spiking-learn

This is "framework" for spiking neural networks machine learning based on temporal encoding.

Citation

Please, cite my last paper while using this repo https://scholar.google.com/citations?user=wiwYfQMAAAAJ&hl=en

Dependencies

• Python3
• NEST Simulator https://www.nest-simulator.org/
• sklearn
• numpy
• matplotlib
• hyperopt
• tqdm

Running

all below is deprecated

To start simulation create your own py file and add

from spilearn.solver import solve_task

solve_task(path-to-folder-with-settings-file)

or run from command line

python solver path-to-folder-with-settings-file

here is example of settings file for Fisher's Iris Classification settings.json

{
    "model": {
        "neuron_out": {
            "V_reset": 0.0,
            "E_L": 0.0,
            "I_e": 0.0,
            "C_m": 1.0,
            "V_m": 0.0,
            "t_ref": 19.0,
            "V_th": 8.0,
            "tau_m": 6.0,
            "tau_minus": 31.0
        },
        "syn_dict_inh": {
            "weight": -5,
            "synapse_model": "static_synapse"
        },
        "syn_dict_stdp_hid": {
            "weight": {
                "sigma": 0.0,
                "mu": 1.0,
                "distribution": "normal"
            },
            "mu_plus": 0.0,
            "lambda": 0.03,
            "tau_plus": 10.429564842488617,
            "mu_minus": 0.0,
            "synapse_model": "stdp_synapse",
            "Wmax": {
                "sigma": 0.0,
                "mu": 1.0,
                "distribution": "normal"
            },
            "alpha": 0.85
        },
        "neuron_hid": {
            "V_reset": -5.0,
            "E_L": 0.0,
            "I_e": 0.0,
            "C_m": 10.0,
            "V_m": -5.0,
            "t_ref": 3.0,
            "V_th": 1.0,
            "tau_m": 10.0,
            "tau_minus": 33.7
        },
        "syn_dict_stdp": {
            "weight": {
                "sigma": 0.0,
                "mu": 1.0,
                "distribution": "normal"
            },
            "mu_plus": 0.0,
            "lambda": 0.03,
            "tau_plus": 6.0,
            "mu_minus": 0.0,
            "synapse_model": "stdp_synapse",
            "Wmax": {
                "sigma": 0.0,
                "mu": 1.0,
                "distribution": "normal"
            },
            "alpha": 0.65
        },
        "neuron_out_model": "iaf_psc_exp",
        "neuron_hid_model": "iaf_psc_exp"
    },
    "learning": {
        "n_splits": 5,
        "fitness_func": "f1",
        "use_teacher": true,
        "reinforce_delta": 0.0,
        "use_fitness_func": true,
        "teacher_amplitude": 1000.0,
        "epochs": 5,
        "reinforce_time": 0.0,
        "metrics": "f1",
        "inhibitory_teacher": false,
        "reverse_learning": false,
        "threshold": 8.0
    },
    "data": {
        "coding_sigma": 0.005,
        "shuffle_train": true,
        "n_coding_neurons": 40,
        "normalization": "normalize",
        "valid_size": 0.1,
        "dataset": "iris",
        "preprocessing": "",
        "use_valid": false,
        "shuffle_test": true,
        "frequency_coding": false,
        "conversion": "receptive_fields"
    },
    "network": {
        "num_threads": 48,
        "noise_after_pattern": false,
        "h_time": 25.0,
        "noise_freq": 0.0,
        "test_with_noise": false,
        "num_procs": 1,
        "h": 0.01,
        "separate_networks": false,
        "save_history": false,
        "start_delta": 50,
        "test_with_inhibition": true
    },
    "topology": {
        "use_reciprocal": false,
        "use_inhibition": true,
        "two_layers": false,
        "n_layer_hid": 100,
        "n_layer_out": 3,
        "n_input": 80
    }
}