Theory and Models of Synaptic Plasticity

Synaptic plasticity (aka Neural Learning) 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 understanding how neural circuits in the brain form and function, as well as 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, such as implementing learning rules in neuromorphic hardware (including FPGAs), autonomous vehicles, AI-based applications for medical imaging, 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 cognitive function, and the impact on neural circuits leading to the manifestation of brain disorders, such as depression. 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 underlying biochemical processes manifests into learning and memory, along with their involvement in the emergence of brain or cognitive disorders. It is also important to observe how such learning rules support the development of AI-based applications and their application to studying brain function in normal and pathological conditions.

To gather further insights into the boundaries of synaptic plasticity research, we welcome articles addressing, but not limited to, the following themes:
- Novel experimental approaches for induction and expression of LTP/LTD
- Experiments and models that elucidate the molecular underpinning of synaptic plasticity
- The role of synaptic plasticity in the development and emergence of neurological disorders
- Spike timing-dependent plasticity and (Markovian and non-Markovian) reinforcement learning in single and networks of neurons
- Impact of learning rules in the single neuron, neuronal dendrites, and neural networks
- Synaptic plasticity’s role in the development and impact on cognitive function
- Calcium-based learning
- Molecular models of learning
- Experiments and models of synaptic tagging
- Life-long learning
- Learning in neuromorphic platforms including robotics
- Theory of neural learning
- AI-based applications to brain function and medical imaging

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.