Which is why the brain is so “difficult to crack”:
A Chaos of Codes
Already we’re beginning to discover clues about how the brain’s coding works. Perhaps the most fundamental: except in some of the tiniest creatures, such as the roundworm C. elegans, the basic unit of neuronal communication and coding is the spike (or action potential), an electrical impulse of about a tenth of a volt that lasts for a bit less than a millisecond. In the visual system, for example, rays of light entering the retina are promptly translated into spikes sent out on the optic nerve, the bundle of about one million output wires, called axons, that run from the eye to the rest of the brain. Literally everything that you see is based on these spikes, each retinal neuron firing at a different rate, depending on the nature of the stimulus, to yield several megabytes of visual information per second. The brain as a whole, throughout our waking lives, is a veritable symphony of neural spikes—perhaps one trillion per second. To a large degree, to decipher the brain is to infer the meaning of its spikes.
But the challenge is that spikes mean different things in different contexts. It is already clear that neuroscientists are unlikely to be as lucky as molecular biologists. Whereas the code converting nucleotides to amino acids is nearly universal, used in essentially the same way throughout the body and throughout the natural world, the spike-to-information code is likely to be a hodgepodge: not just one code but many, differing not only to some degree between different species but even between different parts of the brain. The brain has many functions, from controlling our muscles and voice to interpreting the sights, sounds, and smells that surround us, and each kind of problem necessitates its own kinds of codes.
One reason such questions about the brain’s schemes for encoding information have proved so difficult to crack is that the human brain is so immensely complex, encompassing 86 billion neurons linked by something on the order of a quadrillion synaptic connections. Another is that our observational techniques remain crude. The most popular imaging tools for peering into the human brain do not have the spatial resolution to catch individual neurons in the act of firing. To study neural coding systems that are unique to humans, such as those used in language, we probably need tools that have not yet been invented, or at least substantially better ways of studying highly interspersed populations of individual neurons in the living brain.
We are informed that there is some cause for hope.
But is the computer even the right image?