The code provided is a computational model focusing on the electrical activity of neurons, specifically involving key ion dynamics and neurotransmitter interactions within a neuronal membrane. This simulation is rooted in the field of computational neuroscience and aims to explore the behavior of neuronal excitability under varying conditions and stimuli intensities. Here are the core biological elements modeled: ### Ion Concentrations and Gradients - **Chloride (Cl) Ions:** Intracellular ([Cli]) and extracellular ([Clo]) chloride concentrations are crucial as they influence the reversal potential for GABAergic synapses. The KCC2 cotransporter's influence on chloride homeostasis is also modeled, with a focus on its half-activation potential (Vhalf) and its maximum current values set for normal and pathological states. - **Potassium (K) Ions:** Intracellular (Ki) and extracellular (Ko) potassium concentrations are central to the model, as the potassium gradient is vital for setting the resting membrane potential and for action potential repolarization. The delayed-rectifier potassium current and its influence on the membrane potential are modeled using the conductance (G_Kv) and other dynamics (Vbolz, d). - **Sodium (Na) Ions:** Sodium dynamics play a crucial role in action potential initiation. The model includes representations of intracellular (Nai) and extracellular (Nao) sodium concentrations, addressing their roles in the sodium-potassium pump and the fast sodium channel activation and inactivation dynamics. ### Membrane Dynamics and Conductances - **Membrane Capacitance (Cm):** This parameter is fundamental to understanding the temporal dynamics of voltage changes across the neuron's membrane. - **Channel Conductances:** The model simulates active and passive ion channel conductances, including sodium (G_Na), potassium (G_Kv), and leak channels (G_kl, G_Nal). These conductances determine ionic current flows that are critical for neuronal excitability and signal propagation. ### Synaptic Dynamics - **GABA and AMPA:** The model incorporates synaptic inputs through GABA_A (inhibitory) and AMPA (excitatory) receptors. The parameters for these synapses, such as `alpha1_GABA`, `alpha2_GABA`, `alpha1_AMPA`, and `alpha2_AMPA`, reflect the timescales of synaptic conductance changes during neuronal activity. ### Neuronal Compartments - **Soma and Dendrite:** The model distinguishes between different cellular compartments—soma and dendrites—each having specific areas, surface-to-volume ratio, and inter-compartmental conductance (kappa). ### Ion Pumps and Buffers - **Sodium-Potassium Pump:** This is a vital component in maintaining ionic gradients and membrane potential stability by actively transporting sodium out of and potassium into the cell (Imaxsoma, Imaxdend). - **Glial Buffering:** The buffering capacity of glial cells is included to maintain potassium homeostasis, which is vital for preventing excessive neuronal excitability. ### Seizure Modeling - **Seizure Dynamics:** The code attempts to simulate seizure-like afterdischarges influenced by varying external stimulation frequencies (HZ). It evaluates the duration of these afterdischarges and their relation to ion dynamics, synaptic input, and cellular excitability. ### Summary Overall, this computational model integrates elements of membrane biophysics, synaptic physiology, and neuronal excitability to explore how neurons respond to stimuli and how alterations in ionic concentrations can lead to abnormal electrical activity, such as seizures. The parameters and equations strive to replicate real biological processes and contribute to understanding the mechanisms underlying neuronal dynamics.