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MATLAB code for a network filter model, consisting of 'filter-and-fire' neurons with both recurrent and feed-forward filters, that performs close to optimal tracking of its input.
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Example_run_model.m
Example_run_network_paper.m
README.md
run_model_correlations.m
run_model_if_sta_prc.m
run_model_noise.m
run_model_redundancy.m

README.md

Efficient-coding-in-a-spiking-predictive-coding-network

MATLAB code for a network filter model, consisting of 'filter-and-fire' neurons with both recurrent and feed-forward filters, that performs close to optimal tracking of its input.

When using this code, please cite the following preprint:

Zeldenrust, F., Gutkin, B., & Denève, S. (2019). Efficient and robust coding in heterogeneous recurrent networks. BioRxiv. https://doi.org/10.1101/804864

About

This MATLAB toolbox creates and runs the filter network for efficient coding described in the paper above. A few examples scripts are given:

  • In 'Example_run_model' it is shown how to create representing filters for each neuron, and then run the network. It first creates a set of basis-kernels (make_basisfunctions). Next, it creates a set of feed-forward filters as a random combination of these basisfunctions, and creates the corresponding recurrent filters for optimal coding (generate_filters). The network can now track any input signal (run_model).
  • In 'Example_run_network_paper' the different networks as used in the paper (parameter 'net' sets it to homogeneous, heterogeneous and type 1 & type 2) are pre-defined. A network is then created (make_kernels_network) and run.
  • The following scripts will run the simulations for the figures of the paper:
    • Figure 3: run_model_if_sta_prc
    • Figure 4: run_model_redundancy
    • Figure 5: run_model_noise
    • Figure 6: run_model_correlations

Use

see: Example_run_model and Example_run_network_paper.

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