Artificial neural networks (ANNs) have undergone a revolution, catalyzed by better supervised learning algorithms. However, in stark contrast to young animals (including humans), training such networks requires enormous numbers of labeled examples, leadi
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 ...
Learning constructed functional network motifs for short-term memory where subnetworks consisting of neurons with similar task relevance were embedded in a sparsely connected global network. The cortical network motifs were further elaborated during learning by selective strengthening of a region-specific ...
Unsupervised lifelong learning, on the other hand, has been proposed mostly through the use of self-organizing neural networks (e.g., Parisi et al., 2017, Parisi et al., 2018 and Richardson and Thomas (2008)). Although significant advances have been made in the design of learning methods ...
PyPi Package of Self-Organizing Recurrent Neural Networks (SORN) and Neuro-robotics using OpenAI Gym machine-learningreinforcement-learningcomplex-networksreservoir-computingneuroinformaticshopfield-networkhebbian-learningautonomous-agentscortical-learningcortical-networkpoint-neuronsself-organizing-networkneuroplasticitybol...
Biophysics Steady-state learning and synaptic connectivity in local cortical networks of excitatory and inhibitory neuronsa NORTHEASTERN UNIVERSITY Armen Stepanyants ChapetonJulioLearning and memory storage are arguably the most fundamental and well-studied functions of the mammalian cortex. It is established...
A laminar cortical model of stereopsis and 3D surface perception is developed and simulated. The model shows how spiking neurons that interact in hierarchi... Y Cao,S Grossberg - 《Neural Networks the Official Journal of the International Neural Network Society》 被引量: 41发表: 2012年 Visual ...
35 However, most prior studies of learning-induced cortical changes have either focused only on single ACtx subregions36,37 or have only investigated certain time points during learning. Therefore, it is unclear how learning-induced modulations emerge in the processing hierarchy. We hypothesized that...
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
networks. These problems are addressed with multiscale models where only some parts of the brain are simulated at a finer scale (for example, at the level of spiking neurons45) while the remaining parts are simulated by a coarser network to save computational resources. In addition, by ...