User:RoodSTO/sandbox

Article version of changes at User:RoodSTO/tcedits
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Temporal coding is a type of neural coding in which a neuron encodes information through the precise timing of action potentials, or spikes, on a millisecond time scale. There is no universal definition of temporal coding, though efforts have been made to differentiate the precise timing of spikes in a single neuron which encodes information about a stimulus from synchronized firing of neurons within a localized area. The latter is sometimes referred to as correlation coding.

Finding meaning in patterns
Simply put, a neural code can be defined as the minimum number of symbols necessary to express all biologically significant information. There are many hypotheses about an encoding method, two of which are rate coding and temporal coding. Many systems of the body utilize a more complex coding system than could be considered reasonable for a rate code '''alone. The two are often thought to work in conjunction, as in the gustatory system. '''

Neurons exhibit high-frequency fluctuations of firing-rates which could be noise or could carry information. Rate coding models suggest that these irregularities are noise, while temporal coding models suggest that they encode information. If the nervous system only used rate codes to convey information, a more consistent, regular firing rate would have been evolutionarily selected for. Temporal coding supplies an alternate explanation for the “noise” and allows for a more information-rich code. To model this idea, binary symbols can be used to mark the spikes: 1 for a spike, 0 for no spike. Temporal coding allows the sequence 000111000111 to mean something different than 001100110011, even though the mean rate of firing is the same for both sequences, at 6 spikes/10 msec.

Until recently, scientists had put the most emphasis on rate encoding alone. However, functions of the brain are more temporally precise than the use of only rate encoding would seem to allow. In other words, essential information would be lost due to the inability of the rate code to capture all the available information of the spike train. In addition, responses are stochastic enough between similar (but not identical) stimuli to suggest that the different patterns of spikes contain a higher volume of information than is possible to include in a rate code.

Evidence
Because it is unclear when a neuron begins encoding a stimulus, neuroscientists must choose a point of reference to compare different spike trains, and may form different conclusions based on the same spike train pattern. Even so, by observing trends between the stimuli and the response, it is possible to find different patterns which are more likely to be elicited by a certain type of stimulus. Each stimulus can elicit a variety of responses, and there does not seem to be a one-to-one, stimulus-to-response pattern. Despite this, scientists have found that there is a higher likelihood of certain response trends with specific stimuli, but once patterns have been established, there is still the problem of decoding the messages that lie within.

The temporally precise nature of inter-neuronal interactions is one factor that is considered when attempting to establish a probable coding system. Spike-timing-dependent plasticity is one canonical example in which the synchronicity between two neural responses is vital for synapse strengthening or weakening. In the hippocampus, when two EPSPs arrive simultaneously, the likelihood of their producing an action potential is much higher than the smaller, temporally dispersed EPSPs. An in vivo study done by intracellular analysis of pyramidal cells in the monkey motor cortex and simultaneous recordings from connected neurons along the thalamocortical and intracortical transmission chain showed that the EPSPs from the cortical cells had a much more drastic impact on surrounding neurons when they arrived within two milliseconds of each other. The importance of spike-timing-dependent plasticity in the learning and evolution process is an indicator that the neural code is at least partially temporal in nature. [don't think it's relevant to the type of TC we're talking about]

If a neuron is capable of firing at a maximum rate of one hundred spikes per second, then a stimulus of less than ten milliseconds would likely elicit only a single spike. Due to the density of information about the abbreviated stimulus contained in this single spike, it would seem that the timing of the spike itself would have to convey more information than simply the average frequency of action potentials over a given period of time. This model is especially important for sound localization, which occurs within the brain on the order of milliseconds'''. In this case, the''' brain must obtain a large quantity of information based on a relatively short neural response. Additionally, if low firing rates on the order of ten spikes per second must be distinguished from similar(?) rate coding for different stimuli, then a neuron trying to discriminate these two stimuli may need to wait for a second or more to accumulate enough information. This is not consistent with numerous organisms which are able to discriminate between stimuli in the time frame of milliseconds, suggesting that a rate code is not the only model at work.

The gustatory system
The mammalian gustatory system is useful for studying temporal coding because of its fairly distinct stimuli and the easily discernible responses of the organism. Temporally encoded information may help an organism discriminate between different tastants of the same category (sweet, bitter, sour, salty, umami) that elicit very similar responses in terms of spike count. The temporal component of the pattern elicited by each tastant may be used to determine its identity (e.g., the difference between two bitter tastants, such as quinine and denatonium). In this way, both rate coding and temporal coding may be used in the gustatory system: rate for basic tastant type, temporal for more specific differentiation.

Research on mammalian gustatory system has shown that there is an abundance of information present in temporal patterns across populations of neurons, and this information is different than that which is determined by rate coding schemes. Groups of neurons may synchronize in response to a stimulus. In studies dealing with the front cortical portion of the brain in primates, precise patterns with short time scales only a few milliseconds in length were found across small populations of neurons which correlated with certain information processing behaviors. However, little information could be determined from the patterns; one possible theory is they represented the higher-order processing taking place in the brain.

The visual system
'' The specificity of temporal coding requires highly refined technology to create informative, reliable, experimental data. Advances made in optogenetics allowed neurologists to control spikes in individual neurons, offering electrical and spatial single-cell resolution. For example, when blue light is perceived, a channelrhodopsin in pond scum opens, depolarizing the cell and producing a spike. When blue light is not sensed, the channel closes, and the neuron ceases to spike. The pattern of the spikes matches the pattern of the blue light stimuli. By inserting channelrhodopsin gene sequences into mouse DNA, researchers can control spikes and therefore certain behaviors of the mouse (e.g., making the mouse turn left). Researchers, through optogenetics, have the tools to effect different temporal codes in a neuron while maintaining the same mean firing rate, and thereby can test whether or not temporal coding occurs in specific neural circuits. ''

In the primary visual cortex of macaques, the timing of the first spike relative to the start of the stimulus was found to provide more information than the interval between spikes. However, the interspike interval could be used to encode more information, which is especially important when the spike rate reaches its limit, as in high-contrast situations. For this reason, temporal coding may play a part in coding defined edges rather than gradual transitions.

The olfactory system
As with the visual system, in mitral/tufted cells in the olfactory bulb of mice, first-spike latency relative to the start of a sniffing action seemed to encode much of the information about an odor. This strategy of using spike latency allows for rapid identification of and reaction to an odorant. In addition, some mitral/tufted cells have specific firing patterns for given odorants. This type of extra information could help in recognizing a certain odor, but is not completely necessary, as average spike count over the course of the animal's sniffing was also a good identifier. Along the same lines, experiments done with the olfactory system of rabbits showed distinct patterns which correlated with different subsets of odorants, and a similar result was obtained in experiments with the locust olfactory system.

Implications
Optogenetic technology has the potential to help researchers crack the neural code and enable the correction of spike abnormalities at the root of several neurological and psychological disorders. There is a possibility that the neuron encodes information in individual spike timing, and key signals could be missed by attempting to crack the code while looking only at mean firing rates. Understanding any temporally encoded aspects of the neural code and being able to replicate these sequences in neurons could allow for greater control and treatment of depression and Parkinson’s Disease. Controlling the precise spike intervals in single cells has been shown to be more effective in controlling brain activity than adding chemicals and neurotransmitters intravenously.[source?] Such medical possibilities require scientists and communities to address the ethics of such tight control over the brain. While the benefits could be enormous, so could the abuses. However, understanding where the brain uses a temporal code is important and valuable for neuroscientists and patients alike.["orig research" for last part?]

Limitations

 * Not as robust as rate [source]
 * More difficult to decode than rate [source]