The given code snippet is part of a computational neuroscience model that simulates and optimizes synaptic dynamics to investigate the cooperativity of synaptic inputs on dendritic branches, particularly focusing on NMDA receptor-dependent events.

Biological Basis

1. NMDA Receptors and Synaptic Plasticity:

   • The model focuses on NMDA receptor (NMDAR)-mediated synaptic transmission, as indicated by parameters like gmax_NMDA_KIN and unitary_nmda_contribution. NMDARs are glutamate receptors that are crucial for synaptic plasticity and memory formation. They allow calcium and sodium ions to enter the cell, triggering intracellular processes necessary for synaptic plasticity when activated.

2. Branch Cooperativity:

   • The goal of the simulation is to replicate data from Harnett et al., 2012, which examines how multiple synaptic inputs on a dendritic branch can interact in a cooperative manner to influence the overall excitatory postsynaptic potential (EPSP). This cooperativity is crucial for understanding how neurons integrate signals spatially and temporally.

3. Dendritic Integration:

   • The model investigates dendritic integration by simulating synaptic inputs across spines on a dendritic branch (150 µm from the soma) to study non-linearities like supralinearity, which refers to the combined effect of multiple inputs being greater than the sum of individual inputs.

4. Biophysical Parameters:

   • Parameters such as gamma, Kd, and kin_scale are tuned to fit the experimental data. These could relate to the kinetics or binding properties of the NMDARs, and how they scale with signal integration.

5. Experimental Context:

   • Simulations are performed in conditions simulating experiments done under sodium channel blocker tetrodotoxin (TTX) to isolate synaptic inputs' effects without action potentials, focusing directly on local dendritic integration mechanisms.

6. Optimization and Model Fitting:

   • The code utilizes optimization algorithms to adjust the model parameters so that the simulated data aligns with empirical observations. The model uses target values like a peak nonlinearity of 44%, consistent with physiological findings of synaptic integration.

Key Aspects

• Parallelization: The code runs in parallel, using multiple cores to simulate large datasets efficiently. This approach allows the exploration of various parameter combinations in a reasonable time.

• Outputs and Metrics: The code calculates metrics such as peak supralinearity and NMDA contribution to understand how modifications in parameters influence the biological phenomena of interest.

This model aims to provide insights into how dendrites integrate synaptic inputs, a fundamental question in neuroscience, reflecting the complex interplay of biophysical properties, synaptic arrangements, and receptor dynamics in shaping neuronal output.