The provided code snippet is related to a computational model that simulates neuronal activity, particularly focusing on action potential generation and synaptic input. Here's a description of the biological basis of this model:

Biological Basis of the Model

Neuronal Action Potentials

The code appears to model the generation and propagation of action potentials (APs) in a neuron. Action potentials are the fundamental signals used by neurons to communicate with each other and occur when the neuron depolarizes due to synaptic or intrinsic activity.

• Parameters related to action potentials:
  • AP_time, AP_durtime: These parameters likely correspond to the timing and duration of action potentials. The action potential initiation (AP_time) follows the onset of an "upstate" (a depolarized state), indicating how neurons can transition from resting to active states.
  • inj: Represents a current injection (in picoamperes, pA), which is a common method to artificially induce action potentials in computational models.

Synaptic Activity

The model involves the simulation of synaptic inputs, which are crucial for neuronal communication and network activity.

• Types of synaptic activity:
  • makeALLpreGrad, makeALLpostGrad: These functions appear to model synaptic input from presynaptic (pre) and postsynaptic (post) neurons. There are conditions set for different "grad" levels (high, med, low), which may correspond to different strengths or frequencies of synaptic input.
  • stopGluGrad, stopGABAGrad: These commands suggest that the model incorporates both excitatory (glutamatergic) and inhibitory (GABAergic) synaptic inputs. Glutamate is the primary excitatory neurotransmitter, while GABA is the main inhibitory transmitter in the central nervous system.

Network Dynamics

• Upstate Dynamics:
  • The upstate_time might represent the duration of a sustained depolarized state seen in cortical neurons, known as the "upstate." This is a feature of cortical dynamics where neurons show periods of elevated activity, which is crucial for processes like signal integration and information processing.

Model Time Resolution

• Temporal Resolution:
  • The model uses discrete time steps (e.g., step 0.05 -time), which is typical for numerical simulations in computational neuroscience to solve differential equations that describe the biophysical states of the neurons over time.

Conclusion

Overall, this model simulates the integrative dynamics of neurons by capturing synaptic inputs, action potential generation, and various temporal aspects of neuronal activity, reflecting a fundamental aspect of brain function. It allows researchers to study how neurons process information through synaptic inputs and how action potentials are modulated by different synaptic states.