjects/neural-decoding sgo:sdDataset onto_subjects
126 rdf:type npg:Subject
127 skos:Concept
128 rdfs:label Neural decoding
129 skos:broader sg:ontologies/subjects/computational-neuroscience
130 skos:definition Neural decoding is the study of what information is available in the electrical activity (action potentials) of individual cells or networks of neurons. Studies of neural decoding aim to identify what stimulus, event, or desired output elicits a particular pattern of neural activity.
131 skos:inScheme sg:ontologies/subjects/
132 skos:prefLabel Neural decoding
133 sg:ontologies/subjects/neural-encoding sgo:sdDataset onto_subjects
134 rdf:type npg:Subject
135 skos:Concept
136 rdfs:label Neural encoding
137 skos:altLabel neural coding
138 skos:broader sg:ontologies/subjects/computational-neuroscience
139 skos:definition Neural encoding is the study of how neurons represent information with electrical activity (action potentials) at the level of individual cells or in networks of neurons. Studies of neural encoding aim to characterize the relationship between sensory stimuli or behavioural output and neural signals.
140 skos:inScheme sg:ontologies/subjects/
141 skos:prefLabel Neural encoding
142 sg:ontologies/subjects/neuroscience sgo:sdDataset onto_subjects
143 rdf:type npg:Subject
144 skos:Concept
145 rdfs:label Neuroscience
146 skos:altLabel Neurosciences
147 skos:broader sg:ontologies/subjects/biological-sciences
148 skos:definition Neuroscience is a multidisciplinary science that is concerned with the study of the structure and function of the nervous system. It encompasses the evolution, development, cellular and molecular biology, physiology, anatomy and pharmacology of the nervous system, as well as computational, behavioural and cognitive neuroscience.
149 skos:inScheme sg:ontologies/subjects/
150 skos:narrower sg:ontologies/subjects/auditory-system
151 sg:ontologies/subjects/blood-brain-barrier
152 sg:ontologies/subjects/cell-death-in-the-nervous-system
153 sg:ontologies/subjects/cellular-neuroscience
154 sg:ontologies/subjects/circadian-rhythms-and-sleep
155 sg:ontologies/subjects/cognitive-ageing
156 sg:ontologies/subjects/cognitive-neuroscience
157 sg:ontologies/subjects/computational-neuroscience
158 sg:ontologies/subjects/development-of-the-nervous-system
159 sg:ontologies/subjects/diseases-of-the-nervous-system
160 sg:ontologies/subjects/emotion
161 sg:ontologies/subjects/epigenetics-in-the-nervous-system
162 sg:ontologies/subjects/feeding-behaviour
163 sg:ontologies/subjects/genetics-of-the-nervous-system
164 sg:ontologies/subjects/glial-biology
165 sg:ontologies/subjects/gliogenesis
166 sg:ontologies/subjects/gustatory-system
167 sg:ontologies/subjects/ion-channels-in-the-nervous-system
168 sg:ontologies/subjects/learning-and-memory
169 sg:ontologies/subjects/molecular-neuroscience
170 sg:ontologies/subjects/motivation
171 sg:ontologies/subjects/motor-control
172 sg:ontologies/subjects/myelin-biology-and-repair
173 sg:ontologies/subjects/neural-ageing
174 sg:ontologies/subjects/neural-circuit
175 sg:ontologies/subjects/neuro-vascular-interactions
176 sg:ontologies/subjects/neurogenesis
177 sg:ontologies/subjects/neuroimmunology
178 sg:ontologies/subjects/neuronal-physiology
179 sg:ontologies/subjects/neurotrophic-factors
180 sg:ontologies/subjects/oculomotor-system
181 sg:ontologies/subjects/olfactory-system
182 sg:ontologies/subjects/peripheral-nervous-system
183 sg:ontologies/subjects/regeneration-and-repair-in-the-nervous-system
184 sg:ontologies/subjects/reward
185 sg:ontologies/subjects/sensorimotor-processing
186 sg:ontologies/subjects/sensory-processing
187 sg:ontologies/subjects/sexual-behaviour
188 sg:ontologies/subjects/social-behaviour
189 sg:ontologies/subjects/social-neuroscience
190 sg:ontologies/subjects/somatosensory-system
191 sg:ontologies/subjects/spine-regulation-and-structure
192 sg:ontologies/subjects/stem-cells-in-the-nervous-system
193 sg:ontologies/subjects/stress-and-resilience
194 sg:ontologies/subjects/synaptic-plasticity
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198 skos:prefLabel Neuroscience
199 skos:Concept sgo:sdDataset for_codes
200 rdf:type rdfs:Class
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203 skos:Concept
 




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