Supervised learning in multilayer spiking neural networks with spike temporal error backpropagation
The brain-inspired spiking neural networks (SNNs) hold the advantages of lower power
consumption and powerful computing capability. However, the lack of effective learning …
consumption and powerful computing capability. However, the lack of effective learning …
A unified dynamic model for learning, replay, and sharp-wave/ripples
Hippocampal activity is fundamental for episodic memory formation and consolidation.
During phases of rest and sleep, it exhibits sharp-wave/ripple (SPW/R) complexes, which …
During phases of rest and sleep, it exhibits sharp-wave/ripple (SPW/R) complexes, which …
Hysteresis, neural avalanches, and critical behavior near a first-order transition of a spiking neural network
Many experimental results, both in vivo and in vitro, support the idea that the brain cortex
operates near a critical point and at the same time works as a reservoir of precise …
operates near a critical point and at the same time works as a reservoir of precise …
SpikeTemp: An enhanced rank-order-based learning approach for spiking neural networks with adaptive structure
This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp,
for spiking neural networks (SNNs) with a dynamically adaptive structure. The trained feed …
for spiking neural networks (SNNs) with a dynamically adaptive structure. The trained feed …
Spike-based Bayesian-Hebbian learning of temporal sequences
Many cognitive and motor functions are enabled by the temporal representation and
processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably …
processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably …
Neural avalanches at the critical point between replay and non-replay of spatiotemporal patterns
We model spontaneous cortical activity with a network of coupled spiking units, in which
multiple spatio-temporal patterns are stored as dynamical attractors. We introduce an order …
multiple spatio-temporal patterns are stored as dynamical attractors. We introduce an order …
Alternation of up and down states at a dynamical phase-transition of a neural network with spatiotemporal attractors
Complex collective activity emerges spontaneously in cortical circuits in vivo and in vitro,
such as alternation of up and down states, precise spatiotemporal patterns replay, and …
such as alternation of up and down states, precise spatiotemporal patterns replay, and …
Oscillatory neurocomputing with ring attractors: a network architecture for map** locations in space onto patterns of neural synchrony
Theories of neural coding seek to explain how states of the world are mapped onto states of
the brain. Here, we compare how an animal's location in space can be encoded by two …
the brain. Here, we compare how an animal's location in space can be encoded by two …
Effects of Poisson noise in a IF model with STDP and spontaneous replay of periodic spatiotemporal patterns, in absence of cue stimulation
We consider a network of leaky integrate and fire neurons, whose learning mechanism is
based on the Spike-Timing-Dependent Plasticity. The spontaneous temporal dynamic of the …
based on the Spike-Timing-Dependent Plasticity. The spontaneous temporal dynamic of the …
Optimizing information processing in neuronal networks beyond critical states
Critical dynamics have been postulated as an ideal regime for neuronal networks in the
brain, considering optimal dynamic range and information processing. Herein, we focused …
brain, considering optimal dynamic range and information processing. Herein, we focused …