Resolving the prefrontal mechanisms of adaptive cognitive behaviors: A cross-species perspective
The prefrontal cortex (PFC) enables a staggering variety of complex behaviors, such as
planning actions, solving problems, and adapting to new situations according to external …
planning actions, solving problems, and adapting to new situations according to external …
[HTML][HTML] Learning with three factors: modulating Hebbian plasticity with errors
Highlights•The three-factor framework describes various learning rules in a unified
way.•Third factors can encode reward, attention, summary statistics, or supervised …
way.•Third factors can encode reward, attention, summary statistics, or supervised …
The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks
Brains process information in spiking neural networks. Their intricate connections shape the
diverse functions these networks perform. Yet how network connectivity relates to function is …
diverse functions these networks perform. Yet how network connectivity relates to function is …
Learning rules in spiking neural networks: A survey
Spiking neural networks (SNNs) are a promising energy-efficient alternative to artificial
neural networks (ANNs) due to their rich dynamics, capability to process spatiotemporal …
neural networks (ANNs) due to their rich dynamics, capability to process spatiotemporal …
Superspike: Supervised learning in multilayer spiking neural networks
A vast majority of computation in the brain is performed by spiking neural networks. Despite
the ubiquity of such spiking, we currently lack an understanding of how biological spiking …
the ubiquity of such spiking, we currently lack an understanding of how biological spiking …
Rethinking the performance comparison between SNNS and ANNS
Artificial neural networks (ANNs), a popular path towards artificial intelligence, have
experienced remarkable success via mature models, various benchmarks, open-source …
experienced remarkable success via mature models, various benchmarks, open-source …
[HTML][HTML] Event-based backpropagation can compute exact gradients for spiking neural networks
Spiking neural networks combine analog computation with event-based communication
using discrete spikes. While the impressive advances of deep learning are enabled by …
using discrete spikes. While the impressive advances of deep learning are enabled by …
Gradient descent for spiking neural networks
Most large-scale network models use neurons with static nonlinearities that produce analog
output, despite the fact that information processing in the brain is predominantly carried out …
output, despite the fact that information processing in the brain is predominantly carried out …
Adaptive smoothing gradient learning for spiking neural networks
Spiking neural networks (SNNs) with biologically inspired spatio-temporal dynamics
demonstrate superior energy efficiency on neuromorphic architectures. Error …
demonstrate superior energy efficiency on neuromorphic architectures. Error …
Brain-inspired learning on neuromorphic substrates
Neuromorphic hardware strives to emulate brain-like neural networks and thus holds the
promise for scalable, low-power information processing on temporal data streams. Yet, to …
promise for scalable, low-power information processing on temporal data streams. Yet, to …