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Found 7 papers, 3 papers with code
Learning fast changing slow in spiking neural networks
Reinforcement learning (RL) faces substantial challenges when applied to real-life problems, primarily stemming from the scarcity of available data due to limited interactions with the environment.
Beyond spiking networks: the computational advantages of dendritic amplification and input segregation
Recent works have proposed that segregation of dendritic input (neurons receive sensory information and higher-order feedback in segregated compartments) and generation of high-frequency bursts of spikes would support error backpropagation in biological neurons.
Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networks
Visual object recognition has been extensively studied in both neuroscience and computer vision.
Burst-dependent plasticity and dendritic amplification support target-based learning and hierarchical imitation learning
The brain can learn to solve a wide range of tasks with high temporal and energetic efficiency.
Error-based or target-based? A unifying framework for learning in recurrent spiking networks
Finally, we show that our theoretical formulation suggests protocols to deduce the structure of learning feedback in biological networks.
Target spiking patterns enable efficient and biologically plausible learning for complex temporal tasks
We propose a novel target-based learning scheme in which the learning rule derived from likelihood maximization is used to mimic a specific spiking pattern that encodes the solution to complex temporal tasks.
Analysis and Model of Cortical Slow Waves Acquired with Optical Techniques
Here we combined wide-field fluorescence microscopy and a transgenic mouse model expressing a calcium indicator (GCaMP6f) in excitatory neurons to study SW propagation over the meso-scale under ketamine anesthesia.
