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    Nerve Impulses: the Key to Understanding the Brain


    One of the greatest, relatively underappreciated, discoveries in all of science was the discovery of the nerve impulse in the 1930s by the British Lord Adrian. Adrian did win a Nobel Prize for his discovery in 1932, but scholars underestimated its implications, which go beyond the fact that four later Nobel Prizes were awarded for work based on Adrian’s discovery. This included discovery of sodium and potassium ionic flux during impulses, the role of impulses in releasing neurotransmitters, and the role of membrane ion channels in impulse generation and second messenger cascades.

    Like many discoveries in science, this one could not have been made without technological advance. In this case, the essential advance was the development of the capillary electrometer, which enabled detection of very small electrical pulses on the order of one millisecond duration. This instrumentation was crude and far inferior to later advances such as the oscilloscope and computer screens. Before Adrian’s use of the electrometer, scientists generally knew that peripheral nerves generated some kind of electrical signal, but nothing was known about the nature of the signal in individual neurons.

    Nerves contain fibers from hundreds of neurons that produce a summed, relatively long duration and large wave that spreads down the nerve. No one knew how the individual nerve fibers contributed to this compound signal. Adrian answered this question by tedious microdissection of nerves into their individual fibers and recording stimulus-evoked responses in a single fiber. What Adrian saw was that the response was a series of voltage pulses, each about one millisecond long, all of the same amplitude in a given fiber.  Decades later, development of microelectrodes enabled confirmation of Adrian’s discovery in neurons in the brain.

    Fig. 1. Train of nerve impulses from a single neuron over 2.5 seconds, as recorded with extracellular electrodes. Amplitude calibration = 0.5 millivolts. The thick baseline is electronic noise, in which the spikes are embedded. The signal-to-noise ratio is vastly improved with modern electronics and intracellular recording. From Fromm and Bond, 1967, Electroenceph. clin. Neuro. 22, 159.



    This provided the evidence of the basic similarity and difference between brains and the later development of computers. Both computers and brains convert the real world into representations. In computers, information is coded, in the form of 1s and 0s, and as nerve impulses in brains. Both computers and brains distribute and process this represented information, and can store it as memories. However, because brains are biological and use impulses to represent information, they can change their circuitry and can self-program. Unlike computers, brains also have will, including a likely degree of free will.

    Brains have conspicuous functional states, ranging from intense conscious concentration to drowsiness, to sleep, to coma, to death. Neuronal electrical activity correlates in a systematic way with these state changes. The most conspicuous of these activity measures exist in terms of nerve impulse firing and the extracellular ionic currents they create at synapses, known as field potentials. As these field potentials reach the scalp, they produce the signal we call an electroencephalogram. Field potentials are technologically easier to record than individual nerve impulses, but more ambiguous to interpret because of the spatial summation of voltages from hundreds of heterogeneous neurons.

    The original nerve impulse findings were that the rate of impulse firing governed the impact on neuronal targets, whether they be muscle or other neurons. Various labs, including my own, in the 1980s discovered that the intervals between impulses also contained their own kind of information. For example, my lab reported that some neurons contained statistically significant serial ordering of impulse intervals in a neuron’s impulse stream. The intervals, at least in higher-level brain areas, are not random. They are serially dependent, as if they contained a message. If you are familiar with Markov transition probability, you can understand our finding that serial dependences exist in as many as five successive intervals (Sherry et al. 1982). This led us to suggest “byte processing” as a basic feature of neuronal information processing. This view has not caught on, and most people still seem to think that firing rate is the basic information code, despite the well-established temporal summation that occurs as impulses arrive at synapses. Bernard Katz demonstrated temporal summation of impulse effects in neuromuscular junctions in 1951 and later J.P. Segundo and colleagues confirmed it in neuronal synapses (Segundo et al., 1963).

     It should not be surprising that there are serial dependencies in impulse intervals. For example, intracellular recording of postsynaptic potentials revealed that the polarization change caused by a single impulse input decays in a few millisecond. However, a succession of closely spaced impulse inputs allows the polarization changes to summate.

    These days, the emphasis needs to be put on impulse activity in defined circuitry. All neurons are linked in one or more circuits, and the impulse train in any one neuron is only a small part of the over-all circuit activity. The function of any given circuit depends on the circuit impulse pattern (CIP) of the whole circuit. Researchers have developed microelectrodes that allow recording of impulse trains from single neurons, but the problem is in implanting a series of electrodes so that each one monitors the activity of a selected neuron in a defined.

    I think that research should focus on CIPs and the phase relationships of electrical activity among cortical circuits, both within and among cortical columns (Klemm, 2011). Nerve impulses have to be at the heart of consciousness, inasmuch as impulses contain the brain’s representation of information and create the synaptic field potentials.

    We know from monitoring known anatomical pathways for specific sensations that the brain creates a CIP representation of the stimuli. As long as the CIPs remain active, the representation of sensation or neural processing is intact and may even be accessible to consciousness. However, if something disrupts ongoing CIPs to create a different set of CIPs, as for example would happen with a different stimulus, then the original representation disappears. If the original CIPs persist long enough, a memory could form, but otherwise the information would be lost. The implication for memory formation is that the immediate period after learning must be protected from new inputs to keep the CIP representation of the learning intact long enough to form a more lasting memory.

    Much current research shows that conscious awareness correlates with the degree of synchrony and time-locking of CIPs in various regions and within regions of cortex. The evidence comes from electroencephalographic monitoring of the oscillating field potentials in a given area. These are voltage waves that occur in multiple frequency bands. Phase relationships of voltage waves from different circuits surely reflect the timing of the impulse discharges that create those fields. I summarized the animal research evidence for this view in my first book, some 50 years ago (Klemm, 1969). Depending on the nature of stimulus and mental state, these oscillations of various circuits may jitter with respect to each other or become time locked. The functional consequence of synchrony has to be substantial, and many others and I suggest that this is a fundamental aspect of consciousness. The correlation between frequency coherences and states of consciousness is clear. Frequency coherence reflects a “binding” of neurons into linked and shared electrochemical activity, but how this relates to conscious awareness will require a next great discovery in science.

    Sources:

    Klemm, W. R. (1969). Animal Electroencephalography. New York: Academic Press.
    Klemm, W. R. (2011). Atoms of Mind. The “Ghost in the Machine” Materializes. New York: Springer.
    Segundo, J. P., et al. (1963). Sensitivity of neurons in Aplysia to temporal pattern of arriving impulses. J. Exp. Biol. 40: 643-667.
    Sherry, C. J., Barrow, D. L., and Klemm, W. R. 1982. Serial dependen­cies and Markov processes of neuronal interspike intervals from rat cerebellum. Brain Res. Bull. 8: 163‑169.

    For more information, see my book, Mental Biology (Prometheus)


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