Hippocampus memory model with forgetting mechanism

This sections shows a hippocampus memory model with forgetting mechanism (Memory class in hippocampus_with_forgetting).

Theoretical model

This memory model comes from the paper entitled: “A bio-inspired implementation of a sparse-learning spike-based hippocampus memory model”, which can be found here.

To refer to this particular model:

APA: Casanueva-Morato, D., Ayuso-Martinez, A., Dominguez-Morales, J. P., Jimenez-Fernandez, A., & Jimenez-Moreno, G. (2024). A bio-inspired implementation of a sparse-learning spike-based hippocampus memory model. IEEE Transactions on Emerging Topics in Computing.

ISO 690: CASANUEVA-MORATO, Daniel, et al. A bio-inspired implementation of a sparse-learning spike-based hippocampus memory model. IEEE Transactions on Emerging Topics in Computing, 2024.

MLA: Casanueva-Morato, Daniel, et al. “A bio-inspired implementation of a sparse-learning spike-based hippocampus memory model.” IEEE Transactions on Emerging Topics in Computing (2024).

BIBTEX: @article{casanueva2024bio, title={A bio-inspired implementation of a sparse-learning spike-based hippocampus memory model}, author={Casanueva-Morato, Daniel and Ayuso-Martinez, Alvaro and Dominguez-Morales, Juan P and Jimenez-Fernandez, Angel and Jimenez-Moreno, Gabriel}, journal={IEEE Transactions on Emerging Topics in Computing}, year={2024}, publisher={IEEE}}

Class information

class sPyMem.hippocampus_with_forgetting.hippocampus_with_forgetting.Memory(cueSize, contSize, sim, ILayer, OLayer, initCA3W=None, configFilePath=None)

Spike-based bio-inspired hippocampal memory model with forgetting

Parameters:

  • cueSize (int) – number of cues of the memory

  • contSize (int) – size of the content of the memory in bits/neuron

  • sim (simulation object (spynnaker8 for spynnaker)) – object in charge of handling the simulation

  • ILayer (population) – input population to the memory model

  • OLayer (population) – output population of the memory model

  • configFilePath (int, optional) – path + filename to the config file of internal model parameters

  • initCA3W (list, optional) – list of initial weight to use in CA3 synapse (initial memory content); format of each element of the list: (source_neuron_id, destination_neuron_id, initial_weight, delay)

Variables:

  • cueSize (int) – number of cues of the memory, initial value: cueSize

  • contSize (int) – size of the content of the memory in bits/neuron, initial value: contSize

  • sim (simulation object (spynnaker8 for spynnaker)) – object in charge of handling the simulation, initial value: sim

  • ILayer (population) – input population to the memory model, initial value: ILayer

  • CA3cueLayer (population) – CA3cue population

  • CA3contLayer (population) – CA3cont population

  • DGLayer (population) – DG population

  • CA1Layer (population) – CA1 population

  • OLayer (population) – output population of the memory model, initial value: OLayer

  • configFilePath (str) – path + filename to the config file of internal model parameters, initial value: configFilePath or internal path to default config file

  • initCA3W (list) – list of initial weight to use in CA3 synapse (initial memory content); format of each element of the list: (source_neuron_id, destination_neuron_id, initial_weight, delay), initial value: None or input class parameter

  • popNeurons (dict) – dict that contains the number of neuron of each population, at the input interface level - {“ILayer”: ilInputSize, “DGLayer”: dgInputSize, “CA3cueLayer”: self.cueSize, “CA3contLayer”: self.contSize, “CA1Layer”: self.cueSize, “OLayer”: ilInputSize}

  • neuronParameters (dict) – all neuron parameters of each population (for more information see Custom config files)

  • initNeuronParameters (dict) – init membrane potential of each population (for more information see Custom config files)

  • synParameters (dict) – all synapses parameters of each synapse group (for more information see Custom config files)

  • IL_CA3contL_conn (synapse) – IL-CA3cont synapses

  • CA3cueL_CA3contL_conn (synapse) – CA3cue-CA3cont synapses (STDP)

  • CA3contL_OL_conn (synapse) – CA3cont-OL synapses

create_population()

Create all populations of the memory model

Returns:

create_synapses()

Create all synapses of the memory model

Returns:

open_config_files()

Open configuration json file with all the internal parameters needed by the network and assign parameters to variables

Returns:

read_json()

Open json file

Raises:

NameError: path to config file not found

Returns:

the json data as a dict

Return type:

dict

How to use the model

To integrate the memory model in your own network, just import the model class and instantiate it:

from sPyMem.hippocampus_with_forgetting import hippocampus_with_forgetting

memory = hippocampus_with_forgetting.Memory(cueSize, contSize, sim, ILayer, OLayer)

The full example can be found at sPyMem Github, and for other examples see Test and applications section.

This model of memory can perform 3 basic operations: learning memories, reacalling learned memories and forgetting them. Forgetting will take place automatically when an attempt is made to learn a memory with the same cue as a previously stored memory.

In order to carry out learning and recall operations in this model, it is necessary to consider the following. For learning operations, spikes need to be held for 3 time units at the input of the memory and no further operation can be performed until 7 time units later. In the case of recall operations, spikes must be displayed for a single time unit and 6 time units must be waited until the next operation.

When performing a learning operation, the network stores a memory and, 8 time units after having started the operation, the memory returns the learned memory to its output. In the case of a recall operation, after 6 time units the cue used to start the operation will appear at the memory output and one time unit later the rest of the memory.

For more information on this temporality, principles of operation, internal functioning, … read the paper.

Custom config files

When the memory model is instantiated, if no value is passed to the configFilePath variable, it will take the default configuration file. This can be found in the sPyMem Github repository.

This file contains all the parameters that define the neuron models and the initial state of the neurons of each population, as well as all the parameters necessary to create the synapses between populations. Among these synapses, there are also those connecting the input and output populations of the model.

Although changes to any internal network parameters are discouraged as the results of the network could be unpredictable, the user is allowed to define a different configuration file. This could be done by downloading the default configuration file, changing the desired parameters and passing the complete path to it as the network creation parameter. In this way, the network will take the internal parameters indicated in the new configuration file.

The file format is a json file with 3 main fields:

  • neuronParameters: parameters of the LIF neuron model used for each population.

  • initNeuronParameters: initial conditions at the level of membrane potential of the neurons in each population.

  • synParameters: internal parameters of the synapse models used for each set of connections between populations.