Example of Biological Cybernetics format
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Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format
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Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format Example of Biological Cybernetics format
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open access Open Access

Biological Cybernetics — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Computer Science (all) #72 of 226 down down by 39 ranks
Biotechnology #151 of 282 down down by 68 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 132 Published Papers | 393 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 20/07/2020
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Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

1.111

15% from 2018

Impact factor for Biological Cybernetics from 2016 - 2019
Year Value
2019 1.111
2018 1.305
2017 1.44
2016 1.716
graph view Graph view
table view Table view

3.0

7% from 2019

CiteRatio for Biological Cybernetics from 2016 - 2020
Year Value
2020 3.0
2019 2.8
2018 2.8
2017 4.0
2016 4.0
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 15% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 7% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.608

76% from 2019

SJR for Biological Cybernetics from 2016 - 2020
Year Value
2020 0.608
2019 0.345
2018 0.396
2017 0.667
2016 0.762
graph view Graph view
table view Table view

0.791

9% from 2019

SNIP for Biological Cybernetics from 2016 - 2020
Year Value
2020 0.791
2019 0.725
2018 0.841
2017 1.091
2016 0.995
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 76% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 9% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Biological Cybernetics

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Springer

Biological Cybernetics

The aim of Biological Cybernetics is to foster and intensify the search for a general systems theory of biological information processing. Following the definition of cybernetics given by Norbert Wiener in 1948, the scope of works published in the journal shall encompass all i...... Read More

Computer Science

i
Last updated on
20 Jul 2020
i
ISSN
0340-1200
i
Impact Factor
High - 1.36
i
Acceptance Rate
55%
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
SPBASIC
i
Citation Type
Author Year
(Blonder et al, 1982)
i
Bibliography Example
Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

Journal Article DOI: 10.1007/BF00337288
Self-organized formation of topologically correct feature maps
Teuvo Kohonen1
01 Jan 1988 - Biological Cybernetics

Abstract:

This work contains a theoretical study and computer simulations of a new self-organizing process. The principal discovery is that in a simple network of adaptive physical elements which receives signals from a primary event space, the signal representations are automatically mapped onto a set of output responses in such a way... This work contains a theoretical study and computer simulations of a new self-organizing process. The principal discovery is that in a simple network of adaptive physical elements which receives signals from a primary event space, the signal representations are automatically mapped onto a set of output responses in such a way that the responses acquire the same topological order as that of the primary events. In other words, a principle has been discovered which facilitates the automatic formation of topologically correct maps of features of observable events. The basic self-organizing system is a one- or two-dimensional array of processing units resembling a network of threshold-logic units, and characterized by short-range lateral feedback between neighbouring units. Several types of computer simulations are used to demonstrate the ordering process as well as the conditions under which it fails. read more read less

Topics:

Self-organizing map (51%)51% related to the paper
View PDF
8,247 Citations
open accessOpen access Journal Article DOI: 10.1007/BF00339943
Neural computation of decisions in optimization problems
John J. Hopfield1, David W. Tank2
01 Jul 1985 - Biological Cybernetics

Abstract:

Highly-interconnected networks of nonlinear analog neurons are shown to be extremely effective in computing. The networks can rapidly provide a collectively-computed solution (a digital output) to a problem on the basis of analog input information. The problems to be solved must be formulated in terms of desired optima, often... Highly-interconnected networks of nonlinear analog neurons are shown to be extremely effective in computing. The networks can rapidly provide a collectively-computed solution (a digital output) to a problem on the basis of analog input information. The problems to be solved must be formulated in terms of desired optima, often subject to constraints. The general principles involved in constructing networks to solve specific problems are discussed. Results of computer simulations of a network designed to solve a difficult but well-defined optimization problem-the Traveling-Salesman Problem-are presented and used to illustrate the computational power of the networks. Good solutions to this problem are collectively computed within an elapsed time of only a few neural time constants. The effectiveness of the computation involves both the nonlinear analog response of the neurons and the large connectivity among them. Dedicated networks of biological or microelectronic neurons could provide the computational capabilities described for a wide class of problems having combinatorial complexity. The power and speed naturally displayed by such collective networks may contribute to the effectiveness of biological information processing. read more read less

Topics:

Optimization problem (58%)58% related to the paper, Models of neural computation (55%)55% related to the paper, Hopfield network (53%)53% related to the paper
5,328 Citations
Journal Article DOI: 10.1007/BF00344251
Neocognitron: A Self Organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position
01 Jan 1980 - Biological Cybernetics

Abstract:

A neural network model for a mechanism of visual pattern recognition is proposed in this paper. The network is self-organized by “learning without a teacher”, and acquires an ability to recognize stimulus patterns based on the geometrical similarity (Gestalt) of their shapes without affected by their positions. This network i... A neural network model for a mechanism of visual pattern recognition is proposed in this paper. The network is self-organized by “learning without a teacher”, and acquires an ability to recognize stimulus patterns based on the geometrical similarity (Gestalt) of their shapes without affected by their positions. This network is given a nickname “neocognitron”. After completion of self-organization, the network has a structure similar to the hierarchy model of the visual nervous system proposed by Hubel and Wiesel. The network consits of an input layer (photoreceptor array) followed by a cascade connection of a number of modular structures, each of which is composed of two layers of cells connected in a cascade. The first layer of each module consists of “S-cells”, which show characteristics similar to simple cells or lower order hypercomplex cells, and the second layer consists of “C-cells” similar to complex cells or higher order hypercomplex cells. The afferent synapses to each S-cell have plasticity and are modifiable. The network has an ability of unsupervised learning: We do not need any “teacher” during the process of self-organization, and it is only needed to present a set of stimulus patterns repeatedly to the input layer of the network. The network has been simulated on a digital computer. After repetitive presentation of a set of stimulus patterns, each stimulus pattern has become to elicit an output only from one of the C-cell of the last layer, and conversely, this C-cell has become selectively responsive only to that stimulus pattern. That is, none of the C-cells of the last layer responds to more than one stimulus pattern. The response of the C-cells of the last layer is not affected by the pattern's position at all. Neither is it affected by a small change in shape nor in size of the stimulus pattern. read more read less

Topics:

Neocognitron (61%)61% related to the paper, Form perception (51%)51% related to the paper, Stimulus (physiology) (51%)51% related to the paper, Artificial neural network (50%)50% related to the paper
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4,713 Citations
open accessOpen access Journal Article
Self-organized formation of topographically correct feature maps
01 Jan 1982 - Biological Cybernetics

Topics:

Feature (computer vision) (71%)71% related to the paper
3,184 Citations
Journal Article DOI: 10.1007/BF00336961
The structure of images
01 Jan 1984 - Biological Cybernetics

Abstract:

In practice the relevant details of images exist only over a restricted range of scale. Hence it is important to study the dependence of image structure on the level of resolution. It seems clear enough that visual perception treats images on several levels of resolution simultaneously and that this fact must be important for... In practice the relevant details of images exist only over a restricted range of scale. Hence it is important to study the dependence of image structure on the level of resolution. It seems clear enough that visual perception treats images on several levels of resolution simultaneously and that this fact must be important for the study of perception. However, no applicable mathematically formulated theory to deal with such problems appears to exist. In this paper it is shown that any image can be embedded in a one-parameter family of derived images (with resolution as the parameter) in essentially only one unique way if the constraint that no spurious detail should be generated when the resolution is diminished, is applied. The structure of this family is governed by the well known diffusion equation (a parabolic, linear, partial differential equation of the second order). As such the structure fits into existing theories that treat the front end of the visual system as a continuous stack of homogeneous layers, characterized by iterated local processing schemes. When resolution is decreased the images becomes less articulated because the extrem ("light and dark blobs") disappear one after the other. This erosion of structure is a simple process that is similar in every case. As a result any image can be described as a juxtaposed and nested set of light and dark blobs, wherein each blob has a limited range of resolution in which it manifests itself. The structure of the family of derived images permits a derivation of the sampling density required to sample the image at multiple scales of resolution.(ABSTRACT TRUNCATED AT 250 WORDS) read more read less

Topics:

Scale space (55%)55% related to the paper, Scale-space axioms (53%)53% related to the paper, Erosion (morphology) (51%)51% related to the paper, Diffusion equation (50%)50% related to the paper
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2,641 Citations
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Biological Cybernetics format uses SPBASIC citation style.

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Frequently asked questions

1. Can I write Biological Cybernetics in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Biological Cybernetics guidelines and auto format it.

2. Do you follow the Biological Cybernetics guidelines?

Yes, the template is compliant with the Biological Cybernetics guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Biological Cybernetics?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Biological Cybernetics citation style.

4. Can I use the Biological Cybernetics templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Biological Cybernetics.

5. Can I use a manuscript in Biological Cybernetics that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Biological Cybernetics that you can download at the end.

6. How long does it usually take you to format my papers in Biological Cybernetics?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Biological Cybernetics.

7. Where can I find the template for the Biological Cybernetics?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Biological Cybernetics's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Biological Cybernetics's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Biological Cybernetics an online tool or is there a desktop version?

SciSpace's Biological Cybernetics is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Biological Cybernetics?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Biological Cybernetics?”

11. What is the output that I would get after using Biological Cybernetics?

After writing your paper autoformatting in Biological Cybernetics, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Biological Cybernetics's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Biological Cybernetics?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Biological Cybernetics. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Biological Cybernetics?

The 5 most common citation types in order of usage for Biological Cybernetics are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Biological Cybernetics?

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16. Can I download Biological Cybernetics in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Biological Cybernetics Endnote style according to Elsevier guidelines.

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