Neural Network Dynamics: Unraveling Spiking and Synaptic Plasticity in Learning and Memory

Neuroscience continually strives to unravel the intricate relationship between neural network morphology, spiking dynamics, and their resulting functional outcomes in the service of learning and memory. Central to this understanding is the concept of synaptic plasticity, the fundamental mechanism by which neural circuits adapt and store information. While activity dependent mechanisms like Spike Timing Dependent Plasticity (STDP) are crucial for establishing new connections and forming memory traces, as demonstrated by models like Izhikevich's Pavlovian conditioning in addressing the "distal reward problem" by dopamine modulated network transformations, they can also introduce instabilities.

This Research Topic posits that to maintain functional stability and adaptability, these Hebbian like plasticity mechanisms operate in combination with homeostatic plasticity. Such homeostatic processes serve to counterbalance potential runaway excitation or depression, ensuring that networks remain within a functional range despite the inherent volatility of activity dependent changes. Exploring this critical interaction between plasticity types is key to understanding how networks achieve stable memory formation while remaining flexible. Furthermore, learning and memory are not solely products of individual synaptic changes but emerge from the collective behavior of neural ensembles. This Topic therefore broadens its scope to include emergent circuit level dynamics such as neural network synchrony, signal propagation, and resonance phenomena. These collective dynamics profoundly influence how information is encoded, transmitted, and retrieved, playing a pivotal role in memory consolidation, stability, and the broader understanding of neural computation. For instance, rhythmic synchronization might facilitate signal communication across brain regions, while resonance could highlight preferred processing frequencies vital for specific cognitive functions.

The aim of this Research Topic is to foster a deeper understanding of how the multifaceted components of synaptic plasticity (both activity dependent and homeostatic) and emergent circuit level dynamics interact to sculpt and maintain learning and memory. It seeks contributions that bridge theoretical models with experimental evidence, investigating how networks transform to form stable memories, how they cope with spontaneous activity, how neuromodulatory influences (e.g., dopamine) can lead to vulnerability or enhancement, and ultimately, how functional stability is achieved amidst the ceaseless adaptation of the brain. Sub themes and questions of interest can include, but are not limited to:

Plasticity Interaction and Stability: Elucidating the interplay between activity dependent and homeostatic plasticity for memory adaptability and long term stability.

Emergent Dynamics and Memory Function: Investigating how circuit level dynamics (synchrony, signal propagation, resonance) contribute to memory encoding, consolidation, and retrieval, and their relationship with synaptic plasticity.

Neuromodulation and Circuit Resilience: Examining how neuromodulators influence plasticity balance and emergent dynamics, impacting memory vulnerability, enhancement, and system resilience.

Bridging Scales: Models to Experiments: Developing theoretical frameworks integrating multi scale plasticity and emergent dynamics, informed by experimental findings in learning and memory.