The provided code is a computational model of synaptic plasticity in a Layer 5b (L5b) pyramidal neuron, based on studies by Weber et al. (2016), Ebner et al. (2019), and Hay et al. (2011). The model focuses on simulating synaptic plasticity mechanisms, particularly long-term potentiation (LTP) and long-term depression (LTD), using the reconstructed biophysical properties of a L5b pyramidal cell.

Biological Basis:

1. Neuronal Model:

• L5b Pyramidal Neurons: The code uses the morphology of a Layer 5b pyramidal neuron. These neurons are located in the cerebral cortex and are crucial for integrating synaptic inputs and generating outputs to other brain areas, primarily due to their large dendritic trees and axonal projections.
• Morphology and Ion Channels: The biological cell model incorporates the morphology files representing the detailed structure of dendrites. Specific ion channels are considered, including Na+ (sodium) and Ca2+ (calcium) channels, which influence action potential generation and back-propagating action potentials, critical for synaptic plasticity.

2. Synaptic Plasticity:

• Synapses: The model places four synapses at different locations on the dendrites. Each synapse experiences modifications based on pre- and postsynaptic activities, vital for modeling Hebbian synaptic plasticity.
• Ionotropic Receptors: The parameters include AMPA and NMDA receptor contributions, which are common in excitatory synapses. AMPA receptors mediate fast synaptic transmission, while NMDA receptors are essential for Ca2+ influx, triggering intracellular signaling cascades necessary for synaptic plasticity.
• LTP and LTD Mechanisms: The synapses undergo changes in synaptic efficacy characterized as increases (LTP) or decreases (LTD) in synaptic strength. The model uses specific parameters to represent these processes:
  • LTD and LTP Amplitudes: Parameters like A_LTD_pre, A_LTP_pre, A_LTD_post, and A_LTP_post relate to the amplitudes of induced LTD and LTP based directly on pre- and postsynaptic activities.
  • Voltage Thresholds and Time Constants: Thresholds and time constants, e.g., theta_u_T, tau_N_alpha, modulate the conditions under which plasticity induction protocols apply, simulating the requirement of specific postsynaptic voltage conditions for LTP and LTD induction.

3. Ion Channel Modulation:

• Blocking Channels: The code provides a mechanism to block specific Na+ and Ca2+ channels in the apical dendrites (BLOCK variable), which allows the simulation of pharmacological experiments where blocking these channels helps understand their role in synaptic plasticity.

4. Synaptic Distance Influence:

• Location-specific Synaptic Behavior: Synapses are modeled at distinct positions along the dendrite (example_dists), recognizing that the efficacy and nature of synaptic input can vary profoundly with dendritic location. This pertains to how dendritic processing and attenuation affect plasticity outcomes.

The model uses computational experiments, integrating these biological principles to simulate how various factors like synaptic input timing, synaptic location, and ion channel activities influence synaptic strength outcomes, effectively modeling the dynamics of Hebbian plasticity in cortical neurons.