One of the most striking properties of biological systems is their ability to learn and adapt to ever changing environmental conditions, tasks and stimuli. It emerges from a number of different forms of plasticity, that change the properties of the computing substrate, mainly acting on the modification of the strength of synaptic connections that gate the flow of information across neurons.
Plasticity is an essential ingredient for building artificial autonomous cognitive agents that can learn to reliably and meaningfully interact with the real world. For this reason, the neuromorphic community at large has put substantial effort in the design of different forms of plasticity and in putting them to practical use. These plasticity forms comprise, among others, Short Term Depression and Facilitation, Homeostasis, Spike Frequency Adaptation and diverse forms of Hebbian learning (e.g. Spike Timing Dependent Plasticity).
This special research topic aims at collecting the most advanced developments in the design of the diverse forms of plasticity, as well as their exploitation in the implementation of cognitive systems. Further, it aims at summarizing and reviewing the main contributions to the design of neuromorphic learning and adaptive systems, from the single circuit to the system level.
Relevant topics include (but are not limited to):
- Theoretical advances in plasticity research that aim at hardware implementations
- Plasticity in analog, digital or mixed signal hardware implementations of spiking neural networks
- Plasticity implemented with new devices and computing structures (memristors, phase change materials, nanodevices, etc.)
- Application of the learning and adaptation rules to specific problems (e.g. spatio-temporal pattern recognition, reinforcement learning, etc.)
The resulting collection of original research articles, reviews and commentaries will be the reference for plasticity in neuromorphic systems, fostering the research progress through discussions and new collaborations among the different players in our community.
One of the most striking properties of biological systems is their ability to learn and adapt to ever changing environmental conditions, tasks and stimuli. It emerges from a number of different forms of plasticity, that change the properties of the computing substrate, mainly acting on the modification of the strength of synaptic connections that gate the flow of information across neurons.
Plasticity is an essential ingredient for building artificial autonomous cognitive agents that can learn to reliably and meaningfully interact with the real world. For this reason, the neuromorphic community at large has put substantial effort in the design of different forms of plasticity and in putting them to practical use. These plasticity forms comprise, among others, Short Term Depression and Facilitation, Homeostasis, Spike Frequency Adaptation and diverse forms of Hebbian learning (e.g. Spike Timing Dependent Plasticity).
This special research topic aims at collecting the most advanced developments in the design of the diverse forms of plasticity, as well as their exploitation in the implementation of cognitive systems. Further, it aims at summarizing and reviewing the main contributions to the design of neuromorphic learning and adaptive systems, from the single circuit to the system level.
Relevant topics include (but are not limited to):
- Theoretical advances in plasticity research that aim at hardware implementations
- Plasticity in analog, digital or mixed signal hardware implementations of spiking neural networks
- Plasticity implemented with new devices and computing structures (memristors, phase change materials, nanodevices, etc.)
- Application of the learning and adaptation rules to specific problems (e.g. spatio-temporal pattern recognition, reinforcement learning, etc.)
The resulting collection of original research articles, reviews and commentaries will be the reference for plasticity in neuromorphic systems, fostering the research progress through discussions and new collaborations among the different players in our community.
