It's Monday morning of some week in 2050 and you're shuffling into your kitchen, drawn by the smell of fresh coffee C-3PO has brewed while he unloaded the dishwasher. "Here you go, Han Solo, I used the new flavor you bought yesterday," C-3PO tells you as he hands you the cup. C-3PO arrived barely a month ago and already has developed a wonderful sense of humor and even some snark. He isn't the real C-3PO, of course--you just named him that because you are a vintage movie buff--but he's the latest NeuroCyber model that comes closest to how people think, talk, and acquire knowledge. He's no match to the original C-3PO's fluency in 6 million forms of communication, but he's got full linguistic mastery and can learn from humans like humans do--from observation and imitation, whether it's using sarcasm or sticking dishes into slots. Unlike the early models of such assistants like Siri or Alexa who could recognize commands and act upon them, NeuroCybers can evolve into intuitive assistants and companions.

CSHL neuroscientist Anthony Zador shows how evolution and animal brains can be a rich source of inspiration for machine learning, especially to help AI tackle some enormously difficult problems, like... doing the dishes. Artificial intelligence (AI) still has a lot to learn from animal brains, says Cold Spring Harbor Laboratory (CSHL) neuroscientist Tony Zador. Now, he's hoping that lessons from neuroscience can help the next generation of artificial intelligence overcome some particularly difficult barriers. Anthony Zador, M.D., Ph.D., has spent his career working to describe, down to the individual neuron, the complex neural networks that make up a living brain. But he started his career studying artificial neural networks (ANNs).

CSHL neuroscientist Anthony Zador shows how evolution and animal brains can be a rich source of inspiration for machine learning, especially to help AI tackle some enormously difficult problems, like… doing the dishes. Artificial intelligence (AI) still has a lot to learn from animal brains, says Cold Spring Harbor Laboratory (CSHL) neuroscientist Tony Zador. Now, he's hoping that lessons from neuroscience can help the next generation of artificial intelligence overcome some particularly difficult barriers. Anthony Zador, M.D., Ph.D., has spent his career working to describe, down to the individual neuron, the complex neural networks that make up a living brain. But he started his career studying artificial neural networks (ANNs).

Sitting at the desk in his lower-campus office at Cold Spring Harbor Laboratory, the neuroscientist Tony Zador turned his computer monitor toward me to show off a complicated matrix-style graph. Imagine something that looks like a spreadsheet but instead of numbers it's filled with colors of varying hues and gradations. Casually, he said: "When I tell people I figured out the connectivity of tens of thousands of neurons and show them this, they just go'huh?' But when I show this to people …" He clicked a button onscreen and a transparent 3-D model of the brain popped up, spinning on its axis, filled with nodes and lines too numerous to count.

The cold spot consisted of a high density of a Ca-activated K channel.

The cold spot consisted of a high density of a Ca-activated K channel.

The cold spot consisted of a high density of a Ca-activated K channel.

We are exploring the significance of biological complexity for neuronal computation. Here we demonstrate that Hebbian synapses in realistically-modeled hippocampalpyramidal cells may give rise to two novel forms of self-organization in response to structured synaptic input. First, on the basis of the electrotonic relationships between synaptic contacts, a cell may become tuned to a small subset of its input space. Second, the same mechanisms may produce clusters of potentiated synapses across the space of the dendrites. The latter type of self-organization may be functionally significant in the presence of nonlinear dendritic conductances.