The provided code snippet models synaptic conductances in a neuronal network during a stimulus presentation. It calculates and visualizes the time-varying synaptic strengths for both excitatory and inhibitory inputs over a specific simulation length. Here's a breakdown of the biological basis for this computational model: ### Biological Context 1. **Synaptic Conductance (gsyn):** - In the context of neuronal modeling, synaptic conductance refers to the influence of ion flow through synaptic channels, typically mediated by neurotransmitters. This conductance affects the membrane potential of a neuron, impacting its excitability and likelihood to fire action potentials. - The code represents this through two vectors: `exc_gsyn_vec` for excitatory conductance and `inh_gsyn_vec` for inhibitory conductance. 2. **Excitatory vs. Inhibitory Synapses:** - **Excitatory Synapses**: Typically mediated by neurotransmitters like glutamate, which bind to receptors like AMPA and NMDA, leading to depolarization and increasing the likelihood of a neuron firing. - **Inhibitory Synapses**: Often involve neurotransmitters such as GABA, which bind to receptors like GABA_A and GABA_B, generally causing hyperpolarization, thus reducing the probability of neuronal firing. - The code separately accounts for excitatory and inhibitory synaptic activities, summing their respective conductances over defined time bins. 3. **Temporal Dynamics:** - Neurons receive synaptic inputs that vary over time, especially in response to stimuli. This temporal summation of synaptic conductances is critical for understanding neuronal behavior in response to environmental cues. - In the code, these dynamics are discretized into time bins (`edges`), with conductances being summed within these intervals to reflect temporal integration. 4. **Synaptic Integration and Balance:** - Synaptic integration is the process by which a neuron sums its excitatory and inhibitory inputs to reach a decision threshold for firing. - The modeling in the code captures this by accumulating synaptic weights (`exc_weighted` and `inh_weighted`) over time, which are then normalized for analysis (`plot( weight_t, exc_weighted ./ max(exc_weighted), weight_t, inh_weighted ./ max(inh_weighted))`). 5. **Functional Implications:** - The ratio between excitatory and inhibitory conductances (`exc_weighted./inh_weighted`) is significant because it reflects the neuronal input balance, which can influence network stability, oscillatory dynamics, and eventually cognitive functions such as learning and memory through synaptic plasticity. In summary, the code captures the essence of how neuronal inputs (excitatory and inhibitory) are modeled over time, emphasizing the integration and balance of synaptic currents. This reflects fundamental processes of neuronal computation necessary for brain function.