Neural Code

An open question in neuroscience is: what is the neural code? By that it is meant, how is information represented and processed in the brain. I would say that the majority of neuroscientists, especially experimentalists, don’t worry too much about this problem and implicitly assume what is called a rate code, which I will describe below. There is then a small but active group of experimentalists and theorists who are keenly interested in this question and there is a yearly conference, usually at a ski resort, devoted to the topic. I would venture to say that within this group – who use tools from statistics, Bayesian analysis, machine learning and information theory to analyze data obtained from in vivo multi-electrode recordings of neural activity in awake or sedated animals given various stimuli – there is a larger amount of skepticism towards a basic rate code than the general neural community.

For the beneficiary of the uninitiated, I will first give a very brief and elementary review of neural signaling. The brain consists of 10^11 or so neurons, which are intricately connected to one another. Each neuron has a body, called the soma, an output cable, called the axon, and input cables, called the dendrites. Axons “connect” to dendrites through synapses. Neurons signal each other with a hybrid electro-chemical scheme. The electrical part involves voltage pulses called action potentials or spikes. The spikes propagate down axons through the movement of ions across the cell membrane. When the spikes reach a synapse, they trigger a release of neurotransmitters, which diffuse across the synaptic cleft, bind to receptors on the receiving end of the synapses and induce either a depolarizing voltage pulse (excitatory signal) or a hyperpolarizing voltage pulse (inhibitory signal). In that way, spikes from a given neuron can either increase or decrease the probability of spikes in a connected neuron.

The neuroscience community is basically all in agreement that neural information is carried by the spikes. So the question of the neural code becomes: how is information coded into spikes? For example, if you look at an apple, something in the spiking pattern of the neurons in the brain is representing the apple. Does this change involve just a single neuron? This is called the grandmother cell code, from the joke that there is a single neuron in the brain that represents your grandmother. Or does it involve a population of neurons, known not surprisingly, as a population code. How did the spiking pattern change? Neurons have some background spiking rate, so do they simply spike faster when they are coding for something, or does the precise spiking pattern matter. If it is just a matter of spiking faster then this is called a rate code, since it is just the spiking rate of the neuron that contains information. If the pattern of the spikes matter then it is called a timing code.

The majority of neuroscientists, especially experimentalists, implicitly assume that the brain uses a population rate code. The main reason they believe this is because in most systems neuroscience experiments, an animal will be given a stimulus, and then neurons in some brain region are recorded to see if any respond to that particular stimulus. To measure the response they often count the number of spikes in some time window, say 500 ms, and see if it exceeds some background level. What seems to be true from almost all of these experiments is that no matter how complicated a stimulus you want to try, a group of neurons can usually be found that respond to that stimulus. So, the code must involve some population of neurons and the spiking rate must increase. What is not known is which and how many neurons are involved and whether or not the timing of the spikes matter.

My sense is that the neural code is a population rate code but the population and time window change and adapt depending on context. Thus understanding the neural code is no simpler than understanding how the brain computes. In molecular biology, deciphering the genetic code ultimately led to understanding the mechanisms behind gene transcription but I think in neuroscience it may be the other way around.