Hodgkin and Huxley ’ s legacy : the science of neural supercomputing

The small sub-tropical island of Okinawa, some 400 miles south of Japan, is home to a unique colony of creatures. About the size of large rabbits, they possess strange translucent heads into which a single eye is embedded. They spend most of their time foraging and mating and communicate with each other using a luminescent bulb located towards the small of their backs. Their predilection for reproduction has allowed their primitive genome to evolve rapidly to adapt to their unusual circumstances. For their food is a power supply to charge their battery packs, and mating involves infrared data transfer from their hard drives. They are cyber-rodents: mobile, autonomous robots confined only to their home in the Okinawa Institute of Science and Technology by the doors of the laboratory. There are two remarkable things about the cyber-rodents. First, they demonstrate that remarkably little computational power can sustain ‘awake’, behaving organisms. Their brain is a small fourCPU onboard computer, not a giant supercomputer as you might expect. Second, many algorithms used by the rodents are based on those thought to be implemented by live rats and indeed humans. These algorithms control basic behaviours of self-preservation, mimicking the functions thought to be controlled by neuromodulators such as serotonin and dopamine. This second fact underpins a basic fact about neuroscience: if we want to understand the brain, we need to understand how it actually processes information. Like it or not, the brain is a computer, and in the same way, as we cannot hope to understand the brain of the cyber-rodent without a fair knowledge of computer science, so too we are not going to understand the human brain without some sort of neural computer science framework. It is easy to feel a bit daunted by this prospect. With 100 billion neuronal processing units (not to mention those involving glia), each with thousands of synaptic connections, the astronomical complexity that the brain harbours is abundantly clear. If anything, this sentiment is aggravated by the sheer scale of recent big-money projects popular with the media. For instance, the ‘Blue Brain’ project is attempting to build a full-scale model of the human organ based on assembling biologically accurate simulations of single neurons and exploring behaviour of the assembled network using the formidable Swiss BlueGene supercomputer. In much the same way, The Human Connectome project is attempting to map all the connections in the brain, by painstakingly identifying neuronal connections using fibre-tracing studies. But despite the many insights and tools that these projects will provide, one thing they seem unlikely to yield when completed and ‘turned on’ is a behaving and talking android. This, then, still leaves us with the problem of how to understand behaviour in terms of information processing. David Marr, the brilliant computer scientist-turned-theoretical neuroscientist, famously proposed three distinct levels of understanding how the brain works: characterizing the computational problems that the MATHEMATICS FOR NEUROSCIENTISTS By Fabrizio Gabbiani and Stephen Cox 2010. New York: Academic Press ISBN: 978-0-12-374882-9 (Hardcover) Price: E71.95/$US99.95