# Biological Basis of the Code The provided code models the dynamics of synaptic plasticity, particularly focusing on spike-timing-dependent plasticity (STDP) in excitatory-to-excitatory (exc-exc) synaptic connections within neural circuits. It is applied in the context of studying time-dependent homeostatic mechanisms influenced by Brain-Derived Neurotrophic Factor (BDNF) following neural circuit injury. ## Key Biological Concepts 1. **Spike-Timing-Dependent Plasticity (STDP):** STDP is a form of synaptic plasticity in which the timing of spikes between presynaptic and postsynaptic neurons determines the direction and magnitude of synaptic weight change. Typically, if a presynaptic spike precedes a postsynaptic action potential within a certain time window, long-term potentiation (LTP) occurs. Conversely, if the presynaptic spike follows the postsynaptic spike, long-term depression (LTD) is induced. This code distinguishes between `STDP_pre` and `STDP_post`, representing the mean synaptic weights before and after an experimental manipulation of interest. 2. **BDNF and Neural Plasticity:** BDNF is a neurotrophin that significantly influences neural plasticity, neurogenesis, and survival. It plays a pivotal role in neural circuitry remodeling, particularly during recovery from injury. The code examines conditions with and without BDNF (`BDNF1` and `BDNF2`), reflecting its modulation on synaptic strength and connectivity. 3. **Injury and Homeostatic Mechanisms:** Following neural injury, homeostatic plasticity mechanisms can alter synaptic strengths to stabilize neural activity. This code accounts for simulated injury conditions with varying degrees of effect (`InjuryFraction1` and `InjuryFraction2`) on synaptic connections. ## Model Specifics - **Simulated Neural Network:** The code processes peer connections (`con_ee`) among excitatory neurons while excluding those involving injured neurons (`exc_injuredNeurons1`), reflecting realistic network adaptation following damage. - **Statistical Analysis of Synaptic Strengths:** The code calculates the empirical cumulative distribution function (CDF) of synaptic weights post-intervention (`CDF_f` and `CDF_x`), allowing for the visualization and statistical comparison of synaptic changes across conditions. - **Temporal Segmentation:** The model distinguishes between pre- and post-injury synaptic activity by defining specific time intervals (`statemon_dt` and `STDP_startstopIndex`) for data extraction and analysis. - **Conditions and Combinations:** Variability in BDNF presence and injury leads to different experimental conditions, facilitating an understanding of their combined effects on synaptic plasticity and network homeostasis. Overall, this code represents a detailed attempt to computationally simulate and analyze the effects of BDNF and injury on synaptic plasticity mechanisms, providing insights into how neural circuits adapt and potentially recover from damage.