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 …
Contributions by metaplasticity to solving the catastrophic forgetting problem
Catastrophic forgetting (CF) refers to the sudden and severe loss of prior information in
learning systems when acquiring new information. CF has been an Achilles heel of standard …
learning systems when acquiring new information. CF has been an Achilles heel of standard …
Adaptive control of synaptic plasticity integrates micro-and macroscopic network function
Synaptic plasticity configures interactions between neurons and is therefore likely to be a
primary driver of behavioral learning and development. How this microscopic-macroscopic …
primary driver of behavioral learning and development. How this microscopic-macroscopic …
Formation and computational implications of assemblies in neural circuits
In the brain, patterns of neural activity represent sensory information and store it in non‐
random synaptic connectivity. A prominent theoretical hypothesis states that assemblies …
random synaptic connectivity. A prominent theoretical hypothesis states that assemblies …
Neuromodulator-dependent synaptic tagging and capture retroactively controls neural coding in spiking neural networks
AB Lehr, J Luboeinski, C Tetzlaff - Scientific Reports, 2022 - nature.com
Events that are important to an individual's life trigger neuromodulator release in brain areas
responsible for cognitive and behavioral function. While it is well known that the presence of …
responsible for cognitive and behavioral function. While it is well known that the presence of …
Age-related attenuation of cortical synaptic tagging in the ACC is rescued by BDNF or a TrkB receptor agonist in both sex of mice
SB Zhou, M Xue, W Liu, YX Chen, QY Chen, JS Lu… - Molecular Brain, 2023 - Springer
Long-term potentiation (LTP) is a key cellular mechanism for learning and memory, and
recent studies in the hippocampus found that LTP was impaired in aged animals. Previous …
recent studies in the hippocampus found that LTP was impaired in aged animals. Previous …
Weight versus node perturbation learning in temporally extended tasks: Weight perturbation often performs similarly or better
Biological constraints often impose restrictions on plasticity rules such as locality and reward-
based rather than supervised learning. Two learning rules that comply with these restrictions …
based rather than supervised learning. Two learning rules that comply with these restrictions …
Li Promoting Long Afterglow Organic Light‐Emitting Transistor for Memory Optocoupler Module
The artificial brain is conceived as advanced intelligence technology, capable to emulate in‐
memory processes occurring in the human brain by integrating synaptic devices. Within this …
memory processes occurring in the human brain by integrating synaptic devices. Within this …
Predicting Forex Currency Fluctuations Using a Novel Bio-Inspired Modular Neural Network
In the realm of foreign exchange (Forex) market predictions, Convolutional Neural Networks
(CNNs) and Recurrent Neural Networks (RNNs) have been commonly employed. However …
(CNNs) and Recurrent Neural Networks (RNNs) have been commonly employed. However …
Organization and priming of long-term memory representations with two-phase plasticity
J Luboeinski, C Tetzlaff - Cognitive Computation, 2023 - Springer
Abstract Background/Introduction In recurrent neural networks in the brain, memories are
represented by so-called Hebbian cell assemblies. Such assemblies are groups of neurons …
represented by so-called Hebbian cell assemblies. Such assemblies are groups of neurons …