The provided code models synaptic transmission in a neural network, focusing on excitatory and inhibitory synaptic currents. It aims to simulate and analyze the dynamics of synaptic interactions and membrane potential changes in neurons, which are fundamental for understanding how neural circuits process information. ### Key Biological Concepts 1. **Synaptic Currents**: - The code calculates excitatory and inhibitory synaptic currents (`Isyn_E` and `I_syn_I`) which influence the neuron's membrane potential (`VV`). - **Excitatory Synapses (AMPA-type)**: Modeled with parameters like reversal potential (`VsynE = 0.0 mV`) and time constant (`tsynE = 2.0 ms`). These currents typically depolarize the neuron, increasing the likelihood of action potential firing. - **Inhibitory Synapses (GABA_A-type)**: Modeled with a different reversal potential (`VsynI = -85.0 mV`) and time constant (`tsynI = 3.0 ms`), which hyperpolarizes the neuron, making it less likely to fire. 2. **Synaptic Strength and Connectivity**: - Parameters such as `gET`, `gEE`, and `gEI` represent synaptic conductances for different types of synaptic connections (e.g., thalamic input to excitatory neurons, excitatory to excitatory neurons, and excitatory to inhibitory neurons). - These values are adjusted by the connectivity (`KET`, `KEE`, `KEI`) and incorporated into calculations for synaptic efficacy (`gsynET`, `gsynEE`, `gsynEI`). 3. **Neuronal Membrane Potential**: - The membrane potential (`VV`) is a key variable that represents the difference in electric potential across the neuronal membrane. - It is dynamically influenced by the sum of synaptic currents as calculated by equations representing the integration of set (`set`), excitatory (`see`), and inhibitory (`sei`) synaptic inputs. 4. **Temporal Dynamics and Integration**: - The code incorporates time (`time`) to assess the temporal evolution of synaptic interactions and their effects on the neuron's membrane potential. - Averaging over time (`ntstat`) provides a measure of steady-state synaptic inputs and currents during the simulated period. 5. **Neural Firing and Synchronization**: - Spike timing (from `tc.ras.` file) contributes to understanding how synchronized inputs influence synaptic currents and, subsequently, neuronal firing dynamics. - The raster plot data is used to track action potentials, exploring potential synchronization phenomena within the network. ### General Biological Interpretation This code embodies a simplified model of cortical processing, focusing on the balance and interaction between excitatory and inhibitory synapses. Such a model helps illustrate principles like excitation-inhibition balance, synaptic integration, and their roles in shaping network activity and output. The parameters and equations are rooted in the biophysical properties of neuronal synapses, providing insights into how neurons process and integrate various inputs into coherent patterns of neural activity.