The provided code appears to model synaptic plasticity processes, specifically focusing on spike-timing dependent plasticity (STDP) in a neural network. Here’s a breakdown of relevant biological concepts being modeled:
SpikeMakerSTDP), which is a form of synaptic plasticity that depends on the relative timing of spikes from the presynaptic and postsynaptic neurons. If a presynaptic spike precedes a postsynaptic spike within a certain time window, synaptic strengthening (long-term potentiation, LTP) generally occurs. If the order is reversed, synaptic weakening (long-term depression, LTD) can result.Compartments: The model includes multiple compartments, indicated by tertdend1_1, tertdend2_1, etc., simulating dendritic sections of a neuron. These compartments mimic the spatial complexity of a real neuron, where synaptic inputs are integrated.
Compartmental Inputs/Connections: The addmsg commands imply connections between these compartments, simulating the flow of information (e.g., membrane potential, Vm).
VM (Membrane Potential): The model adjusts the membrane potential (Vm), which is critical for action potential initiation and signaling across the neuron.
Calcium Dynamics and Receptors: The parameters such as NMDACa and LCa likely correspond to calcium dynamics through NMDA receptors and possibly L-type calcium channels. Calcium influx through NMDA receptors plays a significant role in synaptic plasticity, triggering intracellular signaling cascades that contribute to modifications in synaptic strength.
Action Potential Dynamics: The AP_time, AP_durtime, and isi parameters define the timing characteristics of action potentials. The abrupt changes in Vm suggest modeling of action potential generation in response to synaptic events, essential for STDP.
Synaptic Inputs: The use of makeALLpre implies activation of presynaptic events to model neurotransmitter release. The distinction between high, medium, and low synaptic activity (makeALLpost "high/med/low") reflects varying levels of postsynaptic responsiveness, potentially simulating different neuronal states or responses to firing patterns.
step function calls indicate temporal updates in the simulation, capturing the continuous dynamics of neuronal activity and plasticity processes over time.Overall, the code is designed to simulate a neural network model incorporating detailed biophysical processes related to STDP. It captures the essence of neuronal communication and synaptic modification, reflecting critical processes in learning and memory within a computational framework. The integration of synaptic inputs and action potential dynamics effectively models the conditions necessary for STDP, emphasizing the biological underpinnings of synaptic change based on spike timing.