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Great discussion. I had it on while working, so I didn’t absorb too much. I’m surely not going to take a side in this discussion. I understand tunneling just a little better now. They didn’t spend too much time on it, but seemed in agreement that the small variations that quantum physics introduces are partly an explanation for evolution.
Ran across this article.
How Computationally Complex Is a Single Neuron?
The most basic analogy between artificial and real neurons involves how they handle incoming information. Both kinds of neurons receive incoming signals and, based on that information, decide whether to send their own signal to other neurons. While artificial neurons rely on a simple calculation to make this decision, decades of research have shown that the process is far more complicated in biological neurons. Computational neuroscientists use an input-output function to model the relationship between the inputs received by a biological neuron’s long treelike branches, called dendrites, and the neuron’s decision to send out a signal.
This function is what the authors of the new work taught an artificial deep neural network to imitate in order to determine its complexity. They started by creating a massive simulation of the input-output function of a type of neuron with distinct trees of dendritic branches at its top and bottom, known as a pyramidal neuron, from a rat’s cortex. Then they fed the simulation into a deep neural network that had up to 256 artificial neurons in each layer. They continued increasing the number of layers until they achieved 99% accuracy at the millisecond level between the input and output of the simulated neuron. The deep neural network successfully predicted the behavior of the neuron’s input-output function with at least five — but no more than eight — artificial layers. In most of the networks, that equated to about 1,000 artificial neurons for just one biological neuron.
Neuroscientists now know that the computational complexity of a single neuron, like the pyramidal neuron at left, relies on the dendritic treelike branches, which are bombarded with incoming signals. These result in local voltage changes, represented by the neuron’s changing colors (red means high voltage, blue means low voltage) before the neuron decides whether to send its own signal called a “spike.” This one spikes three times, as shown by the traces of individual branches on the right, where the colors represent locations of the dendrites from top (red) to bottom (blue).