Positive Technology Journal

The Advanced Course in Computational Neuroscience is for advanced graduate students and postdoctoral fellows who are interested in learning the essentials of the field. We seek students of any nationality from a variety of disciplines, including neuroscience, physics, electrical engineering, computer science, mathematics and psychology. Students are expected to have a keen interest and basic background in neurobiology as well as some computer experience.

The course has two complementary parts. Mornings are devoted to lectures given by distinguished international faculty on topics across the breadth of experimental and computational neuroscience. During the rest of the day, students are given practical training in the art and practice of neural modelling, largely through the medium of their individual choice of model systems.

The first week of the course introduces students to essential neurobiological concepts and to the most important techniques in modelling single cells, networks and neural systems. Students learn how to solve their research problems using software packages such as MATLAB, NEST, NEURON, XPP, etc. During the following three weeks the lectures cover specific brain areas and functions. Topics range from modelling single cells and subcellular processes through the simulation of simple circuits, large neuronal networks and system level models of the brain. The course ends with project presentations by the students.

A maximum of 30 students will be accepted. There will be a minimum fee of EUR 500 per student (depending on the course’s funding) covering costs for lodging, meals and other course expenses. Also depending on funding, there will be a limited number of tuition fee waivers and travel stipends available for students who need financial help for attending the course. We specifically encourage applications from researchers who work in the developing world. These students will be selected following the normal submission procedure.

Applications, including a description of the target project must be submitted electronically (see below) and should be accompanied by the names and email details of two referees who have agreed to furnish references. Applications will be assessed by a committee, with selection being based on the following criteria: the scientific quality of the candidate (CV) and of the project, the recommendation letters, and evidence that the course affords substantial.

More information and application forms can be obtained from here

This symposium aims to bring together international researchers from the cognitive neuroscience and engineering communities for biologically-inspired models and system implementations with human-like intelligent functions. The previous meeting was held as a post-IJCNN Symposium on Bio-Inspired Models and Hardware (BIMH2005) at Montreal, Canada, on August 5, 2005.

Although artificial neural networks are based on information processing mechanisms in our brain, there still exists a big gap between the biological neural networks and artificial neural networks. The more intelligence we would like to incorporate into artificial intelligent systems, the more biologically-inspired models and hardware are required. Fortunately the cognitive neuroscience has been developed enormously during the last decade, and engineers now have more to learn from the science.  

In this symposium we will discuss what engineers want to learn from the science and how the scientists may be able to provide the knowledge.
Then, mathematical models will be presented with more biological plausibility.
The hardware and system implementation will also be reported with the performance comparison with conventional methods for real-world complex applications. A panel will be organized for the future research directions at the end.

This symposium will promote synergetic interaction among cognitive neuroscientists, neural networks and robotics engineers, and result in more biologically-plausible mathematical models and hardware systems with more human-like intelligent performance in real-world applications.