About this Research Topic
Synaptic plasticity has been an intensely studied topic in both experimental and computational neuroscience as it strives to uncover the brain’s underlying processes and corresponding “rule(s)” of how information is learned and stored. This area of research has been historically instrumental in, not only understanding how neural circuits in the brain form and function, but also in the development of learning rules used in various areas, including Artificial Intelligence (AI), machine learning and neural networks (including deep learning). Previous studies have focused on various aspects of synaptic plasticity and how these translate into learning rules that can be implemented in simulations of neural networks to observe how information is potentially learned and stored within a simulated neural network. Over the last decade, there have been important contributions towards the understanding of learning in the brain, including the translation and application of these rules to various areas of engineering; especially to autonomous vehicles, image recognition and robotics. In parallel to such advances, there is increasing interest in the interaction between synaptic plasticity and the development of brain disorders. Many years have passed since the last research topic on models of synaptic plasticity first appeared in Frontiers in Computational Neuroscience and given the numerous advances it is beneficial to present the next research topic in this area.
This research topic aims to assemble the current status for both the theory of learning and computational models of synaptic plasticity in neural circuits. We plan to bring together papers focusing on how learning in neural systems shapes single neuron and network activity, as well as topological characteristics of neuronal networks and the role of synaptic plasticity in the development of brain disorders, such as depression, and the impact on neural circuits leading to the manifestation of brain disorders. It is important to develop a deeper understanding between the type of plasticity rule and the impact on neural and/or network activity, including the onset of neurological disorders resulting from plastic change . Notably, one needs a better understanding of how the underlying biochemistry manifests into learning and memory, along with the emergence of brain or cognitive disorders . Put simply, the molecular basis of plastic change in the brain, including STDP, (non-Markovian) reinforcement learning, and the role of synaptic plasticity in the development and emergence of neurological disease requires further elucidation.
The scope of this research topic invites both experimental and theoretical research in this area that includes novel experimental approaches for induction and expression of LTP/LTD, experiments and models that aid to elucidate the molecular underpinning of synaptic plasticity, their role in the development and emergence of brain disorders and impact on cognitive function, spike timing-dependent plasticity and (Markovian and non-Markovian) reinforcement learning in single and networks of neurons, including plasticity in neuronal dendrites. The sub-topics include but are not limited to role of synaptic plasticity in the emergence of neurological disorders, calcium-based learning, molecular models of learning, LTP/LTD, spike timing-based and reinforcement learning, experiments and models of synaptic tagging, life-long learning, theory of neural learning, impact of learning rules in the single neuron, neuronal dendrites and neural networks.
Topic Editor Yuankun Xue is employed by LinkedIn, US. All other Topic Editors declare no competing interests with regards to the Research Topic subject
This research topic aims to assemble the current status for both the theory of learning and computational models of synaptic plasticity in neural circuits. We plan to bring together papers focusing on how learning in neural systems shapes single neuron and network activity, as well as topological characteristics of neuronal networks and the role of synaptic plasticity in the development of brain disorders, such as depression, and the impact on neural circuits leading to the manifestation of brain disorders. It is important to develop a deeper understanding between the type of plasticity rule and the impact on neural and/or network activity, including the onset of neurological disorders resulting from plastic change . Notably, one needs a better understanding of how the underlying biochemistry manifests into learning and memory, along with the emergence of brain or cognitive disorders . Put simply, the molecular basis of plastic change in the brain, including STDP, (non-Markovian) reinforcement learning, and the role of synaptic plasticity in the development and emergence of neurological disease requires further elucidation.
The scope of this research topic invites both experimental and theoretical research in this area that includes novel experimental approaches for induction and expression of LTP/LTD, experiments and models that aid to elucidate the molecular underpinning of synaptic plasticity, their role in the development and emergence of brain disorders and impact on cognitive function, spike timing-dependent plasticity and (Markovian and non-Markovian) reinforcement learning in single and networks of neurons, including plasticity in neuronal dendrites. The sub-topics include but are not limited to role of synaptic plasticity in the emergence of neurological disorders, calcium-based learning, molecular models of learning, LTP/LTD, spike timing-based and reinforcement learning, experiments and models of synaptic tagging, life-long learning, theory of neural learning, impact of learning rules in the single neuron, neuronal dendrites and neural networks.
Topic Editor Yuankun Xue is employed by LinkedIn, US. All other Topic Editors declare no competing interests with regards to the Research Topic subject
Keywords: Spike timing-dependent plasticity (STDP), Calcium-based plasticity (CBP), Learning in neural systems, Reinforcement learning, Synaptic tagging, AI, Machine learning, Deep learning, Autonomous systems, Network Physiology
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
