Pink Noise - Leonid Korogodski [47]
If we only had excitatory neurons, they would have quickly synchronized, all neurons in the brain firing in perfect unison, as pendulums that stand on the same floor influence each other via mutual feedback to spontaneously synchronize their oscillations in a process called entrainment. Their clocks begin to tick together. But perfect unison is an extremely simple structure; it does not support complexity. Neuronal oversynchronization is, in fact, what happens during an epileptic fit (grand mal); predictably, the person is unconscious while it lasts.
Inhibitory neurons create complexity, by enabling competition. When an excitatory neuron fires to another, it wakes up its inhibitory allies, which try to silence other neurons that want to send similar signals. The winner takes all. Moreover, the winner is rewarded further: the firing neuron-to-neuron synapses get stronger, so that they are more likely to win in the future. Synapses get weaker if they don’t fire for a while. This process is called brain plasticity—the brain keeps modifying itself to get smarter, better at reacting to new situations. This is how, for example, we can learn tasks to such a level of perfection that we can perform them on auto-pilot—learning new faps, getting rewired.
In the brain, massively parallel neuronal ensembles thus compete to deliver the best results, comparing their predictions with the feedback from external action, making corrections. The impression that our brain is “single-threaded” is an illusion, for we only perceive the results of massively parallel computations, like many teams that work on the same task. And if you think that our memory capacity is low just because we can juggle only a handful of objects in our mind at the same time, consider how much information is involved in just one object, taking into account all sensory inputs, not to mention interaction with the object, such that the number of forking paths—decisions made on the basis of the object’s proper ties—can grow exponentially. As Daniel Dennett (b. 1942), one of the proponents of Neural Darwinism, put it in [10]:
Throw a skeptic a dubious coin, and in a second or two of hefting, scratching, ringing, tasting, and just plain looking at how the sun glints on its surface, the skeptic will consume more bits of information than a Cray supercomputer can organize in a year.
NEURONS ARE NATURAL OSCILLATORS OF ELECTRICAL potential across the cellular membrane. When they fire to each other, they can synchronize, producing what we call brain oscillations, or brainwaves.
The random variations, necessary to drive the neuronal selection, follow the pink noise distribution as 1/f, the amplitude (strength) of oscillations being inversely proportional to their frequency. A noise following a more general 1/f a distribution is called fractal noise, where the number a is its fractal dimension. When a < 1, chaos is stronger than order; when a > 1, order is stronger than chaos. But when a = 1, this is the zone of the highest complexity, if complexity is measured by the number of states that the system can tell apart from each other. In other words, a = 1 is when the butterfly effect is most strongly felt. Of course, the higher the number of states the system can distinguish between, the higher the amount of information the system can contain. Pink noise is the most informationally dense noise in the universe.
1/f noise is called pink because, if the frequencies were those of visible light, the resulting color would be closer to the red part of the spectrum, intuitively expected to be pink. Although calculations show that the color is closer to golden tan, no one is about to rename pink noise.
Once again, the evolutionary process has spontaneously established a perfect balance between order and chaos. On one hand, the oscillations in the brain must synchronize, for this is precisely how the inputs from disparate sources combine in order to produce a cognitive event. Yet on the other hand, oversynchronization