The capacity of the new brain simulator introduced by IBM exceeds the number of neurons and synapses in a cat's brain. The cortical simulator, called C2, recreates roughly 1 billion neurons connected by 10 trillion individual synapses. IEEE Spectrum carries an article on this new research platform titled, IBM Unveils a New Brain Simulator: A big step forward in a project that aims for thinking chips.
“Each neuron in the network is a faithful reproduction of what we now know about neurons,” [Jim Old] 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.
While the introduction of the simulator indicates that computer scientists are on track to build a simulator with the synaptic capacity of the human brain by 2019, it also suggests drawbacks in this approach for building supercomputers with human-level intelligence.
A major problem is power consumption. Dawn is one of the most powerful and power-efficient supercomputers in the world, but it takes 500 seconds for it to simulate 5 seconds of brain activity, and it consumes 1.4 MW. Extrapolating from today’s technology trends, IBM projects that the 2019 human-scale simulation, running in real time, would require a dedicated nuclear power plant.
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LOGICAL EXTRACTION OF NEOCORTEX STRUCTURE
I do not understand why the neocortex is a mystery to everyone. Its neuron net circuit is repeated throughout the cortex. It consists of excitatory and inhibitory neurons whose functions, each, have been known for decades. The neuron net circuit is repeated over layers whose axonal outputs feed on as inputs to other layers. The neurons of each layer, each receive axonal inputs from one or more sending layers and all that they can do is correlate the axonal input stimulus pattern with their axonal connection pattern from those inputs and produce an output frequency related to the resultant psps. Axonal growth toward a neuron is definitely the mechanism for permanent memory formation and it is just what is needed to implement conditioned reflex learning. This axonal growth must be under the control of the glial cells and must be a function of the signals surrounding the neurons. However, neurons need to do normalized cross correlations and need to recognize patterns. This requires the inclusion of the inhibitory neuron in the structure to complete the definition of the neocortex.
The cortex is known to be able to do pattern recognition and the correlation between an axonal input stimulus and an axonal input connection pattern is just what is needed to do pattern recognition. However, pattern recognition needs normalized correlations and a means to compare these correlations so that the largest correlation is recognized by the neurons. Without normalization, the psps’ relative values would not be bounded properly and could not be used to determine the best pattern match. In order to get psps to be compared so that the maximum psp neuron would fire, the inhibitory neuron is needed. By having a group of excitatory neurons feed an inhibitory neuron that feeds back inhibitory axonal signals to those excitatory neurons, one is able to have the psps of the excitatory neurons compared, with the neuron with the largest psps firing before the other do as the inhibitory signal decays after each excitatory stimulus, thus inhibiting the other excitatory neurons with the smaller psps. This inhibitory neuron is needed in order to achieve psp comparisons, no question about it. For a meaningful comparison, the psps must be normalized. As unlikely as it may seem possible, it comes out that the inhibitory connections growing by the same rules as excitatory connections, grow to a value which accomplishes the normalization. That is, as the excitatory axon pattern grows via conditioned reflex rules, the inhibitory axon to each excitatory neuron grows to a value equal to the square root of the sum of the squares of the excitatory connections. This can be shown by a mathematical analysis of a group of mutually inhibiting neurons under conditioned reflex learning. This normalization does not require the neurons to behave different from as known for decades, but rather requires that they interact with an inhibitory neuron as described.
Thus, by simply having the inhibitory neurons receive from neighboring excitatory neuron with large connection strengths where if the excitatory neuron fires, the inhibitory neuron fires and by allowing the inhibitory axonal signals be included with the excitatory axonal input signals to the inputs to those excitatory neurons, the neo-cortex is able to do normalized conditioned reflex pattern recognition as its basic function.
Dr. Ronald J. Swallow
rswallow@ptd.net
610 704 0914
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