The Problem of Neural Coding

Theories of brain function are based on the idea that information is carried by the electrical activity of neurons. How this information is represented is therefore fundamental to all branches of neuroscience. What is the neural code of information, and how is it used by the brain to achieve perception, action, thought, and consciousness? In other words, which aspects of a neuron´s electrical activity convey information about the environment and our mental states? It is common practice in many laboratories to display the electrical activity from one or more neurons in an animal while it look at, hears, and reacts to its environment. This neural activity appears as a sequence of very
brief events, the action potentials, separated by gaps of variable duration.
The intervals between spikes can be as long as a few tenths of a second or
shorter than a hundredth of a second. The spikes and the intervals between
them codify the neuron´s message. If we wish to decipher the neural code, we need to know how to interpret these messages. It is well established that
the only type of message that a neuron can send to another neuron, in a
different part of the brain, must be represented in the sequences of spikes
that are transmitted along its axon. The time-scale for neural computations
involved in perception, thought, and action is too short to allow gene expression, protein synthesis, and chemical cascades to play a part in carrying information. Spikes are the only items in the alphabet, but unlike letters, they are of one kind only. Spikes are all-or-none events, size does not matter.
As Rieke and colleagues puts it, spikes sequences are the language for which the brain is listening, the language the brain uses for its internal musings, and the language it speaks as it talks to the outside world [1].
The question is how to read this sequence of spikes emitted by neurons as a function of time. What are the properties of the neural spike trains that provide the possibility to carry the information or take part in information processing? What is the information contained in such a spatio-temporal pattern of pulse? What is the code used by the neurons to transmit that information? How might other neurons decode the signal? The above questions point to the problem of neuronal coding. At present, a de¯nite answer to these questions is not known. In the theory of neuronal information processing, there are two main hypotheses with respect to where in the spike train the neural information is encoded, whether in the neural ¯ring rate or in the precise timing of the spikes. They are discussed in the following.
Until recently, the most popular hypothesis is that most of, if not all, the relevant information was contained in the mean ¯ring rate of the neuron, de¯ned by the number of spikes that occur in a time window, divided by the length of the window. The concept of mean ¯ring rates has been successfully applied during the last 80 years. In a seminal contribution, of more than 75 years ago, Adrian showed that the ring rate of stretch receptor neurons is related to the force being applied to the muscles [2]. In [3], Adrian and his colleagues showed that each ¯ber originating from skin receptors responded to a particular type of stimulus (pressure, temperature or damage) and the frequency of the impulses was dependent upon the intensity of the stimulus.
The de¯nition of the rate has been applied to the discovery of the properties of many type of neurons in the sensory, motor, and central nervous system, by searching for those stimuli that make neurons ¯re maximally. Further support for the frequency code theory came from the work by Eccles [4], based on spinal cord neuronal recordings. Eccles observed that the spike frequency of a peripheral nerve correlated with the intensity of the applied stimulus, and with the intensity of sensation as well. Later it was shown that all sensory ¯bers which terminate in the spinal cord express stimulus intensity dependent discharge rates [5]. Similarly, the frequency of action potentials recorded from neurons in the motor cortex [6] throughout the cortico-spinal tract [7] and motor units [8] correlated with the tone of the target muscle.
Since Adrian´s studies, the rate coding hypothesis has been dominant in the neural computational ¯eld. Rate coding explains how the presentation and intensity of the stimulus can in°uence neural activity but this coding neglects the temporal organization of spike trains. For this reason, the ¯ring rate concept has been criticized and it is subject of an ongoing debate, stimulated also by experiments showing the importance of the temporal dimension in neural information processing.
Recently, more and more experimental evidences suggested that a straightforward ¯ring rate concept based on temporal averaging may be too simplistic to describe brain activity. Experiments showed that temporal patterns.
Indeed, for any rate there are an in¯nite number of possible temporal distributions of spikes. Vice versa, it has been experimentally showed that
different stimuli or tasks can elicit different patterns of activity that have the same ¯ring rate. In principle, taking into account the temporal structure of a spike train would expand the alphabet that brain uses to encode information.
Recent observations on the behavior of cortical visual neurons demonstrated
a precision in brain function timings higher than would be predicted from
frequency coding. Humans, for example, can recognize and respond to visual
scenes in less than 400ms [9]. Recognition and reaction involve several processing steps from the retinal input to the ¯nger movement at the output. If at each processing step neurons had to wait in order to perform a temporal average, the reaction time would be much longer. This result suggests the existence of computational processes based on the precise timing of spikes in neuronal ensembles. In the last decade, the focus of attention in experimental and computational neuroscience has therefore shifted towards the exploration of how the timing of single spikes is used by the nervous system.
There is evidence of precise temporal correlations between pulses of different neurons [10] [11] and stimulus dependent synchronization of the activity in neuronal populations [12] [13] [14]. These results suggest that the exact timing of spikes should play an important role.
In the following, different type of rate and temporal codes are briefly
introduced, each related to a strategy based on different notion of rate or
spike timing.