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Found 15 papers, 7 papers with code
Neuromorphic dreaming: A pathway to efficient learning in artificial agents
We start from a baseline consisting of an agent network learning without a world model and dreaming, which successfully learns to play the game.
Adaptive behavior with stable synapses
However, such rapid changes are not coherent with the timescales of synaptic plasticity, suggesting that the mechanism responsible for that could be a dynamical network reconfiguration.
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.
Comparing apples to apples -- Using a modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets
Neuroscience is moving towards a more integrative discipline, where understanding brain function requires consolidating the accumulated evidence seen across experiments, species, and measurement techniques.
NREM and REM: cognitive and energetic gains in thalamo-cortical sleeping and awake spiking model
We demonstrate that sleep has a positive effect on energy consumption and cognitive performance during the post-sleep awake classification task of handwritten digits.
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.
Towards biologically plausible Dreaming and Planning in recurrent spiking networks
We propose a two-module (agent and model) spiking neural network in which "dreaming" (living new experiences in a model-based simulated environment) significantly boosts learning.
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.
Simulations Approaching Data: Cortical Slow Waves in Inferred Models of the Whole Hemisphere of Mouse
The model is capable to reproduce most of the features of the non-stationary and non-linear dynamics displayed by the high-resolution recording of the in-vivo mouse brain obtained by wide-field calcium imaging techniques.
Thalamo-cortical spiking model of incremental learning combining perception, context and NREM-sleep-mediated noise-resilience
The brain exhibits capabilities of fast incremental learning from few noisy examples, as well as the ability to associate similar memories in autonomously-created categories and to combine contextual hints with sensory perceptions.
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.
Scaling of a large-scale simulation of synchronous slow-wave and asynchronous awake-like activity of a cortical model with long-range interconnections
Cortical synapse organization supports a range of dynamic states on multiple spatial and temporal scales, from synchronous slow wave activity (SWA), characteristic of deep sleep or anesthesia, to fluctuating, asynchronous activity during wakefulness (AW).
Real-time cortical simulations: energy and interconnect scaling on distributed systems
We demonstrate the importance of the design of low-latency interconnect for speed and energy consumption.
Sleep-like slow oscillations improve visual classification through synaptic homeostasis and memory association in a thalamo-cortical model
The occurrence of sleep passed through the evolutionary sieve and is widespread in animal species.
