Single Trial Sequence learning: a spiking neurons model based on hippocampus (Coppolino et al 2021)

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Accession:266849
In contrast with our everyday experience using brain circuits, it can take a prohibitively long time to train a computational system to produce the correct sequence of outputs in the presence of a series of inputs. This suggests that something important is missing in the way in which models are trying to reproduce basic cognitive functions. In this work, we introduce a new neuronal network architecture that is able to learn, in a single trial, an arbitrary long sequence of any known objects. The key point of the model is the explicit use of mechanisms and circuitry observed in the hippocampus. By directly following the natural system’s layout and circuitry, this type of implementation has the additional advantage that the results can be more easily compared to experimental data, allowing a deeper and more direct understanding of the mechanisms underlying cognitive functions and dysfunctions.
Reference:
1 . Coppolino S, Giacopelli G, Migliore M (2021) Sequence learning in a single trial: a spiking neurons model based on hippocampal circuitry IEEE Transactions on Neural Networks and Learning Systems (in press)
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire leaky neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: PyNN; Python;
Model Concept(s): Place cell/field; Direction Selectivity; Persistent activity;
Implementer(s):
/
SNN_Robot_Model
Learning
Recall
README.txt
                            
Model files from the paper:
"Sequence learning in a single trial: a spiking neurons model based on hippocampal circuitry
by Simone Coppolino, Giuseppe Giacopelli, and Michele Migliore, IEEE Transactions on Neural Networks and Learning Systems, 2020, in press.

The model can be run in the Neurorobotics Platform of the Human Brain Project (https://neurorobotics.net/). 
In this file we report all the steps to install and run the model.
The NRP has an Online and a Local version, we suggest to use the online version. 
Instruction to install the local version through the link: https://bitbucket.org/hbpneurorobotics/neurorobotics-platform/src/master/ 


1) If you do not already have an EBRAINS account, register to create one: https://ebrains.eu/register/

2) Login into the NRP platform with your EBRAINS/HBP credentials (Chrome or Mozilla browsers): https://neurorobotics.net/access-the-nrp.html

3) Extract the zip file in the personal computer

4) Import all folders in NRP account. 

5) Initializate the simulation by clicking on the launch button (it can take up to a few minutes)

6) Click on the play button and enjoy the simulation.

Some additional infos:

The model is in two parts, learning and recall. It permits to write the synaptic weights during the learning phase, in CSV files.
In this way it is possibile to use them for the recall phase using the following steps:

a) Near the launch button, in your NRP home, there is the "files" button. Clicking on this shows all the files in the simulation.
b) On the left, under the name of simulation, there is the CSV_records folder. In that folder there are all the CSV files.
c) Download all of them.
d) Upload the files in the recall simulation. It's not necessary create a new folder, just put the CSV file in the main folder (which contains the TFs and Brain)

Do not hesistate to contact us for any question.

simone.coppolino@ibf.cnr.it

 

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