spike-timing-dependent synaptic plasticity [a biological process that adjusts the strength of connections between neurons in the brain].
X: What is a spiking neuron? I’m visualizing an electric signal that pulses up and back, like the strongman hitting the bell in a carnival high striker game. Or is it more accurate to say that a neuron is either “on” or “off?”
EI: The strongman analogy is good. Neurons are typically quiet for long periods of time, then fire a brief pulse (called an action potential or a spike) or a burst of spikes, in response to signals arriving from other neurons. The exact details depend on where the neuron is.
Imagine 100 such spiking neurons, each firing just one spike per second. If we consider the timing of these neuron spikes, such a system has more than 10^160 (ten with 160 zeros) of possible combinations, each representing a pattern of spikes.
Not only is this number greater than the number of particles in the known universe (which is 10^80), it also is greater than the number of pair-wise combinations of all the particles. It is hard to comprehend how large this number is. It is infinite from any practical point of view, yet we can achieve such a combinatorially large capacity in a network of just 100 neurons—as long as we capture the timing of spikes. Now, imagine not 100, but 100 billion neurons! Any researcher or a company that figures out how to use this will unlock the key to the neural computations in the brain, and enable a trillion-dollar technology. Even a partial success would enable smart consumer devices that behave less like robots and more like animals.
X: Is it accurate to say your first breakthrough was developing an algorithm to describe the biological process of spiking neurons? How, or why, was it important?
EI: Correct. Thousands of researchers use my model (and refer to it by my name) as a computationally efficient way to simulate spiking and bursting activity in neurons. The model, published in 2003, paved the way to simulate millions or billions of neurons with firing patterns similar to those observed in the brain. The model captures the essence of neural computation taking place inside each neurons to the degree that if I stimulate a real neuron and the model neuron with the same stimulus, show the results to an expert neurobiologist, the expert would not be able to tell the difference.
X: Did this algorithm make it possible to develop the large-scale computer model of a normal human brain?
EI: Yes, all the way to the 100 billion neurons.
X: Was this the key innovation that made you think this was technology that could be commercialized? If not, what was that key innovation?
EI: The key technology breakthroughs are (a) the development of the efficient model of spiking neurons, (b) the development of various forms of spike-timing-dependent synaptic plasticity resulting in emergence of neuronal computations, and (c) the availability of high-performance processors and a path to develop a new generation of specialized processors that take our simulations to the next level.
