MAPPING NEURAL CONNECTIONS
نویسندگان
چکیده
منابع مشابه
Estimation of neural connections from partially observed neural spikes
Plasticity is one of the most important features of the nervous systems that enable animals to adjust their behavior to an ever-changing external world. A major mechanism of plasticity is the changes in synaptic efficacy between neurons, and therefore estimation of neural connections is crucial for investigating information processing in the brain. Although many analysis methods have been propo...
متن کاملDevelopment of input connections in neural cultures.
We introduce an approach for the quantitative assessment of the connectivity in neuronal cultures, based on the statistical mechanics of percolation on a graph. This allows us to monitor the development of the culture and to see the emergence of connectivity in the network. The culture becomes fully connected at a time equivalent to the expected time of birth. The spontaneous bursting activity ...
متن کاملReconfigurable Neural Net Chip with 32K Connections
We describe a CMOS neural net chip with a reconfigurable network architecture. It contains 32,768 binary, programmable connections arranged in 256 'building block' neurons. Several 'building blocks' can be connected to form long neurons with up to 1024 binary connections or to form neurons with analog connections. Singleor multi-layer networks can be implemented with this chip. We have integrat...
متن کاملConnections between Neural Networks and Boolean Functions∗
This report surveys some connections between Boolean functions and artificial neural networks. The focus is on cases in which the individual neurons are linear threshold neurons, sigmoid neurons, polynomial threshold neurons, or spiking neurons. We explore the relationships between types of artificial neural network and classes of Boolean function. In particular, we investigate the type of Bool...
متن کاملMemory Augmented Neural Networks with Wormhole Connections
Recent empirical results on long-term dependency tasks have shown that neural networks augmented with an external memory can learn the long-term dependency tasks more easily and achieve better generalization than vanilla recurrent neural networks (RNN). We suggest that memory augmented neural networks can reduce the effects of vanishing gradients by creating shortcut (or wormhole) connections. ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: BioTechniques
سال: 2014
ISSN: 1940-9818,0736-6205
DOI: 10.2144/000114224