Artificial Intelligence models

Olds says the IBM work is extremely promising. “Each neuron in the network is a faithful reproduction of what we now know about neurons,” he says. This in itself is an enormous step forward for neuroscience, but it also allows neuroscientists to do what they have not previously been able to do: rapidly test their own hypotheses on an accurate replica of the brain. “It’s like the Large Hadron Collider in that respect,” says Olds.IEEE Spectrum

HT Overcoming Bias
This is research to actually simulate neurons, rather than emulate intelligence. But, even so, when reading articles about big giga simulations, it is worthwhile to remember what computers really do, compare bytes, count with integers, make memory addresses and send data around in serial fashion.

So, we have a machine that we can duplicate many times, but does four things.

What happens when they simulate the midbain of a bunch of cat neurons? The neuronsd\ at equilibrium encode most of the cat movements with minimal firing. An easy measurements is the current draw on the giga computer. When is does most cat things and has the lowest current draw, then they likely have it. So neurons at equilibrium are all about capturing the typical schema, they encode for certain fields that need to be filled in. These fields are counter initializers, they start a sequence of counters that count out a set of micro actions the cat needs. The micro actions are phased locked to events, what wide brain scientists might call Gibbs events, the brain avoids overlaps.

Bit, byte, neuron, its all about maximum entropy encoding, Shannon.