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Designing artificial brains can help us learn more about real ones

#artificialintelligence

In our paper, we propose that the same holds true for the brain, and so we have to move away from trying to understand the role of each neuron in the brain and instead look at the brain's architecture, that is its network structure; the optimization goals, either at the evolutionary timescale or within the person's lifetime; and the rules by which the brain updates itself -- either over generations or within a lifetime -- to meet those goals. By defining these three components, we may get a much better understanding of how the brain works than by trying to state what each neuron does.


What Is Machine Learning - A Complete Beginner's Guide In 2017

#artificialintelligence

Today things are a little different – thanks to the rollout of the internet, the proliferation of mobile, data-gathering phones and other devices and the adoption of online, connected technology in industry, we literally have more data than we know how to deal with. No human brain can hope to process even a fraction of the digital information it has available. The idea that it can, is one half of what is driving the world-changing breakthroughs we are seeing today. The other half is the "brain" of machine learning. Because as well as simply ingesting data, a machine has to process it in order to learn.


Shallow Unorganized Neural Networks using Smart Neuron Model for Visual Perception

arXiv.org Artificial Intelligence

The recent success of Deep Neural Networks (DNNs) has revealed the significant capability of neuromorphic computing in many challenging applications. Although DNNs are derived from emulating biological neurons, there still exist doubts over whether or not DNNs are the final and best model to emulate the mechanism of human intelligence. In particular, there are two discrepancies between computational DNN models and the observed facts of biological neurons. First, human neurons are interconnected randomly, while DNNs need carefully-designed architectures to work properly. Second, human neurons usually have a long spiking latency (~100ms) which implies that not many layers can be involved in making a decision, while DNNs could have hundreds of layers to guarantee high accuracy. In this paper, we propose a new computational neuromorphic model, namely shallow unorganized neural networks (SUNNs), in contrast to DNNs. The proposed SUNNs differ from standard ANNs or DNNs in three fundamental aspects: 1) SUNNs are based on an adaptive neuron cell model, Smart Neurons, that allows each neuron to adaptively respond to its inputs rather than carrying out a fixed weighted-sum operation like the neuron model in ANNs/DNNs; 2) SUNNs cope with computational tasks using only shallow architectures; 3) SUNNs have a natural topology with random interconnections, as the human brain does, and as proposed by Turing's B-type unorganized machines. We implemented the proposed SUNN architecture and tested it on a number of unsupervised early stage visual perception tasks. Surprisingly, such shallow architectures achieved very good results in our experiments. The success of our new computational model makes it a working example of Turing's B-Type machine that can achieve comparable or better performance against the state-of-the-art algorithms.


Late to the party

Science

Throughout life, new neurons are added to the brain. Just like people arriving late to a cocktail party, the newbies need to figure out how to integrate with those already embroiled in conversations. The zebrafish brain, already capable of complex visual processing at larval stages, accepts new neurons throughout the fish life span. Boulanger-Weill et al. tracked the location, movement, and functional integration of single newborn neurons in developing zebrafish larvae. Following their own developmental trajectories, newborn neurons began with limited dendritic arbors, no neurotransmitter identity, and spontaneous, but not directed, activity.


These Neurons are Alive and Firing. And You Can Watch Them In 3-D

WIRED

For patients with epilepsy, or cancerous brain lesions, sometimes the only way to forward is down. Down past the scalp and into the skull, down through healthy grey matter to get at a tumor or the overactive network causing seizures. At the end of the surgery, all that extra white and grey matter gets tossed in the trash or an incinerator. For the last few years, doctors at a number of hospitals in the Emerald City have been saving those little bits and blobs of brain, sticking them on ice, and rushing them off in a white van across town to the Allen Institute for Brain Science. Scientists there have been keeping the tissue on life support long enough to tease out how individual neurons look, act, and communicate.