Biological Basis of the Code Model

The code provided is part of a computational model designed to simulate synaptic inputs on the dendrites of neurons. This model specifically focuses on excitatory synapses mediated by AMPA and NMDA receptors, which play crucial roles in synaptic transmission and plasticity in the mammalian brain. Below, we explore the biological foundation of this model:

Dendritic Architecture and Synapses

Dendritic Spines and Shafts: The model incorporates two types of synaptic inputs based on their location:

• Spine (Head) Synapses: These are excitatory inputs on the dendritic spines. Spines are small membranous protrusions from a neuron's dendrite that typically receive synaptic input.
• Shaft Synapses: These occur directly on the dendritic shaft rather than the spine.

The code distinguishes between these based on the shaftInput variable and determines synaptic placement accordingly.

AMPA and NMDA Receptors

AMPA Receptors (AMPARs):

• The model uses Exp2Syn for simulating AMPAR-mediated synapses.
• AMPARs mediate fast excitatory synaptic transmission, and their activation leads to rapid current influx primarily via Na+ ions.

NMDA Receptors (NMDARs):

• The model uses Exp2SynNMDA for simulating NMDAR-mediated synapses.
• NMDARs are ionotropic glutamate receptors known for their voltage-dependent Mg2+ block and slower kinetics.
• They are permeable to Ca2+ ions, crucial for synaptic plasticity mechanisms like long-term potentiation (LTP).

These receptors' time constants (tau1 and tau2) are set based on empirical research (e.g., Harnett et al., 2012).

Synaptic Plasticity

The dynamic alteration of synaptic weights (weightAmpa and weightNmda) and the ability to modify synaptic start times (nsAmpa and nsNmda) mirror the plastic nature of synapses, highlighting synaptic strength changes and timing crucial for learning processes like Hebbian plasticity.

Synaptic Clusters and Distributed Inputs

The section involving synapse placement and movement reflects the biological concept of synaptic clustering versus distributed inputs:

• Clustered Inputs: Coordination of input timing and location can lead to more robust postsynaptic responses.
• Distributed Inputs: Scattering synapses across dendritic branches may engage different computational paradigms within the neuron.

Parent Connections

The function getParentX() to access parent compartment values emphasizes the model's consideration of dendritic morphology, impacting how inputs integrate across the dendritic tree.

In summary, the code models critical aspects of synaptic transmission and plasticity, reflecting biological processes in neuronal dendrites. This encompasses the interaction between AMPA and NMDA receptor-mediated inputs, their contribution to synaptic plasticity, and the spatial organization of synapses on dendrites, vital for information processing and cognitive functions in the brain.