## Python and NEURON codes for the NMDA-based synaptic plasticity model This model analyses altered hippocampal synaptic plasticity and its rescue under the Alzheimer's disease (AD) conditions, when the concentrations of AD-related peptides, such as the amyloid precursor protein intracellular domain (AICD) and amyloid beta (Aβ), are increased. The phenomenological NMDA receptor-based voltage-dependent model is used to model synaptic modifications at the CA3-CA1 synapses onto the multicompartmental CA1 pyramidal neuron. The modeling results show that partial blockade of Glu2NB-NMDAR-gated channel restores intrinsic excitability of a CA1 pyramidal neuron and rescues long-term potentiation in AICD and Aβ conditions. The model is implemented in Python and NEURON. ### Reference Justinas J Dainauskas, Paola Vitale, Sebastien Moreno, Helene Marie, Michele Migliore and Ausra Saudargiene. Altered synaptic plasticity at hippocampal CA1–CA3 synapses in Alzheimer's disease: integration of amyloid precursor protein intracellular domain and amyloid beta effects into computational models. Frontiers in Computational Neuroscience 2023 doi.org/10.3389/fncom.2023.1305169 The code reproduces Fig2-Fig9. ## Run the code ```bash python -m "src.manual_run" [options] ``` ``` usage: manual_run.py [-h] [-p {ltp,ltd}] [-aC AICDCHANNELS] [-aN AICDNR2B] [-n2b NR2BMULT] [-b BETA] [-bM BETA_MIDPOINT] [-s SESSION] Build and run neuron simulation options: -h, --help show this help message and exit -p {ltp,ltd}, --protocol {ltp,ltd} Provide protocol name. ltp - 1 burst 100Hz 1s ltd - 1Hz 500s -aC AICDCHANNELS, --aicdchannels AICDCHANNELS Fraction of AICD in channels [0-1] -aN AICDNR2B, --aicdnr2b AICDNR2B Fraction of AICD for GluNR2B-NMDAR [0-4] -n2b NR2BMULT, --nr2bmult NR2BMULT Fraction of GluNR2B-NMDAR -b BETA, --beta BETA Fraction of Beta Amyloids [0-1] -bM BETA_MIDPOINT, --beta_midpoint BETA_MIDPOINT Coefficient for midpoint of LTP function to model beta amyloid effect -s SESSION, --session SESSION Session file ``` For automatic calculations set parameters in "configs" directory yaml file and run with ```bash python -m "src.auto_run" [options] ``` ``` usage: auto_run.py [-h] [-p {all}] [-c CONFIG] Build and run neuron simulation options: -h, --help show this help message and exit -p {all}, --protocol {all} Protocol to run -c CONFIG, --config CONFIG Yaml config file name ``` To plot the results calculated by auto_run calculations run: ```bash python -m "src.plot" [options] ``` ``` usage: plot.py [-h] [-p {all}] [-i ID] Plot runs options: -h, --help show this help message and exit -p {all}, --protocol {all} What to plot -i ID, --id ID Which experiment to run from latest ``` ## Reproduce figures ```bash python -m src.auto_run -p all -c main && python -m src.plot -p all -i 0 ```