The provided code is a segment of a computational model designed to simulate the interactions between pyramidal neurons in the brain through synaptic connections. It models chemical synapses between pyramidal cells, focusing on the dynamics of AMPA and NMDA receptors, as well as the influence of local calcium (Ca²⁺) pools on synaptic plasticity.
Pyramidal neurons are a critical component of the mammalian brain, particularly prevalent in regions like the cerebral cortex and hippocampus. They are excitatory neurons known both for their distinctive, pyramid-shaped soma and their part in various forms of synaptic plasticity.
The code models synaptic transmission via two principal types of glutamate receptors on the postsynaptic neuron: AMPA and NMDA receptors.
AMPA Receptors (g_ampa): These mediate fast excitatory postsynaptic potentials (EPSPs). They are key for rapid synaptic transmission and are modeled to activate quickly with a short duration (Cdur_ampa).
NMDA Receptors (g_nmda): These receptors are slower-acting and critically involved in synaptic plasticity mechanisms, like long-term potentiation (LTP). The code models NMDA receptor dynamics, highlighting their voltage dependence due to magnesium block and calcium permeability, influencing intracellular calcium concentration.
Calcium ions (Ca²⁺) play a vital role in cellular signaling, including synaptic plasticity. The code captures calcium dynamics through parameters like capoolcon, representing local calcium concentration, and functions like eta and omega, which dictate calcium-dependent synaptic plasticity modifications.
capoolcon variable represents intracellular calcium concentration dynamics, which are crucial for triggering signaling cascades leading to synaptic strength changes.The model incorporates mechanisms for synaptic plasticity, reflecting both the potentiation and depression of synaptic strengths. Parameters such as lambda1, lambda2, threshold1, threshold2, Wmax, and Wmin guide this modeling.
Hebbian Plasticity: The code's use of calcium levels to adjust synaptic weights embodies Hebbian learning rules (“cells that fire together, wire together”), where synaptic strengths are adjusted based on activity-level changes.
Short-term Plasticity: Variables such as F, D1, D2, and their associated mechanisms model short-term synaptic facilitation and depression, representing adaptive, transient changes in synaptic strength.
Randomness (unirand): Introduces variability reflecting stochastic aspects of synaptic transmission and neuronal firing.
Time-dependent Plasticity Factors: Functions like GAP1 model time-dependent changes in synaptic plasticity, assessing scenarios like synaptic weight stabilization and normalization over time.
Overall, this code simulates complex biological dynamics at the synaptic level, emphasizing the interactions of AMPA and NMDA receptors, the role of calcium in synaptic plasticity, and the ability of synapses to exhibit both short and long-lasting changes in strength. This mirrors the nuanced, multiscale processes underpinning learning and memory in the brain's neural networks.