Biological Basis of the Computational Model

The provided code is a portion of a computational neuroscience model focused on understanding the electrical behaviors in neurons' dendrites and their impact on neuronal output. This model specifically simulates the conditions under which distal sites on active, uniform dendrites become influential, particularly in the context of distributed tonic inputs. Below are detailed connections of the code to biological concepts:

Neuronal Morphology

• Dendrites and Axons: The code refers to different structural components of neurons, specifically the dendrites and axon. Dendrites are critical for receiving and integrating synaptic inputs, and the axon is responsible for transmitting action potentials away from the neuron cell body.

Membrane Potentials and Ions

• Membrane Potential (v): The code incorporates the calculation of membrane potentials (v), which represent the voltage across the neuronal membrane. This is crucial for understanding how signals propagate through the neuron.

• Equilibrium Potentials and Passive Conductance: Functions eq_nmda and erev_PasSA calculate equilibrium potentials for different sections of the neuron, reflecting the membrane potential where there is no net ion flow across the membrane for specific ion channels or passive properties.

Synaptic Dynamics

• NMDA Receptor Dynamics:
  • NMDA receptors are ionotropic glutamate receptors that allow Ca²⁺ ions to enter the cell, playing important roles in synaptic plasticity and transmission.
  • Conductance Calculations: Functions like CalcGnmda, CalcGpd, and CalcGm calculate the conductance of NMDA receptors (and possibly other channels), translating into how ions are moving across the dendritic membrane as part of synaptic activity.
  • Gating Dynamics: Variables like gnmda_nmda suggest the model includes parameters to simulate the opening/closing of NMDA receptor channels based on various stimuli.

Current Density and Synaptic Currents

• Current Density (Jm): The function CalcJm computes the current density (in µA/cm²) over different sections, varying by the existence of synaptic activity influenced by NMDA receptor dynamics. This reflects how much ionic current per unit area is flowing, a critical factor in determining neuronal excitability.

• Synaptic Currents (Im): The model also calculates the synaptic currents across the neuronal membrane as shown in the CalcI function. It is expressed in picoamperes per micron (pA/µm), further revealing how synaptic inputs translate into measurable electrical signals within neuronal dendrites.

Graphical Outputs

• Graph Generation: Various procedures (e.g., MakeVRGraph, MakeCRGraph, etc.) generate graphs visualizing key electrical properties like membrane potential, conductance, current density, and current along dendrites and axon. These visual representations elucidate complex neuronal interactions influenced by distributed tonic inputs.

Conclusion

Overall, this model is aimed at elucidating how distal dendritic inputs affect neuronal activation and synaptic efficacy, with particular emphasis on NMDA receptor activity. This is critical for understanding the integration of synaptic inputs and the consequent neuronal responses — processes that are fundamental to higher cognitive functions such as learning and memory.