Synaptic plasticity rules used in current computational models of learning are generally insensitive to physiological factors such as spine voltage, animal age, extracellular fluid composition, and body temperature, limiting their predictive power. Here, we built a biophysically detailed synapse model inclusive of electrical dynamics, calcium-dependent signaling via CaMKII and Calcineurin (CaN) activities. The model combined multi-timescale variables, milliseconds to minutes, and intrinsic noise from stochastic ion channel gating. Analysis of the trajectories of joint CaMKII and CaN activities yielded an interpretable geometrical readout that fitted the synaptic plasticity outcomes of nine published ex vivo experiments covering various spike-timing and frequency-dependent plasticity induction protocols, animal ages, and experimental conditions. Using this new approach, we then generated maps predicting plasticity outcomes across the space of these stimulation conditions. Finally, we tested the model's robustness to in vivo-like spike time irregularity, showing that it significantly alters plasticity outcomes.
The human brain's interregional communication is vital for its proper functioning. A promising direction for investigating how these regions communicate relies on the assumption that the brain is a complex network. In this context, images derived from positron emission tomography (PET) have been proposed as a potential source for understanding brain networks. However, such networks are often assembled via direct computation of inter-subject correlations, neglecting variabilities between subjects and jeopardizing the construction of group representative networks. Here, we used [18F]FDG-PET data from 1027 individuals at different syndromal stages (352 CU, 621 MCI and 234 AD) to develop a novel method for constructing stable (i.e. resilient to spurious data points) metabolic brain networks. Our multiple sampling (MS) scheme generates brain networks with higher stability when compared to the conventional approach. In addition, the proposed method is robust to imbalanced datasets and requires 50% fewer subjects to achieve stability than the conventional approach. Our method has the potential to considerably boost PET data reutilization and advance our understating of human brain network patterns in health and disease.
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