To find out which sights specific neurons in monkeys "like" best, researchers designed an algorithm, called XDREAM, that generated images that made neurons fire more than any natural images the researchers tested. As the images evolved, they started to look like distorted versions of real-world stimuli. The work appears in the journal Cell.
"When given this tool, cells began to increase their firing rate beyond levels we have seen before, even with normal images pre-selected to elicit the highest firing rates," explains co-first author.
"What started to emerge during each experiment were pictures that were reminiscent of shapes in the world but were not actual objects in the world," the co-author says. "We were seeing something that was more like the language cells use with each other."
Researchers have known that neurons in the visual cortex of primate brains respond to complex images, like faces, and that most neurons are quite selective in their image preference. Earlier studies on neuronal preference used many natural images to see which images caused neurons to fire most. However, this approach is limited by the fact that one cannot present all possible images to understand what exactly will best stimulate the cell.
The XDREAM algorithm uses the firing rate of a neuron to guide the evolution of a novel, synthetic image. It goes through a series of images over the course of minutes, mutates them, combines them, and then shows a new series of images. At first, the images looked like noise, but gradually they changed into shapes that resembled faces or something recognizable in the animal's environment, like the food hopper in the animals' room or familiar people wearing surgical scrubs.
"The big advantage of this approach is that it allows the neuron to build its own preferred images from scratch, using a tool that is not limited by much, that can create anything in the world or even things that don't exist in the world," says the author.
"In this way we have evolved a super-stimulus that drives the cell better than any natural stimulus we could guess at," says the senior author. "This approach allows you to use artificial intelligence to figure out what triggers neurons best. It's a totally unbiased way of asking the cell what it really wants, what would make it fire the most."
From this study, the researchers believe they are seeing that the brain learns to abstract statistically relevant features of its world. "We are seeing that the brain is analyzing the visual scene, and driven by experience, extracting information that is important to the individual over time," says the author. "The brain is adapting to its environment and encoding ecologically significant information in unpredictable ways."
The team believes this technology can be applied to any neuron in the brain that responds to sensory information, such as auditory neurons, hippocampal neurons, and prefrontal cortex neurons where memories can be accessed. "This is important because as artificial intelligence researchers develop models that work as well as the brain does - or even better - we will still need to understand which networks are more likely to behave safely and further human goals," the author says. "More efficient AI can be grounded by knowledge of how the brain works."
https://www.cell.com/cell/fulltext/S0092-8674(19)30391-5
Images designed by AI to super-stimulate monkey neurons
- 1,441 views
- Added
Edited
Latest News
Why killer T cells lose ene…
By newseditor
Posted 19 Mar
Brain hormone regulate both…
By newseditor
Posted 17 Mar
Blocking long non-coding RN…
By newseditor
Posted 17 Mar
Artificial intelligence and…
By newseditor
Posted 17 Mar
Blood-brain barrier protein…
By newseditor
Posted 17 Mar
Other Top Stories
Blastocyst complementation to generate kidneys in rats from stem cells
Read more
Stem cell derived 3D brain cancer cell organoid model for drug test…
Read more
A centrosome protein regulates brain stem cells
Read more
Nicotine may harm human embryos at the single-cell level
Read more
How cellular context determines the signaling pathway
Read more
Protocols
A mouse DRG genetic toolkit…
By newseditor
Posted 17 Mar
An optogenetic method for t…
By newseditor
Posted 13 Mar
Profiling native pulmonary…
By newseditor
Posted 08 Mar
Neuromuscular organoids mod…
By newseditor
Posted 06 Mar
In situ combinatorial synth…
By newseditor
Posted 03 Mar
Publications
Acetyl-CoA carboxylase obst…
By newseditor
Posted 19 Mar
Crosstalk between TPC2 and…
By newseditor
Posted 19 Mar
Neutrophil-inflicted vascul…
By newseditor
Posted 19 Mar
Synaptopathy: presynaptic c…
By newseditor
Posted 18 Mar
Allergic Rhinitis
By newseditor
Posted 18 Mar
Presentations
Hydrogels in Drug Delivery
By newseditor
Posted 12 Apr
Lipids
By newseditor
Posted 31 Dec
Cell biology of carbohydrat…
By newseditor
Posted 29 Nov
RNA interference (RNAi)
By newseditor
Posted 23 Oct
RNA structure and functions
By newseditor
Posted 19 Oct
Posters
A chemical biology/modular…
By newseditor
Posted 22 Aug
Single-molecule covalent ma…
By newseditor
Posted 04 Jul
ASCO-2020-HEALTH SERVICES R…
By newseditor
Posted 23 Mar
ASCO-2020-HEAD AND NECK CANCER
By newseditor
Posted 23 Mar
ASCO-2020-GENITOURINARY CAN…
By newseditor
Posted 23 Mar