Exploring the role of excitatory-inhibitory dynamics in synaptic plasticity, memory stability, and neural coding
Date Issued
2024-05-27
Author(s)
Supervisors
Referees
Abstract
This thesis provides an exploration of the brain's neural dynamics, shedding light on the mechanisms underlying learning, memory retention, neural synchronization, and sensory processing. Through a comprehensive series of studies, we explore how neurons adjust their connections in response to specific patterns, a fundamental aspect of memory and learning. This synaptic plasticity demonstrates a tight balance between different neural adaptations, allowing the brain to retain old memories while continuously forming new ones. The Investigation proceeds with an investigation of collective neural behavior, in particular the transitions between different firing states within networks of neurons. Our results characterize the critical points that govern these transitions, contributing significantly to the understanding of large-scale neural coordination. This insight highlights the importance of maintaining a balance between excitatory and inhibitory neurons, which directly impacts the brain's ability to process information and maintain functional states. Delving deeper into the mechanisms of sensory processing, our study examines the brain's response to visual stimuli, specifically under circumstances that generate gamma oscillations in the visual cortex. The interaction between external stimuli and brain wave activity shows layer-specific effects, providing a detailed insight into the neural basis of perception and cognition. In conclusion, this thesis presents a focused view of neural function, from synaptic alterations to network dynamics and sensory processing. It highlights the brain's remarkable capacity for stability amidst constant change, a foundational aspect underpinning the continuity of memory and the adaptability of learning. The collective findings of study offer profound implications for both neuroscience and the development of advanced artificial neural systems, emphasizing the necessity of intricate balancing in both natural and artificial learning environments.
Subjects
Stability Plasticity Dilemma
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Incremental Learning
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Self-Supervised Learning
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Noise and memory robustness
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Hetero-synaptic plasticity
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Balance of excitation and inhibition
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Neuronal avalanche
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Self organized criticality
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Order parameter
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Kuramoto model
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Scale-free dynamics
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Leaky integrate and fire model
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Phase transition
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Optogenetic stimulation
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Gamma power
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Visual stimulus
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Visual cortex
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Wilson-cowan model
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Excitatory population
Institution
Type
Dissertation
Language
English
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Final_Dissertation_Mohammad_Dehghani_Habibabadi.pdf
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