In this study, facing the booming development of neuromorphic computing using spiking neural networks (SNNs), we built a spiking continuous attractor neural network (S-CANN) with SFA to implement anticipative tracking. Further, we simplified the model, in terms of connection weights, external inputs, and network size, to facilitate its impleme...
Online versus offline learning for spiking neural networks: A review and new strategies 2010 IEEE 9th International Conference on Cyberntic Intelligent Systems (2010), pp. 1-6, 10.1109/UKRICIS.2010.5898113 Google Scholar [7] Martín C., Langendoerfer P., Zarrin P.S., Díaz M., Rubio B. ...
Neurons can be rate based or spiking. The random motion model is fitted to match real rodent motion. Flexible: Simulate environments in 1D or 2D with arbitrarily wall, boundary and hole arrangements. Combine premade or bespoke Neurons classes into arbitrary deep networks (examples given). Fast: ...
Accordingly, neural networks provide a mechanism that will generalize the classification to new stimuli, although one has to admit, that we humans tend also to be able do that. However, this neat feature of learning a decision pattern that can generalize comes at a price. If the data is ...
We obtained, with artificial evolution, very small (one or two interneurons, one output neuron) spiking neural networks (SNNs) recognizing a simple temporal pattern in a continuous input stream. The patterns the network evolved to recognize consisted of three different signals. In other words, the...
Various methodologies for bridging these two domains are discussed, including Graph Neural Networks, Spiking Neural Networks, and Neuro-Symbolic Goal and Plan Recognition. These techniques show promise for advancing Neuro-Symbolic AI, presenting solutions to the challenge of integrating continuous learning ...