The provided code is a part of a computational model that simulates neuronal dynamics, particularly focusing on synaptic activity and action potential (spike) generation in neurons. The biological underpinnings of this code can be summarized as follows:
The code models the membrane potential dynamics of neurons using a form of the Izhikevich neuron model. The membrane potential v
is updated using a quadratic term .04*v(k)^2
and linear terms 5*v(k)
and constants like 140
, which mimic the behavior of the neuronal membrane potential as it integrates various inputs. The variables a
, b
, c
, and d
are intrinsic neuronal parameters that are set for each neuron to describe its spiking and bursting behaviors.
The model incorporates several types of excitatory and inhibitory synaptic inputs, as indicated by variables like EPSC
, IPSC_in
, and IPSC_ret
. These represent excitatory postsynaptic currents (EPSCs) and inhibitory postsynaptic currents (IPSCs), respectively. The code calculates the synaptic conductance changes due to these inputs using a synaptic model described by TMsynE_inst
, which likely include synaptic gating variables such as r
(recovery variable) and x
(available synaptic resources).
The code seems to implement synaptic depression and facilitation mechanisms distinctively, shown by function calls TMsynE_inst_F
and TMsynE_inst_D
. These refer to the dynamic changes in synaptic strength based on past activity, which are critical for understanding how synapses modulate their efficiency over time.
When the membrane potential v
reaches a threshold vp + zeta(k)
, the neuron is said to fire an action potential (spike). The membrane potential is then reset to c(k)
, and a recovery variable u
is updated (increased by d(k)
) to model the aftereffects of spiking, such as refractory periods and adaptation.
The model includes several parameters for constant inputs Idc
(direct current input) and time-varying inputs like I_psE
(postsynaptic excitation) and noise kisi(k)
. These represent external stimuli and background synaptic noise, respectively, which are crucial for simulating a realistic neuronal response. Additionally, the zeta(k)
variable may introduce variability in spike threshold, a common biological phenomenon seen due to intrinsic noise or modulation by neuromodulators.
The term n_hyp
relates to hypoxia conditions, where Idbs
appears to adjust synaptic or neuronal excitability based on the hypoxic state. This reflects biological scenarios where reduced oxygen levels affect neuronal behavior.
In summary, this model aims to capture the complex interplay of intrinsic neuronal dynamics and extrinsic synaptic inputs that lead to action potential generation and synaptic plasticity, which are fundamental processes in neural computation and information processing.