# Biological Basis of the Provided Code The code snippet represents an implementation of a computational model of a neuronal network incorporating synaptic plasticity mechanisms. This model is inspired by cortical networks in the brain, specifically focusing on how functional specificity can emerge within these networks. Below are the key biological aspects captured in the code: ## Neuronal Composition and Structure - **Types of Neurons:** The model distinguishes between excitatory and inhibitory neurons, with 80% being excitatory (`f = 0.8`) and the remaining 20% inhibitory. This reflects the common excitatory-inhibitory ratio observed in cortical circuits. - **Connection Probabilities:** Different connection probabilities are defined for synapses between neuron types (`eps_ee`, `eps_ei`, `eps_ie`, `eps_ii`). For example, inhibitory-excitatory connections `eps_ie` are set to the maximum, modeling strong local inhibitory control observed in real neural circuits. ## Synaptic Interactions - **Synaptic Weights and Plasticity:** Synaptic plasticity is modeled via parameters that influence the strength and dynamics of synaptic weights (`w_min`, `w_max`). These weight changes are driven by the interplay between long-term potentiation (LTP) and long-term depression (LTD), key mechanisms underlying learning and memory in the brain. - **A_ltp (LTP Strength):** Represents the potentiation strength factor, encouraging synaptic strengthening. - **A_ltd (LTD Strength):** Represents the depression strength factor, promoting synaptic weakening. ## Single Neuron Dynamics - **Leaky Integrate-and-Fire Model:** Neurons are modeled using a simple leaky integrate-and-fire model, where `tm` is the membrane time constant, modeling the gradual decay of membrane potential due to leakage. The threshold voltage `vth` models the firing threshold of neurons. - **Inhibitory and Excitatory Dynamics:** The model captures the dominance of inhibitory over excitatory processes (`g = 8`), reflecting the balance required for stability in cortical networks. ## Orientation Tuning and Stimulus - **Stimulus Orientation:** Preferred orientations (`po_init`) and a specific stimulus orientation (`th = π/2`) are included, simulating features relevant to primary visual cortex neurons known for their orientation selectivity. - **Feedforward Input:** The baseline rate (`b_rate`) and modulation terms (`m_exc`, `m_inh`) represent the activity driven by external inputs, similar to sensory inputs that neurons receive. ## Plasticity Time Constants - **Plasticity Time Constants (`tm_plst`, `tp_plst`, `tx_plst`):** These represent timescales over which synaptic changes occur. The exact dynamics are modeled through exponential decay terms (`Bm_plst`, `Bp_plst`, `Bx_plst`), simulating the biological processes that underpin synaptic strength modulation. By capturing these key properties and parameters, the code aims to mimic certain functionalities and behaviors of biological neural networks, particularly focusing on how synaptic plasticity contributes to functional specificity in balanced cortical networks.