What they found was that not all synapses follow the same learning rules. While some connections strengthened according to the classic Hebbian model, others changed completely independently.

It can be relatively cheap to gather a lot of bio-signal data. To teach a machine-learning algorithm to find a relationship between bio-signals and health outcomes, however, you need to teach the ...

"What we did in that paper is troubleshoot the biological implausibility present in prevailing machine learning algorithms," he said. "Our team explores mechanisms like Hebbian learning and spike ...

We then used machine learning algorithms trained on existing data ... we designed inhibitors that specifically target and block the proteins these genes produce. By analyzing the structure of ...

The machine-learning-designed ONNs show superior performance compared to other design methods (such as Hebbian learning), and they ... binary cross-entropy loss function. The top block diagram ...

This fabricated network is able to perform unsupervised Hebbian learning and spike-based learning. A new method for connecting neurons in neuromorphic wetware has been developed by researchers ...

DeepMind researchers work in areas ranging from robotics to neuroscience, and earlier this week the company demonstrated an algorithm capable of learning to perform manipulation tasks with a wide ...

How does bias in the way AI and machine-learning algorithms were “taught” influence how they function when applied in the real world? How do we interject human values into AI and machine ...