Mimicking Neurons With Math

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Dr. Takayuki Osogami

Artificial neural networks have long been studied with the hope of achieving a machine with the human capability to learn. Today’s attempts at artificial neural networks are built upon Hebb’s rule, which Dr. Donald Hebb proposed in 1949 as how neurons adjust the strength of their connections. Since Hebb, other “rules” of neural learning have been introduced to refine Hebb’s rule, such as spike-timing dependent plasticity (STDP). All of this helps us understand our brains, but makes developing artificial neurons even more challenging.

A biological neural network is too complex to exactly map into an artificial neural network. But IBM mathematician Takayuki Osogami and his team at IBM Research-Tokyomight have figured it out by developing artificial neurons that mathematically mimic STDP to learn words, images, and even music. Takayuki’s mathematical neurons form a new artificial neural network, creating a dynamic Boltzmann machine (DyBM) that can learn about information from multiple contexts through training.

The team taught seven artificial neurons the word “SCIENCE” (one artificial neuron per bit) in a form of a bitmap image. So, the image of “SCIENCE” becomes:

The “1s” equate to the lines making up the letters, while the “0s” translate to the white space around the letters.

Figure 1: The DyBM successfully learns two target sequences and retrieves a particular sequence when an associated cue is presented. (Credit: Scientific Reports)

Figure 2: The DyBM learned the target sequence of human evolution. (Credit: Scientific Reports)

Figure 3: The DyBM learned the target music. (Credit: Scientific Reports)

The scientific paper Seven neurons memorizingsequences of alphabetical images via spike-timing dependent plasticity by Takayuki Osogami and Makoto Otsuka appears in Scientific Reports of the Nature Publishing Group on September 16, 2015, DOI: 10.1038/SREP14149.