Oscillations in an artificial neural network convert competing inputs into a temporal code

• Computer vision is a subfield of artificial intelligence focused on developing artificial neural networks (ANNs) that classify and generate images. Neuronal responses to visual features and the anatomical structure of the human visual system have traditionally inspired the development of computer vision models. The visual cortex also produces rhythmic activity that has long been suggested to support visual processes. However, there are only a few examples of ANNs embracing the temporal dynamics of the human brain. Here, we present a prototype of an ANN with biologically inspired dynamics—a dynamical ANN. We show that the dynamics enable the network to process two inputs simultaneously and read them out as a sequence, a task it has not been explicitly trained on. A crucial component of generating this dynamic output is a rhythm at about 10Hz, akin to the so-called alpha oscillations dominating human visual cortex. The oscillations rhythmically suppress activations in the network and stabilise its dynamics. The presented algorithm paves the way for applications in more complex machine learning problems. Moreover, we present several predictions that can be tested using established neuroscientific approaches. As such, the presented work contributes to both artificial intelligence and neuroscience.

SBIRs

• Read through and edit white paper.
• 9:00 standup
• 9:30 FOM demo discussion
• 11:00 Deltek focus group
• 12:45 USNA
• 4:30 Book club

GPT Agents

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