heterogeneous-synaptic-dynamics

Model heterogeneous synaptic dynamics by applying the Tsodyks-Markram STP/LTP framework with parameter variability across synapses. It calculates conductance using a double-exponential model to ensure realistic synaptic behavior during large-scale brain network simulations.

The Tsodyks-Markram STP framework models short-term and long-term plasticity with parameter heterogeneity across synapses. It provides a systematic method to introduce realistic synaptic variability into computational neuroscience research and brain circuit simulations.

Yes, you can use this approach for neuromorphic hardware-inspired research and teaching. It provides reusable components for studying brain-scale network simulations and synaptic plasticity, making it suitable for both educational and hardware-oriented computational neuroscience applications.

Run a heterogeneous synapse network simulation by calling the example_simulation() function. This initializes and executes a small-scale network, demonstrating the four-dimension modeling of connectivity, transmission, plasticity, and heterogeneity.

Yes, this method supports scalable network generation for complex neural circuits. It systematically models four dimensions—connectivity, transmission, plasticity, and heterogeneity—to handle variability across synapses in large-scale brain networks.

Use a double-exponential model for synaptic conductance calculation to accurately capture the temporal dynamics of synaptic transmission. It complements the Tsodyks-Markram plasticity framework by providing realistic conductance behavior in large-scale network simulations.