The code provided is a computational model of synaptic plasticity, specifically an implementation of Spike Timing Dependent Plasticity (STDP) based on the triplet model by Pfister and Gerstner (2006). This model is built to simulate the plastic changes in synaptic weights based on the temporal correlation of pre- and postsynaptic spikes in neurons. Here's an overview of the biological basis underlying the code: ## Biological Concepts ### Synaptic Plasticity - **Synaptic plasticity** is the process by which synapses (the connection points between neurons) strengthen or weaken over time, in response to activity levels. It is fundamental to learning and memory in the brain. - The specific type of plasticity modeled here is **Spike Timing Dependent Plasticity (STDP)**, which depends on the precise timing of spikes fired by the presynaptic and postsynaptic neurons. ### STDP Mechanism - **STDP** captures the idea that the relative timing of neuronal firing can strengthen or weaken synapses: - If a presynaptic neuron fires just before a postsynaptic neuron (positive timing), the synapse is typically strengthened (Long-Term Potentiation, LTP). - If the presynaptic spike follows the postsynaptic spike (negative timing), the synapse typically weakens (Long-Term Depression, LTD). ### Triplet STDP Model - The code implements modifications to the classical STDP model by incorporating a triplet-based framework from Pfister and Gerstner (2006), which extends standard pair-based STDP. - **Triplet Model Features**: - It considers both spike pairs and triplet interactions (i.e., sequences of three spikes) to better capture experimental data and the non-linearities in synaptic changes. - Parameters such as `A2mais`, `A2menos`, `A3mais`, and `A3menos` correspond to multiplicative factors that modulate weight change, aligning with biological learning rules. - Time constants `Taumais`, `Taumenos`, `Taux`, and `Tauy` represent decay of trace variables (`R` and `O`), which model the synaptic eligibility traces important for updating synaptic weights. ### Neuronal Dynamics - The synaptic conductance `g` and current `i` in the model are adjusted based on pre- and postsynaptic spike correlation, reflecting changes in synaptic efficacy. - `g` (conductance variable) and its decay characterized by `tau` represent the synaptic response dynamics, akin to a simple model of neurotransmitter release and receptor binding. ## Key Code Aspects Linked to Biology - The `NET_RECEIVE` block handles synaptic events, where logic for pre- and postsynaptic spike detection is implemented. - The `BREAKPOINT` and `DERIVATIVE` blocks update synaptic conductance and eligibility traces (`R` and `O`) as per their respective decay rates and neuronal activity. - The concept of "soft bounds" in weight updates (`peso`) provides a way to prevent excessive synaptic potentiation or depression, reflecting biological limits on synaptic strength. This code, hence, is a computational representation of how synaptic strengths could be dynamically modified in real neural systems based on neuronal firing patterns, contributing to learning and memory processes.