# Biological Basis of the Code The provided code is a simulation script utilizing the NEST simulator to model aspects of neural plasticity, particularly dopamine-dependent spike-timing dependent plasticity (STDP). This form of plasticity is a critical biological process underpinning learning and memory in the brain. Below, we highlight key biological concepts relevant to this model: ## Neuronal Dynamics The model uses `iaf_psc_alpha` neurons, which are leaky integrate-and-fire neurons with alpha-shaped postsynaptic currents. These neurons are a simplification of real neurons but capture essential dynamics such as membrane potential integration and spike generation. The specific parameters like `tau_syn_ex` and `tau_syn_in` represent synaptic time constants for excitatory and inhibitory inputs, respectively. These parameters relate to how quickly a postsynaptic neuron responds and returns to baseline after receiving a synaptic input. ## Spike-Timing Dependent Plasticity (STDP) STDP is a form of synaptic plasticity where the timing of neuronal spikes determines the strength of synaptic connections. In biological systems, if a presynaptic neuron fires shortly before a postsynaptic neuron, synaptic strengthening (LTP - Long-Term Potentiation) occurs. Conversely, if the presynaptic neuron fires after the postsynaptic one, synaptic weakening (LTD - Long-Term Depression) may occur. This code models STDP with the addition of dopamine modulation, which is reflective of more complex, biologically realistic scenarios where neuromodulators affect synaptic plasticity. ## Dopamine Modulation Dopamine is a neurotransmitter that plays a significant role in reward and motivation systems in the brain. In the code, a third neuron `dopa_neuron` introduces dopamine via a `stdp_dopamine_synapse`, influencing the synaptic plasticity between two other neurons. The presence of the `volume_transmitter` acts as a mechanism to modulate synaptic changes relative to the neuron's exposure to dopamine, mimicking how dopamine could broadly affect neural circuits in the biological brain. ## Poisson Neuron Firing The input generators, or `poisson_generators`, simulate neurons that fire stochastically like a Poisson process, which is a common assumption for background neural activity. In this model, both excitatory and inhibitory inputs are generated, affecting the activity of the modeled neurons to mimic a typically balanced synaptic input environment that neurons experience in a biological setting. The rate of firing and resultant activity is set to reflect specific biological conditions that are believed to be crucial for understanding how dopamine-dependent STDP operates within neural circuits. This balance is critical to ensure the model reflects stability observed in real neural systems, where excitatory and inhibitory forces are dynamically regulated. ## Synaptic Connection and Modulation The connections between neurons use the `dopa` (dopamine-modulated STDP) model, which represents the key biological focus of the simulation. It aims to explore how dopamine levels can alter synaptic weight changes, replicating biological processes underlying learning reinforcement. The script measures the synaptic weight evolution to identify how dopamine impacts synaptic strength over time, aiding in understanding the role of neuromodulators in plasticity and learning. In summary, this code tries to replicate the effects of dopamine-modulated STDP on synaptic weights between neurons, reflecting biologically relevant neural mechanisms crucial for learning and memory formation. It encompasses several fundamental neuronal properties and neurotransmitter dynamics observed in mammalian brains.