Yalawar et al., 2023
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Brain-inspired computing: A systematic survey and future trends | |
| Frenkel et al. | Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligence | |
| Recanatesi et al. | Predictive learning as a network mechanism for extracting low-dimensional latent space representations | |
| Javanshir et al. | Advancements in algorithms and neuromorphic hardware for spiking neural networks | |
| Hu et al. | Neuroscience and network dynamics toward brain-inspired intelligence | |
| Song et al. | Training excitatory-inhibitory recurrent neural networks for cognitive tasks: a simple and flexible framework | |
| Legenstein et al. | Reinforcement learning on slow features of high-dimensional input streams | |
| Yu et al. | Precise-spike-driven synaptic plasticity: Learning hetero-association of spatiotemporal spike patterns | |
| Yalawar et al. | A brain-inspired cognitive control framework for artificial intelligence dynamic system | |
| Walter et al. | Computation by time | |
| Nere et al. | A neuromorphic architecture for object recognition and motion anticipation using burst-STDP | |
| Bahmer et al. | Modern artificial neural networks: Is evolution cleverer? | |
| Guo et al. | A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems | |
| Casanueva-Morato et al. | Spike-based computational models of bio-inspired memories in the hippocampal CA3 region on SpiNNaker | |
| Rounds et al. | An evolutionary framework for replicating neurophysiological data with spiking neural networks | |
| Santos-Pata et al. | Epistemic autonomy: self-supervised learning in the mammalian hippocampus | |
| Kungl | Robust learning algorithms for spiking and rate-based neural networks | |
| Rakytyanska | Knowledge distillation in granular fuzzy models by solving fuzzy relation equations | |
| Arel | The threat of a reward-driven adversarial artificial general intelligence | |
| Ott | Questions to guide the future of artificial intelligence research | |
| Razzaq et al. | Neural Circuit Policies for Virtual Character Control | |
| Plebe et al. | The brain in silicon: History, and skepticism | |
| Zins | Neuromorphic Computing Applications in Robotics | |
| Balwani et al. | Constructing Biologically Constrained RNNs via Dale’s Backprop and Topologically-Informed Pruning | |
| Candadai | Information theoretic analysis of computational models as a tool to understand the neural basis of behaviors |