The provided code snippet models synaptic plasticity through a four-pathway phenomenological rule based on the work of Ebner et al. (2019). This model is designed to capture the complex interactions governing changes in synaptic weights, indicative of learning and memory processes in the brain.
The model aims to represent several key biological mechanisms underlying synaptic plasticity, particularly long-term potentiation (LTP) and long-term depression (LTD), which are critical for memory formation and learning:
Synaptic Transmission:
g_ampa
and g_nmda
represent conductances attributable to AMPAR and NMDAR, respectively. NMDARs are crucial for synaptic plasticity as they allow Ca²⁺ influx when depolarized and are not blocked by Mg²⁺ (modeled by mgblock
), a voltage-dependent phenomenon.Plasticity Pathways:
w_pre
, G
, and Z
to simulate pre-synaptic mechanisms. This includes processes that are dependent on presynaptic spikes and neurotransmitter release.w_post
, C
, P
, K_alpha
, and Rho
, reflecting the cellular responses to neurotransmitters and intracellular signaling cascades following receptor activation.Calcium Dynamics:
theta_u_T
, theta_u_N
, and theta_u_C
are used to calculate post-synaptic responses based on voltage thresholds, which might be tied to calcium signaling since calcium influx is critical in activating downstream pathways for synaptic plasticity.Activity-Dependent Modulation:
sigmoid_sat
are employed to simulate the non-linear and saturating response of intracellular activities like second messenger cascades and synaptic modulation based on the frequency and pattern of synaptic activity.Glutamatergic Signaling:
s_ampa
, s_nmda
).By simulating biophysical dynamics around these aspects, the code provides a computational framework to understand how synaptic strengths change in response to neuronal activity patterns, providing insights into adaptive neural processes similar to those observed in biological systems.