User:Man6506/Neural coding

Neural coding is the transduction of environmental signals and internal signals of the body into neural activity patterns as representations forming a model of reality suitable for purposeful actions and adaptation, preserving the integrity and normal functioning of the body. It also describes the study of information processing by neurons along with learning on what the information is used for and how it is transformed when it is being passed through from one another.

Overview
Neurons are really noticeable among the cells of the body in their ability to process signals (i.e., light, sound, taste, smell, touch, and others) rapidly and transmit information about them over large distances and among vast neural populations. The brain is the highest achievement in the evolution of natural information technologies in terms of speed and efficiency. It follows that, of all coding schemes, the most likely candidate for neural code is the one that produces information (code patterns) most efficiently.

Neurons generate voltage oscillations called action potentials. All models consider the action potential as a fundamental element of the brain's language. However, the critical issue is the approach to this phenomenon. Physically action potentials are continuous oscillatory processes that vary in duration, amplitude and shape. Neurons demonstrate graded potentials that can provide high capacity and efficiency of the code. Nevertheless, most models regard neural activity as identical discrete events (spikes). If the internal parameters of an action potential are ignored, a spike train can be characterized simply by a series of all-or-none point events in time. The lengths of interspike intervals can also vary. But they are usually ignored in the currently prevailing models of the neural code.

Such theories assume that the information is contained in the number of spikes in a particular time window (rate code) or their precise timing (temporal code). Whether neurons use rate coding or temporal coding is a topic of intense debate within the neuroscience community, even though there is no clear definition of what these terms mean. Anyway, all these theories are variations of a spiking neuron model. Statistical methods and methods of probability theory and stochastic point processes are widely applied to describe and analyze neuronal firing. Some studies claim that they cracked the neural code  and there are several large-scale brain decoding projects. But the actual reading and writing of the neural code remain a challenge facing neuroscience. The problem is that the spiking neuron models run counter to the actual efficiency and speed of the brain. At best, they cover only a part of the observed phenomena and cannot explain others. Recently, models have appeared that answer questions that are unsolvable within the framework of paradigms that consider the action potentials as similar spikes.. As technology has advanced, new architecture has been proposed which consist of neurons that can potentially carry a larger number of synapses. These synapses have not only make connections but they are capable of computing their excitations level themselves and adjust those connections.

Encoding and decoding
The normal approach for studying the neural code is to look for the similar between the incoming signal and the neuronal response and the reverse process of recovering the signal from the observed neuronal activity. However, without a code model, such analysis is like trying to read or write a text without knowing grammar. It is a kind of vicious circle: to read the code, we need to know it, but to cognize it, we need to read it. However, any process of converting an unknown code is based on searching for specific patterns and identifying their correlation with the encoded message. In other words, to read the neural code, we need to find the correspondence between patterns of signal parameters and neural activity.

Any sign of the environment is an oscillatory energy process with a certain amplitude, frequency and development of phases in time. These are the two main axes of signal measurement: spatial and temporal. Accordingly, the neural code must also have spatial and temporal characteristics that create a model of the encoded signal. They may be locked to an external stimulus or be generated intrinsically by the neural circuitry. As we move along the hierarchy of the technological chain of the nervous system from sensors at the periphery to the integrative structures of the cerebral cortex, the neural activity is less and less directly associated with the original signal. It is natural since neurons do not reflect signals but encode them, i.e., create representations. Consciousness is not a mirror of reality but a small exact copy of reality. However, a representation should still contain all the same axes of parameters measurement. Thus, the neural code unfortunately has got to be a complex multidimensional structure. At the same time, information density should combine with efficiency and speed.

Do the proposed coding models reflect these requirements? This question should be a "litmus test" for their adequacy to actual processes in the nervous system.