One of the grand challenges in the study of the brain and neurons is to try and understand what is being computed. To put this in perspective, a digital computer is made up from a bunch of analog circuit elements that are driven in such away that their output can be interpreted as a digital signal. Furthermore, we understand the dynamics of both individual and groups of transistors pretty well, so given an electric current and a bunch of transistors in a circuit, we can predict the output of the circuit quite accurately. We can also understand what the circuit is computing either as a digital computer or an analog computer.

In contrast, the dynamics of neurons are understood very well. That is, given a bunch of wired up neurons, we can simulate their behavior very well using models. But we have no clue as to what they are computing. This isn't because we don't, in some sense, understand the "language," it is far more fundamental than that—if neurons were logic gates, we wouldn't be able to distinguish AND gates from OR gates. On further examination, this is even stranger because, like transistors, we have a pretty good idea about how synapses fire and what triggers them. True, we can't build neurons like we build transistors, but we still understand the mechanics of the computational end of the neuron pretty well. Put simply, we understand how they work, we can predict their behavior, but we have no clue what they actually do.

Now researchers have shown that they are able to relate computational concepts to the dynamical properties of neurons. The result is limited to a class of neurons that exhibit a constant spiking rate but have a varying interval between spikes. The dynamical behavior of such a neuron is described by how the time interval between two pulses is modified by an input stimuli. This is called the phase-resetting curve. Computationally, we are more interested in what features in a stimuli result in spiking. This is called the spike triggered average.

What the researchers have shown is that the spike triggered average and the phase-resetting curve are mathematically related. The spike triggered average is proportional to the derivative of the phase-resetting curve, which relates a dynamical property of the neuron to a computational property of the neuron. The researchers showed that this is analytically true for a particular class of neurons, provided that the stimulation is not too big. Furthermore, measurements on real neurons have confirmed the finding.

This step doesn't yet answer the question of what a neuron, or circuit of neurons is calculating, but it is a step that may make answering that question easier.

Physical Review Letters, 2007, DOI: 10.1103/PhysRevLett.99.248103