Archive

Posts Tagged ‘neural networks’

Neural Networks Forget Information Quickly

December 16, 2012 Leave a comment

Researchers have figured out the speed that neural networks in the cerebral cortex can delete sensory information is a bit of information per active neuron per second. The activity patterns of the neural network models are deleted nearly as soon as they are passed on from sensory neurons.

The scientists used neural network models based on real neuronal properties for the first time for these calculations. Neuronal spike properties were figured into the models which also helped show that the cerebral cortex processes were extremely chaotic.

Neural networks and this type of research in general are all helping researchers better understand learning and memory processes. With better knowledge about learning and memory, researchers can work toward treatments for Alzheimer’s disease, dementia, learning disabilities, PTSD related memory loss and many other problems.

More details are provided in the release below. Read more…

Advertisements

A Glance at the Brain’s Circuit Diagram

December 16, 2012 3 comments

A new method facilitates the mapping of connections between neurons.

The human brain accomplishes its remarkable feats through the interplay of an unimaginable number of neurons that are interconnected in complex networks. A team of scientists from the Max Planck Institute for Dynamics and Self-Organization, the University of Göttingen and the Bernstein Center for Computational Neuroscience Göttingen has now developed a method for decoding neural circuit diagrams. Using measurements of total neuronal activity, they can determine the probability that two neurons are connected with each other.

The human brain consists of around 80 billion neurons, none of which lives or functions in isolation. The neurons form a tight-knit network that they use to exchange signals with each other. The arrangement of the connections between the neurons is far from arbitrary, and understanding which neurons connect with each other promises to provide valuable information about how the brain works. At this point, identifying the connection network directly from the tissue structure is practically impossible, even in cell cultures with only a few thousand neurons. In contrast, there are currently well-developed methods for recording dynamic neuronal activity patterns. Such patterns indicate which neuron transmitted a signal at what time, making them a kind of neuronal conversation log. The Göttingen-based team headed by Theo Geisel, Director at the Max Planck Institute for Dynamics and Self-Organization, has now made use of these activity patterns. Read more…