Neurons in this subnetwork shared a similar choice code during action preparation and formed recurrent functional connectivity across learning. Suppression of PPC activity disrupted choice selectivity in ALM and
Recurrently connected networks of spiking neurons underlie the astounding information processing capabilities of the brain. Yet in spite of extensive research, how they can learn through synaptic plasticity to carry out complex network computations
(2017). Critical Learning Periods in Deep Neural Networks,. Google Scholar Adams et al., 2015 Adams R.A., Friston K.J., Bastos A.M. Active inference, predictive coding and cortical architecture Casanova M.F., Opris I. (Eds.), Recent advances on the modular organization of the cortex,...
The tremendous increase in computing power since the 1990s now makes it possible to train large neural networks in a reasonable amount of time. This is in part due to Mooreâs Law, but also thanks to the gaming industry, which has produced powerful GPU cards by the millions. The ...
So cortical neurons do a lot of signal processing and they're very good at it, and yet they don't send real numbers to each other as far as we can tell, they send these spikes of activity that are one or zero and the timing of the spikes is random. Now this is completely crazy ...
Spatial learning and memory are components of the learning and memory domains in which particular locations are associated with distinct stimuli or cues, and these stimuli are used to learn the location of the object of interest. From: Molecules to Medicine with mTOR, 2016 ...
We present a simulation environment called SPIKELAB which incorporates a simulator that is able to simulate large networks of spiking neurons using a distr... C Grassmann,JK Anlauf - 《International Journal of Neural Systems》 被引量: 33发表: 1999年 Polarity of cortical electrical stimulation diffe...
神经网络结构复杂度及其功能表现:梯径理论与算法信息论 Correlating measures of hierarchical structures in artificial neural networks with their performance 热度: Learning and coding in biological neural networks - Harvard 热度: Hierarchical structure and the prediction of missing links in networks 热度...
2.2.2. Biologically plausible approximations of gradient descent 2.2.2.1. Temporal credit assignment: 2.2.2.2. Spiking networks 2.3. Other principles for biological learning 2.3.1. Exploiting biological neural mechanisms 2.3.2. Learning in the cortical sheet 2.3.3. One-shot learning Human learning is...
In Ref. 11, the accuracy of EEG-based emotion detection was improved through the utilization of technologies such as standard low-resolution electromagnetic tomography (sLORETA) and graph neural networks (GNNs). Additionally, the active EEG source-based GNN nodes (ESB-G3N) algorithm was utilized ...