Published on December 10th, 2013

Synaptic Transistors Should Improve Brain Models

Creating a working synaptic-transistor device should help with the modeling of neurons in the Human Brain Project. But would the IP be open to all?

Harvard scientists have created a transistor that mimics the behavior of a neural synapse, notes a recent article in R&D. This new device modulates the flow of information in a circuit while physically adapting to changing signals. A system that integrates millions of synaptic transistors could open the door to a new area of parallel computing – beyond today’s semiconductor-based systems. But what do synaptic devices modeled after semiconductor-transistor technology mean for efforts to synthesize human thought?

Last month, Karlheinz Meier, Chair of the Experimental Physics department at Heidelberg University, presented at Malcolm Penn’s IEF event in Dublin, Ireland. Professor Meier shared the latest highlights of the Human Brain Project (HBP), which is developing a novel computing architecture for synthesizing thought.

The Blue Brain Project (BBP), part of the Human Brain project, is attempting to reconstruct the brain piece by piece to build a virtual brain in a supercomputer. In practice, the BBP is reverse engineering the mammalian brain down to the molecular level. At that level, cells in the nervous system (known as neurons) serve as the basic building blocks. Neurons are different from other cells in that they can transmit information throughout the body via synaptic interfaces.

Neurons are a key element of both the Harvard synaptic-transistor and brain-simulation projects. Understanding the device characteristics of these unique transistors should lead to greater accuracy of neural device modeling in the Human Brain Project. This is analogous to improved transistors leading to better device modeling in analog semiconductor design.

One is tempted to ask if a neuron is the equivalent of a semiconductor transistor. It’s more accurate to define a neuron or neural cell as an “entity” on the silicon substrate. I defer this discussion for another blog. (See “A Working Transistor Built Out Of DNA Within A Living Cell“)

Creating a working synaptic-transistor device that ultimately improves neuron models of the human brain seems analogous to transistor creation and device modeling in the semiconductor world. One big challenge in the latter is the openness of the data used to create the model. Will the biological designers follow an intellectual-property (IP) model similar to that of the semiconductor world?

I asked Professor Meier about the IP and access issues associated with the Human Brain Project in terms of aggregating the neuroscience brain data, which is a prerequisite for modeling and simulation. Would that data be open to all? Would there be any IP issues?

In response, Professor Meier directed me to Paul Allen’s mouse brain project. Last year, software billionaire Allen pledged $300 million to establish a series of “brain observatories” in Seattle. The goal of those observatories was to map and manipulate the brain of a mouse as the first step in understanding higher-order mammalian brains.

According to Meier, data from Allen’s brain observatories are totally open. “We would make (our Human Brain Project) data open too,” said Meier. “Intellectual property becomes important when developing a circuit. But for the biological data (from the HBP), there is no secret data of which I’m aware.”

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