The code provided is a component of a computational neuroscience model aimed at understanding how neuronal firing patterns and synaptic states evolve with varying intervals between memory encoding. Here, the focus is on the temporal dynamics of maintaining and differentiating memory traces over periods ranging from 1 hour to 24 hours. The key biological processes and concepts represented in this code are outlined below:
Neuronal Firing Rates:
Active Neurons and Synaptic Activity:
CUTOFF
). This can be interpreted as studying neurons engaged in encoding or retrieval, a phenomenon supported by experimental evidence where only a subset of neurons are involved in the active processing of specific memories.Common Neurons Across Patterns:
Synaptic Branch Analysis:
getsynstate
, involves evaluating synaptic strengths on branches, essential for synaptic plasticity. This process helps to identify the role of synaptic clusters (potential hotspots of plasticity) in sustaining memory resilience over time.Memory Interval Impact:
diffs
) between memory encoding sessions (e.g., 1H, 2H, 5H, 24H) are central to the study, simulating real-life scenarios where memory encoding is spaced over time. This aspect aligns biologically with the study of spaced learning and its impact on long-term memory consolidation.Correlational Analysis of Firing Patterns:
Overall, the code simulates critical elements of biological memory processing, such as temporal dynamics, synaptic plasticity, and neural network behavior in response to different intervals between memory encoding. By analyzing firing rates, active neurons, synaptic states, and commonality among neurons, the model explores how distinct memory traces are formed, maintained, and potentially overlap in a neural context. This contributes to our understanding of the biological underpinnings of memory formation, encoding, and retrieval over time.