Lifeboat Foundation

For decades the efficient coding hypothesis has been a guiding principle in determining how neural systems can most efficiently represent their inputs. However, conclusions about whether neural circuits are performing optimally depend on assumptions about the noise sources encountered by neural signals as they are transmitted. Here, we provide a coherent picture of how optimal encoding strategies depend on noise strength, type, location, and correlations. Our results reveal that nonlinearities that are efficient if noise enters the circuit in one location may be inefficient if noise actually enters in a different location. This offers new explanations for why different sensory circuits, or even a given circuit under different environmental conditions, might have different encoding properties.

Citation: Brinkman BAW, Weber AI, Rieke F, Shea-Brown E (2016) How Do Efficient Coding Strategies Depend on Origins of Noise in Neural Circuits? PLoS Comput Biol 12(10): e1005150. doi:10.1371/journal.pcbi.1005150

Editor: Jeff Beck, Duke University, UNITED STATES

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The DeepMind artificial intelligence (AI) being developed by Google’s parent company, Alphabet, can now intelligently build on what’s already inside its memory, the system’s programmers have announced.

Their new hybrid system – called a Differential Neural Computer (DNC) – pairs a neural network with the vast data storage of conventional computers, and the AI is smart enough to navigate and learn from this external data bank.

Continue reading “Google’s AI can now learn from its own memory independently” »

As Uber pushes ahead with plans for self-driving cars, there are differing views on the roles of drivers in that future.

The robot uses trial and error to perfect complex tasks.

A new mathematical model of memory could accelerate the quest to build super-powered, brain-inspired hardware systems.

The American Dream is ending, and its automated software and hardware technology that’s ending it.

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Researchers from the University of Pittsburgh and UPMC have developed a system that’s enabling a man with quadriplegia to experience the sensation of touch through a robotic arm that he controls with his brain.

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