Conditioning Devices Like Pavlov’s Dogs

AsianScientist (Jun. 3, 2021) -In a modern update to Ivan Pavlov’s seminal conditioning experiments, researchers from Hong Kong and the US have developed a brain-like computing device capable of learning by association. Their findings were published in Nature Communications.

At the turn of the 20^th century, Pavlov revolutionized psychology by suggesting that salivation was a learned response. While studying the digestion of dogs, he observed that ringing a bell during feeding conditioned his canine subjects to associate the sound with meals. Over time, whenever the bell rang, the dogs would drool—regardless of whether food was present or not.

While this behavior, known as learning by association, comes naturally to organisms like dogs and humans—computing systems find it much trickier. Typically, computers store and process information separately, causing data-intensive tasks like associative learning to consume large amounts of energy. In contrast, our brains can simultaneously perform both functions.

To overcome the traditional drawbacks of computers, a team led by Associate Professor Paddy Chan from the University of Hong Kong and Assistant Professor Jonathan Rivnay from Northwestern University took inspiration from the brain in developing their new device. Within the brain, neurons transmit signals to one another by passing small molecules known as neurotransmitters through synapses.

Seeking to recreate this process, the researchers optimized a conductive, plastic material within an organic electrochemical transistor to trap ions. In their ‘synaptic transistor,’ the ions behave like neurotransmitters, sending signals between the terminals to form an artificial synapse. By retaining stored data from the trapped ions, the transistor could remember previous stimuli—building on memories to learn by association over time.

To demonstrate their device’s newfound abilities, the researchers connected their synaptic transistors into a circuit with pressure and light sensors. By exposing the circuit to light and then immediately applying pressure, the team gradually conditioned the circuit to associate the two unrelated physical inputs much like Pavlov’s dogs nearly a century ago. Except, in this scenario, the light is the bell and the pressure is the food.

After one training cycle, the circuit made an initial connection between light and pressure, with its sensors detecting both inputs. Over five training cycles later, light exposure alone was capable of triggering a signal.

With its brain-like ability, the novel transistor and circuit could potentially overcome the current limitations of computers, including their energy-sapping hardware and limited ability to perform multiple tasks simultaneously.

Because the synaptic circuit is made of soft plastic-like polymers, it can also be easily integrated into wearable electronics as well as implantable devices with direct connections to living tissue and even the brain itself.

“While our application is a proof of concept, our proposed circuit can be further extended to include more sensory inputs and integrated with other electronics to enable on-site, low-power computation,” concluded Rivnay. “Because it is compatible with biological environments, the device can directly interface with living tissue, which is critical for next-generation bioelectronics.”

The article can be found at: Ji et al. (2021) Mimicking Associative Learning Using an Ion-trapping Non-volatile Synaptic Organic Electrochemical Transistor.

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Source: Northwestern University. Photo credit: Northwestern University.
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