Understanding how the brain works is one of the top scientific challenges of this century. It is commonly believed that the functionalities of the brain reside distributively in the neural circuits formed by a large number of neurons interconnected by synapses. Statistical physics focuses on the study of the collective behaviors of many interacting elements, and has played a significant role in elucidating brain functions in the past few decades.

Traditionally, there have not been sufficient interactions between the statistical physicists who work on neural models and neurobiologists who work on neuroscience experiments. However, there is an increasing broad understanding of neural systems using various tools and models, including the statistical mechanics of complex energy landscapes, non-linear dynamics, the concepts of self-organized criticality and chaos, complex networks, and the relations between integrate-and-fire models and rate models, to name a few. There is also a dramatic upsurge in computational power to deal with large scale simulations of neural systems. On the side of neuroscience, experimental tools, such as electroencephalography, magnetic resonance imaging, fluorescent proteins, and fluorescence resonance energy transfer techniques, have opened up many neuroscience areas for exploration. On the application side, technological advances in human-machine interface and robotics have provided a powerful push for a deeper understanding of neural information processing.

The purpose of this program is to bring together scientists from different backgrounds and hence facilitate an interdisciplinary dialog among them. We aim at reviewing recent progress in statistical physics and computational neuroscience, communicating the latest results and newly established methodologies among the participants, and identifying common themes and issues for further investigation. We believe the program is very timely and can play a significant role in sharpening the issues for both theoretical and experimental communities in this challenging area.

 

    

The Student Conference has been supported by the Postgraduate Students Conference / Seminar Grant of the Research Grants Council, Hong Kong.