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Models, Neurological

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Predictive learning by a burst-dependent learning rule.

Neurobiology of learning and memory
Humans and other animals are able to quickly generalize latent dynamics of spatiotemporal sequences, often from a minimal number of previous experiences. Additionally, internal representations of external stimuli must remain stable, even in the prese...

Synchronization in STDP-driven memristive neural networks with time-varying topology.

Journal of biological physics
Synchronization is a widespread phenomenon in the brain. Despite numerous studies, the specific parameter configurations of the synaptic network structure and learning rules needed to achieve robust and enduring synchronization in neurons driven by s...

Spiking neural P systems with lateral inhibition.

Neural networks : the official journal of the International Neural Network Society
As a member of the third generation of artificial neural network models, spiking neural P systems (SN P systems) have gained a hot research spot in recent years. This work introduces the phenomenon of lateral inhibition in biological nervous systems ...

Long- and short-term history effects in a spiking network model of statistical learning.

Scientific reports
The statistical structure of the environment is often important when making decisions. There are multiple theories of how the brain represents statistical structure. One such theory states that neural activity spontaneously samples from probability d...

Experimental validation of the free-energy principle with in vitro neural networks.

Nature communications
Empirical applications of the free-energy principle are not straightforward because they entail a commitment to a particular process theory, especially at the cellular and synaptic levels. Using a recently established reverse engineering technique, w...

Intrinsic neural diversity quenches the dynamic volatility of neural networks.

Proceedings of the National Academy of Sciences of the United States of America
Heterogeneity is the norm in biology. The brain is no different: Neuronal cell types are myriad, reflected through their cellular morphology, type, excitability, connectivity motifs, and ion channel distributions. While this biophysical diversity enr...

Highly Bionic Neurotransmitter-Communicated Neurons Following Integrate-and-Fire Dynamics.

Nano letters
In biological neural networks, chemical communication follows the reversible integrate-and-fire (I&F) dynamics model, enabling efficient, anti-interference signal transport. However, existing artificial neurons fail to follow the I&F model in chemica...

Targeting operational regimes of interest in recurrent neural networks.

PLoS computational biology
Neural computations emerge from local recurrent neural circuits or computational units such as cortical columns that comprise hundreds to a few thousand neurons. Continuous progress in connectomics, electrophysiology, and calcium imaging require trac...

Evolution-communication spiking neural P systems with energy request rules.

Neural networks : the official journal of the International Neural Network Society
Evolution-communication spiking neural P systems with energy request rules (ECSNP-ER systems) are proposed and developed as a new variant of evolution-communication spiking neural P systems. In ECSNP-ER systems, in addition to spike-evolution rules a...