Recurrent spiking neural network to simulate the consolidation of memory representations based on synaptic tagging and capture

• simulation code and shell scripts added for
  • simulations with varied neuromodulation to regulate the synthesis of plasticity-related proteins
  • testing the model implementation with small example simulations
• Python functions added for
  • fitting temporal traces with linear readout from the network
  • performing principal component analysis (PCA) on the network dynamics
  • extracting pattern completion coefficient Q and mutual information MI from spike raster data
• interactive Jupyter notebook for reproducing the figures of paper 3 added
• larger parts of the code cleaned

Recurrent spiking neural network to simulate the consolidation of memory representations based on synaptic tagging and capture

• functionality and shell scripts have been added for
  • simulations with randomly initialized weights
  • investigations on the frequency dependence of LTP and LTD
  • attractor memory representations
• dependence of protein synthesis thresholds on neuromodulator amount has been added
• analysis scripts such as averageFileColumnsAdvanced.py have received upgrades
• weight units have been changed to mV
• several bugs have been fixed

Recurrent spiking neural network to simulate the consolidation of memory representations based on synaptic tagging and capture

Simulation and analysis of three memory representations have been added. We used this in a new preprint paper which investigates the impact of consolidation on spontaneous activation and priming of memory representations in different organizational paradigms.

v1.0.1

Simulation of memory consolidation in recurrent spiking neural networks based on synaptic tagging and capture

v1.0.0

Simulation of memory consolidation in recurrent spiking neural networks based on synaptic tagging and capture