The provided code models synaptic conductances in a neural network model, focusing on the dynamics of AMPA and NMDA receptor-mediated synaptic transmission. Here's the biological basis for key components of the code: ### AMPA and NMDA Receptors - **AMPA Receptors:** These are ionotropic receptors that mediate fast excitatory synaptic transmission in the central nervous system. When glutamate binds to AMPA receptors, they allow the flow of Na\(^+\) ions into the neuron, leading to a depolarization and possibly triggering an action potential if the depolarization reaches the threshold. - **NMDA Receptors:** These receptors also bind glutamate but have a more complex gating mechanism. NMDA receptors are typically voltage-dependent due to a Mg\(^2+\) block that is alleviated upon sufficient depolarization. Once activated, NMDA receptors allow the influx of Ca\(^{2+}\), Na\(^+\), and K\(^+\), with the calcium influx playing a crucial role in synaptic plasticity, including long-term potentiation (LTP). ### Synaptic Conductance and Plasticity - **Conductance Calculations:** The code updates synaptic conductances for AMPA (`g_AMPA`) and NMDA (`g_NMDA`) receptors. Synaptic conductance is a measure of how easily ions pass through the receptor channels when they are open, thereby influencing synaptic strength and neuronal excitability. - **Pre-synaptic Activity and Weighting:** The function considers spikes from presynaptic neurons to compute changes in synaptic conductance. `Pre_spikes_ext` captures this activity, which is then used to compute `prox_pre_weights` and `dist_pre_weights`, representing synaptic weights based on presynaptic neuron activity. - **Temporal Dynamics:** The decay terms for AMPA and NMDA conductances model the natural time courses of these receptor-mediated currents. The decay is exponential, connected to the receptor kinetics with constants `AMPA_t` and `NMDA_t` representing typical time constants for AMPA and NMDA receptor-mediated synaptic events. ### Biological Relevance - **Excitatory Postsynaptic Potential (EPSP):** The code models changes to EPSPs through AMPA and NMDA receptor activation, directly impacting neuronal depolarization and signaling. - **Synaptic Integration and Plasticity:** By modeling changes in synaptic weights and conductance (as represented through `tot_w` and `tot_w_nmda`), the code captures essential elements of synaptic plasticity, which are crucial for learning and memory processes in the brain. - **Calcium Dynamics:** The NMDA receptor-mediated calcium influx, implied in `tot_w_nmda`, is critical for triggering intracellular signaling pathways that result in synaptic modifications. In summary, the code simulates the interaction of AMPA and NMDA receptors in synaptic transmission, focusing on how input spikes and synaptic weights together influence conductance. This model could help explore dynamics underpinning synaptic integration and cellular mechanisms for learning and memory within neural networks.