The provided code is a computational model of a synapse implementing spike-timing-dependent plasticity (STDP), a biological process that adjusts the strength of connections between neurons in the brain. This process is fundamental to synaptic learning and memory formation. ### Biological Basis #### Synapse Model - **Exponential Synapse**: The model represents a biological synapse using a simple exponential synapse model. This involves the dynamics of synaptic conductance, represented by `g`, which decays exponentially over time with a time constant `tau`. This decay simulates the transient change in synaptic conductance following the release of neurotransmitter in real synapses. - **Reversal Potential**: The parameter `e` denotes the reversal potential, representing the equilibrium potential across the synapse. In the context of the model, it is related to the type of synaptic neurotransmitter receptor, which in biological terms would influence whether the synapse is excitatory or inhibitory. #### Spike-Timing-Dependent Plasticity (STDP) - **Presynaptic and Postsynaptic Spikes**: The code models both presynaptic (`tpre`) and postsynaptic (`tpost`) spike timings, key components of STDP. The `NET_RECEIVE` block handles incoming spikes, adjusting synaptic strengths based on their timing relationships. - **Timing-Dependent Synaptic Changes**: - **Depression**: The parameter `d` indicates a depression factor, showing how synaptic strength is reduced when a presynaptic spike occurs after a postsynaptic spike. This is consistent with the Hebbian rule, emphasizing the weakening of synapses (LTD: Long-Term Depression) when presynaptic activity consistently follows postsynaptic firing. - **Potentiation**: The parameter `p` stands for the potentiation factor, representing the increase in synaptic strength when a presynaptic spike precedes a postsynaptic spike. This reflects Long-Term Potentiation (LTP), where synapses strengthen based on the timing of spikes. - **Time Constants (`dtau`, `ptau`)**: These parameters specify the decay constants for depression and potentiation effects, respectively. They control the temporal window over which STDP influences are effective, aligning with empirical findings such as those from Bi & Poo (1998, 2001). #### Plasticity and Learning STDP describes a form of synaptic plasticity where the timing of spikes between neurons influences synaptic strength. When a presynaptic neuron's spike precedes a postsynaptic spike, potentiation typically occurs, strengthening the synapse. Conversely, if the presynaptic spike follows the postsynaptic activity, synaptic depression often results. These phenomena reflect the cellular basis of learning and memory storage mechanisms in the brain, emphasizing the role of timing in synaptic modifications conducive to Hebbian learning principles. ### Conclusion The model captures essential components of neuronal communication and learning, illustrating how synaptic strengths are dynamically regulated based on the precise timing of neural activity, a foundational concept in understanding neural circuits and their adaptation through experiences. This implementation provides insight into how timing-dependent plasticity can drive long-term changes in neural connectivity.