The provided code models synaptic transmission in neural networks, focusing on the dynamics of AMPA and NMDA receptor-mediated synapses. This model seeks to explore and simulate the biological phenomena of synaptic plasticity, particularly short-term plasticity (STP), which is vital for understanding how neurons communicate and adapt over time. Here are the key biological aspects represented in the code: ### Synaptic Transmission - **AMPA and NMDA Receptors**: These are two types of glutamate receptors located on the post-synaptic membrane of neurons. They play critical roles in excitatory synaptic transmission: - **AMPA receptors** mediate fast synaptic transmission. When glutamate binds to these receptors, they quickly open to allow Na+ ions to enter the cell, which can lead to depolarization and an eventual action potential if the stimulus is strong enough. - **NMDA receptors** contribute to synaptic plasticity and memory functions. They are unique in that they require both membrane depolarization and glutamate binding to open. When open, they allow Ca2+ (as well as Na+ and K+) to flow, which can activate intracellular signaling pathways related to synaptic strengthening. ### Short-term Plasticity (STP) - **Modeling STP**: The code simulates repetitive stimulation and how synaptic strength can vary over time due to STP. STP includes mechanisms like facilitation and depression, where successive synaptic transmissions can increase or decrease the effectiveness of synaptic response, respectively. STP occurs on a time scale of milliseconds to seconds and is thought to play a vital role in neural information processing and network dynamics. ### Network Connectivity - **Individual NetCons**: The use of separate NetCon objects for each stimulus per synapse allows for the generation of arbitrary input patterns. This is essential for capturing the complexity of synaptic interactions and potential plastic changes that occur with different stimulation frequencies and patterns. These configurations enable the study of how specific input patterns can affect synaptic dynamics in the context of STP. ### Summary In sum, this code models the biophysical properties of synaptic transmission involving AMPA and NMDA receptors under the influence of short-term synaptic plasticity. By incorporating individual connections per stimulus, it allows for the study of complex input patterns and their effects on synaptic dynamics, resembling aspects of learning and memory in neuronal circuits.