The provided code is a computational model designed to simulate neural activity, specifically focusing on the dynamics of NMDA (N-Methyl-D-Aspartate) spikes within a neuron. Here are the key biological aspects that the code aims to model: ### Biological Basis #### Neuronal Morphology and Compartments 1. **Morphology**: The code uses data from morphologically detailed models of neurons (e.g., `Branco2010_Morpho.swc`), which describe the physical structure of neurons, including dendritic trees. Such details are vital for simulating how electrical signals propagate within neurons. 2. **Distal Compartments**: The code identifies certain dendritic compartments as "distal," which are far from the soma (cell body) and are crucial for studying localized synaptic inputs. #### Synaptic Transmission and NMDA Receptors 1. **NMDA Receptors**: These are a type of glutamate receptor that play a critical role in synaptic plasticity, which is fundamental for learning and memory. NMDA receptors are known for their voltage-dependent activity, which requires both ligand binding (glutamate) and postsynaptic depolarization to remove a magnesium block. 2. **Mg Block Dynamics**: The code models the magnesium block in NMDA receptors (`Mgblock = 1./(1.+ exp(-0.062*vu2)/3.57)`), which is characteristic of NMDA activity, requiring depolarization to relieve the block and allow Ca²⁺ and Na⁺ to enter the neuron. #### Electrical Properties of Neurons 1. **Resting Membrane Potential**: The resting potential (`V_rest = -70.*mV`) reflects the baseline electrical state of the neuron when not transmitting signals. 2. **Membrane Time Constant**: This (`tau_in = 8.*ms`) represents how quickly the membrane potential can respond to synaptic inputs. 3. **Spiking Threshold**: The threshold (`V_thresh = -45.*mV`) indicates the membrane potential needed to trigger an action potential (or neuronal "spike"). 4. **Capacitance**: Membrane capacitance (`C = 200.*pF`) determines the neuron's ability to store and release charge. #### Neuronal Dynamics 1. **Integrate-and-Fire Model**: The input neuron is simulated using this simplified model of neuronal activity, which integrates incoming signals until a threshold is reached, triggering a spiking event. 2. **Neuron Group and Synapses**: The `NeuronGroup` and `Synapses` constructs in the code simulate networks of neurons and their connections, focusing on the interaction between input neurons and the postsynaptic target cell. #### Synaptic Plasticity 1. **LTP Threshold**: The code references a threshold for long-term potentiation (LTP), which is a sustained increase in synaptic strength following high-frequency stimulation. This is relevant for learning-related synaptic changes. 2. **Spike Timing**: The code introduces a controlled spike timing (`deltat = 0.1` ms), crucial for studying how temporal patterns of neural activity can influence synaptic strength. #### Monitoring and Visualization 1. **Voltage Monitoring**: The simulation tracks both somatic and dendritic voltages over time, facilitating the analysis of local versus global neuronal responses to inputs. 2. **NMDA Spike Detection**: The simulation aims to capture the distinctive voltage dynamics typical of NMDA receptor activation, visible as prolonged depolarizations or "spikes" in the dendritic compartments. Overall, the simulation encapsulates the complex interactions between dendritic architecture, synaptic inputs, and NMDA receptor dynamics, providing insights into how neurons process information and adapt through plasticity mechanisms.