The provided code models the biological dynamics of synaptic transmission in a neuronal system. This is a critical aspect of neural communication, capturing how neurons interact through synapses, which are specialized junctions that facilitate the transfer of signals between neurons. ### Biological Basis of the Code 1. **Synaptic Types**: - The code distinguishes between two types of synapses: inhibitory (`"Inhib"`) and excitatory (`"Excit"`). - **Inhibitory Synapses** typically prevent or reduce the likelihood of the post-synaptic neuron firing an action potential. They often use neurotransmitters like GABA, which allow the influx of chloride ions (Cl-) or efflux of potassium ions (K+), hyperpolarizing the neuron. - **Excitatory Synapses**, on the other hand, increase the probability of the post-synaptic neuron firing. They typically use neurotransmitters like glutamate, which allow the influx of sodium ions (Na+), depolarizing the neuron. 2. **Reversal Potential (`m_E`)**: - Each synapse type is associated with a reversal potential (`m_E`). This is the membrane potential at which the net flow of specific ions through the synaptic channel is zero. For inhibitory synapses, this is often around -70 mV due to Cl- ions, while for excitatory synapses, it is generally nearer to 0 mV due to Na+ ions. 3. **Synaptic Decay (`m_tau_decay`)**: - Synaptic conductance or efficacy (`m_g`) decreases exponentially over time due to `m_tau_decay`, a time constant representing how quickly the synaptic effect dissipates after neurotransmitter release. - This decay models the biological process of synaptic current decline as neurotransmitters are cleared from the synaptic cleft or reuptaken. 4. **Conductance Increase (`m_gStep`)**: - When a spike is received (`m_SpikeReceived`), the synaptic conductance increases by `m_gStep`, mimicking the biological process of neurotransmitter release and receptor activation, leading to ion channel opening and increased conductance. 5. **Synaptic Current (`m_I(V)`)**: - This function computes the synaptic current as a function of membrane potential (`V`) and synaptic variables, following the formula `I = g(V - E)`, where `g` is the conductance and `E` is the reversal potential. This models the ionic current driven by the difference between `V` and `E`. ### Additional Notes Overall, the code models the essential dynamics of synaptic interactions, capturing how synapses can modulate neural activity through changes in conductance. These processes are fundamental to neural computation and communication within the brain, affecting various neural functions such as information processing, learning, and memory.