Hu, S. et al. Real-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes.
Most current training algorithms for recurrent neural networks place hard limits on the potential timescales of learning.
Paper: "Real-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes" Video: "Open field location decoding".
S. Hu, Ciliberti, D., Grosmark, A. D., Michon, édéric, Ji, D., Penagos, H., áki, örgy, Wilson, M. A., Kloosterman, F., and Chen, Z., "Real-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes", Cell Reports, vol. 25, no. 10, pp. 2635 - 2642.e5, 2018.
Its goal is to create national neural observatories, places where neuroscientists will be able to collect high-quality neuron-by-neuron brain data from laboratory animals, and perhaps eventually humans.
Other physical theories have gone beyond the neural and placed the natural locus of consciousness at a far more fundamental level, in particular at the micro-physical level of quantum phenomena.
A source model was generated with regional neural sources placed in the regions of interest (ROIs) using BESA.
Apparently the effects of learning are first retained in the brain by some reversible process, after which a more permanent neural change takes place.
Tubular microcolumns can be flattened in fullerene-like two-dimensional lattices, equipped with about 80 nodes standing for pyramidal neurons where neural computations take place.
This study utilizes artificial neural networks in place of a traditional micromechanical approach to calculate the global (macroscopic) elastic properties of composite materials given the local (microscopic) properties and local geometry.
