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:
f = 0.8) and the remaining 20% inhibitory. This reflects the common excitatory-inhibitory ratio observed in cortical circuits.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.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.
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.g = 8), reflecting the balance required for stability in cortical networks.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.b_rate) and modulation terms (m_exc, m_inh) represent the activity driven by external inputs, similar to sensory inputs that neurons receive.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.