Neurodynamics

The brain is a prime example of a complex system: It is composed of interconnected parts — neurons — that as a whole exhibit properties (behavior among them) not obvious from the properties of the individual parts. From a philosophical point of view, this is related to fundamental questions of free will and consciousness, for example.

Much like a computer, the brain receives, processes and outputs information. The brain's tremendous  complexity makes an immediate understanding of its dynamics an overwhelming task. However, studying the dynamics of much smaller groups of neurons appears to be a promising first step. Currently, researchers are able not only to culture neuronal networks on silicon wafers, but also to send information to specific neurons in these circuits and to read out information – by optically recording the resulting cascade of activity. It is well known that the neurons' activity can influence their connectivity by rewiring the synaptic connections between them. By learning how this neuronal plasticity is used to achieve cognitive function we may also learn how to "program" these neuronal cultures into massively parallel bio-computing devices. These neuronal networks have the potential to excel in areas where conventional computers have struggled, such as pattern recognition and complex decision making.

Progress in understanding the basic principles of neuronal dynamics is needed to make these breakthroughs a reality. The apparent homogeneity and dense interconnectivity of the vast number of neurons in neuronal tissue make it well suited to analysis using techniques from statistical physics and complex network theory. Applying these tools to neuroscience is certainly not a new idea; however, with the advent of new high quality data sets warrants a reinvestigation of some existing ideas and opens up room for new ones. We aim to combine state of the art data analysis with the highest quality neuronal data available in an attempt to reveal the basic principles of neuronal dynamics.