# Biological Basis of the Code The provided code is part of a computational neuroscience model focusing on synaptic dynamics, specifically for modeling inhibitory synapses in neural networks. Below is a biological interpretation of the components modeled in the code: ## Synaptic Model - **Inhibitory Synapses**: The model extends the `Exp2Syn` class to incorporate properties of inhibitory synapses. These synapses decrease the likelihood of the postsynaptic neuron firing an action potential and are crucial for maintaining the balance of excitation and inhibition in neural circuits. - **Two-State Kinetics**: The synapse is modeled using a two-state kinetic scheme involving rise (`tau1`) and decay (`tau2`) time constants. This, biologically, models the binding and unbinding of neurotransmitters (such as GABA) to receptors, leading to a change in conductance and thus influencing the postsynaptic potential. - **Exponential Decay**: The model considers the temporal dynamics of synaptic conductance changes using exponential functions, aligning with how real synapses exhibit changes in conductance in response to neurotransmitter release. ## Specific Synapse Features - **Marker Proteins**: The code tracks expression of specific inhibitory synapse markers—Vgat, Sst, and Npy—which indicate the type of inhibitory interneurons involved: - **Vgat+**: Vesicular GABA transporter; crucial for GABAergic neurotransmission. - **Sst+**: Somatostatin-positive interneurons, known for their role in modulating neuronal excitability and synchrony. - **Npy+**: Neuropeptide Y-positive interneurons, often influencing anxiety and stress responses. - **PV (Parvalbumin) Interneurons**: Though not fully implemented, `pv` likely refers to parvalbumin-expressing interneurons, which are critical for high-frequency firing and synchronization. ## Synapse Activation - **Switch Mechanism**: The `isOn` attribute acts as a switch to toggle the synapse's influence. If `isOn` is `0`, the synapse is inactive regardless of presynaptic activity, simulating conditions where certain synapse populations may be inhibited due to genetic or environmental factors. ## Conductance and Current - **Conductance (`g`)**: The conductance calculated in the model represents how likely ions are to flow through the synaptic cleft, a proxy for synaptic strength. Real inhibitory synapses decrease conductance as they decrease membrane potential and excitability. - **Postsynaptic Current (`i`)**: Derived from conductance and the difference between the postsynaptic membrane potential (`v`) and reversal potential (`e`), the inhibitory current affects whether a neuron will fire an action potential. In reality, this current often hyperpolarizes the postsynaptic neuron, making it less likely to fire. ## Overall Biological Context The model seeks to simulate the behavior of synapses involved in inhibitory signals within a neural network, accounting for neurotransmitter dynamics and neuron-specific markers. Understanding these attributes helps elucidate mechanisms of neural computations involving inhibition, such as controlling neuronal oscillations, shaping neural code, and contributing to plasticity and learning.