A review of learning in biologically plausible spiking neural networks
Artificial neural networks have been used as a powerful processing tool in various areas
such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has …
such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has …
[PDF][PDF] Synaptic plasticity, engrams, and network oscillations in amygdala circuits for storage and retrieval of emotional memories
The neuronal circuits of the basolateral amygdala (BLA) are crucial for acquisition,
consolidation, retrieval, and extinction of associative emotional memories. Synaptic plasticity …
consolidation, retrieval, and extinction of associative emotional memories. Synaptic plasticity …
Training spiking neural networks using lessons from deep learning
The brain is the perfect place to look for inspiration to develop more efficient neural
networks. The inner workings of our synapses and neurons provide a glimpse at what the …
networks. The inner workings of our synapses and neurons provide a glimpse at what the …
Artificial neural networks as models of neural information processing
M Van Gerven, S Bohte - Frontiers in computational neuroscience, 2017 - frontiersin.org
Conclusion Neural networks are experiencing a revival that not only transforms AI but also
provides new insights about neural computation in biological systems. The contributions in …
provides new insights about neural computation in biological systems. The contributions in …
Temporal backpropagation for spiking neural networks with one spike per neuron
SR Kheradpisheh, T Masquelier - International journal of neural …, 2020 - World Scientific
We propose a new supervised learning rule for multilayer spiking neural networks (SNNs)
that use a form of temporal coding known as rank-order-coding. With this coding scheme, all …
that use a form of temporal coding known as rank-order-coding. With this coding scheme, all …
[PDF][PDF] The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks
Neural activity is often described in terms of population-level factors extracted from the
responses of many neurons. Factors provide a lower-dimensional description with the aim of …
responses of many neurons. Factors provide a lower-dimensional description with the aim of …
Fault and error tolerance in neural networks: A review
C Torres-Huitzil, B Girau - IEEE Access, 2017 - ieeexplore.ieee.org
Beyond energy, the growing number of defects in physical substrates is becoming another
major constraint that affects the design of computing devices and systems. As the underlying …
major constraint that affects the design of computing devices and systems. As the underlying …
Mapping spiking neural networks to neuromorphic hardware
Neuromorphic hardware implements biological neurons and synapses to execute a spiking
neural network (SNN)-based machine learning. We present SpiNeMap, a design …
neural network (SNN)-based machine learning. We present SpiNeMap, a design …
Is coding a relevant metaphor for the brain?
R Brette - Behavioral and Brain Sciences, 2019 - cambridge.org
“Neural coding” is a popular metaphor in neuroscience, where objective properties of the
world are communicated to the brain in the form of spikes. Here I argue that this metaphor is …
world are communicated to the brain in the form of spikes. Here I argue that this metaphor is …
Meta-learning through hebbian plasticity in random networks
Lifelong learning and adaptability are two defining aspects of biological agents. Modern
reinforcement learning (RL) approaches have shown significant progress in solving complex …
reinforcement learning (RL) approaches have shown significant progress in solving complex …