Memory models

This section contains a list of implemented spike-based bio-inspired hippocampal memory models and its configuration files. Each memory model is defined by a class and, for each of them, the following will be briefly discussed:

• The theoretical model they come from (references to papers, if applicable).

• The input parameters to instantiate them and connect them with their input and output layers.

• The model variables that can be accessed externally once instantiated.

• The configuration file with the parameters that define the network and how to tune it.

Models:

• CA3 memory model with forgetting mechanism
  • Theoretical model
  • Class information
    • Memory
      • Memory.connect_in()
      • Memory.connect_out()
      • Memory.create_population()
      • Memory.create_synapses()
      • Memory.open_config_files()
      • Memory.read_json()
  • How to use the model
  • Custom config files
• Hippocampus memory model with forgetting mechanism
  • Theoretical model
  • Class information
    • Memory
      • Memory.create_population()
      • Memory.create_synapses()
      • Memory.open_config_files()
      • Memory.read_json()
  • How to use the model
  • Custom config files
• Hippocampus memory model with forgetting mechanism bioinspired DG and CA1 layers
  • Theoretical model
  • Class information
  • How to use the model
  • Custom config files
• CA3 content addressable memory model with forgetting mechanism
  • Theoretical model
  • Class information
    • Memory
      • Memory.connect_in()
      • Memory.connect_out()
      • Memory.create_population()
      • Memory.create_synapses()
      • Memory.open_config_files()
      • Memory.read_json()
  • How to use the model
  • Custom config files