Neural Ensemble - Encoding

Encoding

Neuronal ensembles encode information in a way somewhat similar to the principle of Wikipedia operation - multiple edits by many participants. Neuroscientists have discovered that individual neurons are very noisy. For example, by examining the activity of only a single neuron in the visual cortex, it is very difficult to reconstruct the visual scene that the owner of the brain is looking at. Like a single Wikipedia participant, an individual neuron does not 'know' everything and is likely to make mistakes. This problem is solved by the brain having billions of neurons. Information processing by the brain is population processing, and it is also distributed - in many cases each neuron knows a little bit about everything, and the more neurons participate in a job, the more precise the information encoding. In the distributed processing scheme, individual neurons may exhibit neuronal noise, but the population as a whole averages this noise out.

An alternative to the ensemble hypothesis is the theory that there exist highly specialized neurons that serve as the mechanism of neuronal encoding. In the visual system, such cells are often referred to as grandmother cells because they would respond in very specific circumstances—such as when a person gazes at a photo of their grandmother. Neuroscientists have indeed found that some neurons provide better information than the others, and a population of such expert neurons has an improved signal to noise ratio. However, the basic principle of ensemble encoding holds: large neuronal populations do better than single neurons.

The emergence of specific neural assemblies is thought to provide the functional elements of brain activity that execute the basic operations of informational processing (see Fingelkurts An.A. and Fingelkurts Al.A., 2004; 2005).

Neuronal code or the 'language' that neuronal ensembles speak is very far from being understood. Currently, there are two main theories about neuronal code. The rate encoding theory states that individual neurons encode behaviorally significant parameters by their average firing rates, and the precise time of the occurrences of neuronal spikes is not important. The temporal encoding theory, on the contrary, states that precise timing of neuronal spikes is an important encoding mechanism.

Neuronal oscillations that synchronize activity of the neurons in an ensemble appear to be an important encoding mechanism. For example, oscillations have been suggested to underlie visual feature binding (Gray, Singer and others). In additions, sleep stages and waking are associated with distinct oscillatory patterns.