The code provided is a computational model simulating synaptic connectivity between two neurons in a neural network, with a focus on synaptic plasticity mechanisms. Here's the biological basis of the model as inferred from the code: ### Neurons and Morphology - **Morphology**: The code uses morphological data from neuronal models, specifically from Acker et al., 2008, and potentially Branco et al., 2010, although the latter is commented out. Neuronal morphology data is crucial for accurately modeling the spatial properties and synaptic locations on neurons (i.e., proximal vs. distal compartments). - **Neuron Creation**: Two neurons are instantiated, each with mechanisms to regulate calcium dynamics (`makeNeuron_Ca`), which are vital for various cellular processes, including synaptic plasticity. ### Synaptic Connectivity - The neurons are connected via synapses, modeled as chemical synapses in the code. These connections are designed to simulate AMPA and NMDA receptor-mediated synaptic transmission. - **AMPA and NMDA Receptors**: Synaptic models incorporate AMPA and NMDA conductances (`ampa_cond` and `nmda_cond`), representing the ionotropic receptors crucial for fast excitatory synaptic transmission in the central nervous system. The NMDA receptor's voltage-dependent magnesium block is explicitly modeled, which is an essential feature of NMDA receptor activity, influencing calcium influx and synaptic plasticity. ### Synaptic Plasticity - The synapses between the two neurons exhibit plasticity, as suggested by the use of `eq_1_plastAMPA` and `eq_2_plastAMPA` equations, which manage the synaptic weight dynamics. This signifies a focus on synaptic strength modifications, a core component of learning and memory. - **Plasticity Rules**: Though the specific equations and rules are not detailed in the provided text, the script implies the use of Hebbian plasticity mechanisms, often involving long-term potentiation (LTP) and long-term depression (LTD). These processes depend on the temporal correlation between pre- and postsynaptic activity. ### Synaptic Arrangement - Synaptic locations are either proximal or distal, reflecting the reality of dendritic integration, where the location of synapses can significantly affect neuronal output. - Synapses can be clustered or distributed, mimicking the biological variability in synaptic organization, which influences the integration of synaptic inputs. ### Simulation Protocol - The neurons are stimulated sequentially, with neuron 1 always being active before neuron 2, a setup that is likely testing the effects of synaptic timing on plasticity. - The simulation includes repeated trials and incorporates buffer times, simulating real-life neural activity patterns that experience phases of activity and rest. ### Output and Monitoring - The simulation records synaptic weights during the experiments, providing insight into how synaptic strengths change in response to specific activity patterns. This data can be critical for understanding mechanisms underlying synaptic plasticity. Overall, the code models connectivity between neurons with a focus on the biological processes of synaptic transmission and plasticity, crucial for understanding neural circuit dynamics and learning. These simulations offer insights into how variations in synaptic architecture and activity affect neuronal function, paralleling investigations in synaptic plasticity and neurophysiology.