In this chapter we first outline some of the popular computing environments used for analysing neural data, followed by a brief discussion of 'software carpentry', basic tools and skills from software engineering that can be of great use to computational scientists. We then introduce the concept of open-source software and explain some of its potential benefits for the academic community before giving a brief directory of some freely available open source software packages that address various aspects of the study of neural codes. While there are many commercial offerings that provide similar functionality, we concentrate here on open source packages, which in addition to being available free of charge, also have the source code available f...
Advances in experimental techniques and computational power allowing researchers to gather anatomica...
Almost all research work in computational neuroscience involves software. As researchers try to unde...
This paper examines the history and current state of machine learning. It examines neural networks, ...
The approaches used in software development in an industry setting and a scientific environment are ...
In recent years, deep learning has revolutionized machine learning and its applications, producing r...
This article describes basics of neural networks, specifically aimed at usage of freeware to test fr...
New technologies for monitoring and manipulating the nervous system promise exciting biology but pos...
Neuroscience initiatives aim to develop new technologies and tools to measure and manipulate neurona...
Computational neuroscience is an interdisciplinary field that incorporates an analysis of brain func...
NeuroCharter is an open-source software that helps in prediction problems in scientific research thr...
Computational models are powerful tools for exploring the properties of complex biological systems. ...
While recognizing a face or kicking a ball may seem to be easy tasks for us, they still constitute c...
[[abstract]]©2003 Humana Press-Within this paper, we describe a neuroinformatics project (called &qu...
Advances in experimental techniques and computational power allowing researchers to gather anatomica...
Neurodesk is a flexible and scalable data analysis environment for reproducible neuroimaging. This i...
Advances in experimental techniques and computational power allowing researchers to gather anatomica...
Almost all research work in computational neuroscience involves software. As researchers try to unde...
This paper examines the history and current state of machine learning. It examines neural networks, ...
The approaches used in software development in an industry setting and a scientific environment are ...
In recent years, deep learning has revolutionized machine learning and its applications, producing r...
This article describes basics of neural networks, specifically aimed at usage of freeware to test fr...
New technologies for monitoring and manipulating the nervous system promise exciting biology but pos...
Neuroscience initiatives aim to develop new technologies and tools to measure and manipulate neurona...
Computational neuroscience is an interdisciplinary field that incorporates an analysis of brain func...
NeuroCharter is an open-source software that helps in prediction problems in scientific research thr...
Computational models are powerful tools for exploring the properties of complex biological systems. ...
While recognizing a face or kicking a ball may seem to be easy tasks for us, they still constitute c...
[[abstract]]©2003 Humana Press-Within this paper, we describe a neuroinformatics project (called &qu...
Advances in experimental techniques and computational power allowing researchers to gather anatomica...
Neurodesk is a flexible and scalable data analysis environment for reproducible neuroimaging. This i...
Advances in experimental techniques and computational power allowing researchers to gather anatomica...
Almost all research work in computational neuroscience involves software. As researchers try to unde...
This paper examines the history and current state of machine learning. It examines neural networks, ...