def main():

    fs = 48e3

    ### Make sound
    tmax = 0.03
    t = np.arange(0, tmax, 1 / fs)
    s = dsp.chirp(t, 80, t[-1], 16000)
    sound = cochlea.set_dbspl(s, 50)

    ### Run model
    anf = run_matlab_auditory_periphery(
        sound,
        fs,
        anf_num=(100, 50, 20),
        cf=(125, 16000, 80),
        seed=0,
    )

    ### Accumulate spike trains
    anf_acc = th.accumulate(anf, keep=['cf', 'duration'])
    anf_acc.sort('cf', ascending=False, inplace=True)

    ### Plot auditory nerve response
    fig, ax = plt.subplots(2, 1, sharex=True)
    th.plot_signal(signal=sound, fs=fs, ax=ax[0])
    th.plot_neurogram(anf_acc, fs, ax=ax[1])
    plt.show()
def main():

    fs = 100e3

    ### Make sound
    t = np.arange(0, 0.1, 1/fs)
    s = dsp.chirp(t, 80, t[-1], 20000)
    s = cochlea.set_dbspl(s, 50)
    s = np.concatenate( (s, np.zeros(10e-3 * fs)) )



    ### Run model
    rates = cochlea.run_zilany2014_rate(
        s,
        fs,
        anf_types=['msr'],
        cf=(125, 20000, 100),
        powerlaw='approximate',
        species='human'
    )


    ### Plot rates
    fig, ax = plt.subplots()
    img = ax.imshow(
        rates.T,
        aspect='auto'
    )
    plt.colorbar(img)
    plt.show()
示例#3
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def main():

    fs = 100e3

    ### Make sound
    t = np.arange(0, 0.1, 1 / fs)
    s = dsp.chirp(t, 80, t[-1], 20000)
    s = cochlea.set_dbspl(s, 50)
    pad = np.zeros(10e-3 * fs)
    sound = np.concatenate((s, pad))

    ### Run model
    anf = cochlea.run_zilany2014(
        sound, fs, anf_num=(100, 0, 0), cf=(125, 20000, 100), seed=0, powerlaw="approximate", species="human"
    )

    ### Accumulate spike trains
    anf_acc = th.accumulate(anf, keep=["cf", "duration"])
    anf_acc.sort("cf", ascending=False, inplace=True)

    ### Plot auditory nerve response
    fig, ax = plt.subplots(2, 1)
    th.plot_signal(signal=sound, fs=fs, ax=ax[0])
    th.plot_neurogram(anf_acc, fs, ax=ax[1])
    plt.show()
示例#4
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def main():

    fs = 48e3

    ### Make sound
    t = np.arange(0, 0.1, 1 / fs)
    s = dsp.chirp(t, 80, t[-1], 20000)
    s = cochlea.set_dbspl(s, 50)
    s = np.concatenate((s, np.zeros(int(10e-3 * fs))))

    ### Run model
    anf_trains = cochlea.run_holmberg2007(
        s,
        fs,
        anf_num=(100, 0, 0),
        seed=0,
    )

    ### Plot auditory nerve response
    anf_acc = th.accumulate(anf_trains, keep=['cf', 'duration'])
    anf_acc.sort('cf', ascending=False, inplace=True)

    fig, ax = plt.subplots(2, 1)
    th.plot_signal(signal=s, fs=fs, ax=ax[0])
    th.plot_neurogram(anf_acc, fs, ax=ax[1])
    plt.show()
示例#5
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def main():
    fs = 100e3
    cf = 8e3

    ### Make sound
    t = np.arange(0, 0.1, 1/fs)
    tone = np.sin(2*np.pi*t*cf)

    tone = cochlea.set_dbspl(tone, 20)

    pad = np.zeros(50e-3 * fs)
    sound = np.concatenate( (tone, pad) )



    ### Run model
    anf = cochlea.run_zilany2014(
        sound,
        fs,
        anf_num=(200,0,0),
        cf=cf,
        seed=0,
        powerlaw='approximate',
        species='human'
    )


    print(anf.head(20))


    ### Plot PSTH
    all_spikes = np.concatenate(anf.spikes)
    tmax = anf.duration.max()

    bin_size = 1e-3

    fig,ax = plt.subplots()

    ax.hist(
        all_spikes,
        bins=int(tmax/bin_size),
        range=(0,tmax),
        weights=np.ones_like(all_spikes)/bin_size/len(anf)
    )

    ax.set_xlabel("Time [s]")
    ax.set_ylabel("Rate [spikes/s]")
    ax.set_title("PSTH")

    plt.show()
示例#6
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def main():
    fs = 100e3
    cf = 8e3

    ### Make sound
    t = np.arange(0, 0.1, 1/fs)
    tone = np.sin(2*np.pi*t*cf)

    tone = cochlea.set_dbspl(tone, 20)

    pad = np.zeros(50e-3 * fs)
    sound = np.concatenate( (tone, pad) )



    ### Run model
    anf = cochlea.run_zilany2014(
        sound,
        fs,
        anf_num=(200,0,0),
        cf=cf,
        seed=0,
        powerlaw='approximate',
        species='human'
    )


    print(anf.head(20))


    ### Plot PSTH
    all_spikes = np.concatenate(anf.spikes)
    tmax = anf.duration.max()

    bin_size = 1e-3

    fig,ax = plt.subplots()

    ax.hist(
        all_spikes,
        bins=int(tmax/bin_size),
        range=(0,tmax),
        weights=np.ones_like(all_spikes)/bin_size/len(anf)
    )

    ax.set_xlabel("Time [s]")
    ax.set_ylabel("Rate [spikes/s]")
    ax.set_title("PSTH")

    plt.show()
示例#7
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def main():

    fs = 100e3

    ### Make sound
    t = np.arange(0, 0.1, 1/fs)
    s = dsp.chirp(t, 80, t[-1], 20000)
    s = cochlea.set_dbspl(s, 50)
    s = np.concatenate( (s, np.zeros(10e-3 * fs)) )



    ### Run model
    anf = cochlea.run_zilany2009(
        s,
        fs,
        anf_num=(100,0,0),
        cf=(80, 20000, 100),
        seed=0,
        powerlaw='approximate'
    )



    ### Plot auditory nerve response
    anf_acc = th.accumulate(anf, keep=['cf', 'duration'])
    anf_acc.sort('cf', ascending=False, inplace=True)

    cfs = anf.cf.unique()

    fig, ax = plt.subplots(2,1, sharex=True)
    th.plot_neurogram(
        anf_acc,
        fs,
        ax=ax[0]
    )

    th.plot_raster(
        anf[anf.cf==cfs[30]],
        ax=ax[1]
    )

    ax[1].set_title("CF = {}".format(cfs[30]))

    plt.show()
def main():

    fs = 48e3

    ### Make sound
    tmax = 0.03
    t = np.arange(0, tmax, 1/fs)
    s = dsp.chirp(t, 80, t[-1], 16000)
    sound = cochlea.set_dbspl(s, 50)



    ### Run model
    anf = run_matlab_auditory_periphery(
        sound,
        fs,
        anf_num=(100,50,20),
        cf=(125, 16000, 80),
        seed=0,
    )



    ### Accumulate spike trains
    anf_acc = th.accumulate(anf, keep=['cf', 'duration'])
    anf_acc.sort('cf', ascending=False, inplace=True)



    ### Plot auditory nerve response
    fig, ax = plt.subplots(2, 1, sharex=True)
    th.plot_signal(
        signal=sound,
        fs=fs,
        ax=ax[0]
    )
    th.plot_neurogram(
        anf_acc,
        fs,
        ax=ax[1]
    )
    plt.show()
示例#9
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def main():

    fs = 100e3

    # Make sound
    t = np.arange(0, 0.1, 1/fs)
    s = dsp.chirp(t, 80, t[-1], 20000)
    s = cochlea.set_dbspl(s, 50)
    s = np.concatenate((s, np.zeros(int(10e-3*fs))))

    # Run model
    anf = cochlea.run_zilany2009(
        s,
        fs,
        anf_num=(100,0,0),
        cf=(80, 20000, 100),
        seed=0,
        powerlaw='approximate'
    )

    # Plot auditory nerve response
    anf_acc = th.accumulate(anf, keep=['cf', 'duration'])
    anf_acc.sort_values('cf', ascending=False, inplace=True)

    cfs = anf.cf.unique()

    fig, ax = plt.subplots(2,1, sharex=True)
    th.plot_neurogram(
        anf_acc,
        fs,
        ax=ax[0]
    )

    th.plot_raster(
        anf[anf.cf==cfs[30]],
        ax=ax[1]
    )

    ax[1].set_title("CF = {}".format(cfs[30]))

    plt.show()
示例#10
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def main():

    fs = 100e3

    # Make sound
    t = np.arange(0, 0.1, 1/fs)
    s = dsp.chirp(t, 80, t[-1], 20000)
    s = cochlea.set_dbspl(s, 50)
    pad = np.zeros(int(10e-3 * fs))
    sound = np.concatenate( (s, pad) )

    # Run model
    anf = cochlea.run_zilany2014(
        sound,
        fs,
        anf_num=(100,0,0),
        cf=(125, 20000, 100),
        seed=0,
        powerlaw='approximate',
        species='human',
    )

    # Accumulate spike trains
    anf_acc = th.accumulate(anf, keep=['cf', 'duration'])
    anf_acc.sort_values('cf', ascending=False, inplace=True)

    # Plot auditory nerve response
    fig, ax = plt.subplots(2,1)
    th.plot_signal(
        signal=sound,
        fs=fs,
        ax=ax[0]
    )
    th.plot_neurogram(
        anf_acc,
        fs,
        ax=ax[1]
    )
    plt.show()
示例#11
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def main():

    fs = 48e3

    ### Make sound
    t = np.arange(0, 0.1, 1/fs)
    s = dsp.chirp(t, 80, t[-1], 20000)
    s = cochlea.set_dbspl(s, 50)
    s = np.concatenate( (s, np.zeros(10e-3 * fs)) )



    ### Run model
    anf_trains = cochlea.run_holmberg2007(
        s,
        fs,
        anf_num=(100,0,0),
        seed=0,
    )


    ### Plot auditory nerve response
    anf_acc = th.accumulate(anf_trains, keep=['cf', 'duration'])
    anf_acc.sort('cf', ascending=False, inplace=True)


    fig, ax = plt.subplots(2,1)
    th.plot_signal(
        signal=s,
        fs=fs,
        ax=ax[0]
    )
    th.plot_neurogram(
        anf_acc,
        fs,
        ax=ax[1]
    )
    plt.show()
示例#12
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def main():

    fs = 100e3

    ### Make sound
    t = np.arange(0, 0.1, 1 / fs)
    s = dsp.chirp(t, 80, t[-1], 20000)
    s = cochlea.set_dbspl(s, 50)
    s = np.concatenate((s, np.zeros(10e-3 * fs)))

    ### Run model
    rates = cochlea.run_zilany2014_rate(s,
                                        fs,
                                        anf_types=['msr'],
                                        cf=(125, 20000, 100),
                                        powerlaw='approximate',
                                        species='human')

    ### Plot rates
    fig, ax = plt.subplots()
    img = ax.imshow(rates.T, aspect='auto')
    plt.colorbar(img)
    plt.show()
def run_exp(c_freq, itd, str_e, str_i):
    # br.globalprefs.set_global_preferences(useweave=True, openmp=True, usecodegen=True,
    #                                       usecodegenweave=True )

    br.defaultclock.dt = 20E-6 * second

    #Basic Parameters
    fs_coch = 100e3  # s/second
    duration = 100E-3  # seconds##
    pad = 20E-3  # second
    n_neuron = 300
    dbspl = 50
    n_neuron = 500
    dt_coch = 1 / fs_coch
    n_pad = int(pad / dt_coch)
    n_itd = int(itd / dt_coch)
    const_dt = 100e-6

    sound = audio.generate_tone(c_freq, duration, fs_coch)
    sound = sound * audio.cosine_fade_window(sound, 20e-3, fs_coch)
    sound = coch.set_dbspl(sound, dbspl)
    sound = np.concatenate((np.zeros(n_pad), sound, np.zeros(n_pad)))
    sound = audio.delay_signal(sound, np.abs(itd), fs_coch)

    if itd < 0:
        sound = sound[:, ::-1]
    duration = len(sound) / fs_coch

    # construct ipsi and contra-lateral ANF trains and convert them to
    # neuron groups
    cochlea_train_left = coch.run_zilany2014(sound=sound[:, 0],
                                             fs=fs_coch,
                                             anf_num=(n_neuron, 0, 0),
                                             cf=c_freq,
                                             species='human',
                                             seed=0)

    cochlea_train_right = coch.run_zilany2014(sound=sound[:, 1],
                                              fs=fs_coch,
                                              anf_num=(n_neuron, 0, 0),
                                              cf=c_freq,
                                              species='human',
                                              seed=0)

    anf_group_left = coch.make_brian_group(cochlea_train_left)
    anf_group_right = coch.make_brian_group(cochlea_train_right)

    # Setup a new mso group and new gbc groups
    mso_group_left = make_mso_group(n_neuron)
    mso_group_right = make_mso_group(n_neuron)

    gbc_group_left = cochlea_to_gbc(anf_group_left, n_neuron)
    gbc_group_right = cochlea_to_gbc(anf_group_right, n_neuron)

    # Synaptic connections for the groups
    syn_mso_l_in_ipsi, syn_mso_l_in_contra = inhibitory_to_mso(
        mso_group=mso_group_left,
        ipsi_group=gbc_group_left['neuron_groups'][0],
        contra_group=gbc_group_right['neuron_groups'][0],
        strength=str_i,
        ipsi_delay=0,
        contra_delay=-0.6e-3 + const_dt)

    syn_mso_r_in_ipsi, syn_mso_r_in_contra = inhibitory_to_mso(
        mso_group=mso_group_right,
        ipsi_group=gbc_group_right['neuron_groups'][0],
        contra_group=gbc_group_left['neuron_groups'][0],
        strength=str_i,
        ipsi_delay=0,
        contra_delay=-0.6e-3 + const_dt)

    syn_mso_l_ex_ipsi, syn_mso_l_ex_contra = excitatory_to_mso(
        mso_group=mso_group_left,
        ipsi_group=anf_group_left,
        contra_group=anf_group_right,
        strength=str_e,
        contra_delay=const_dt)

    syn_mso_r_ex_ipsi, syn_mso_r_ex_contra = excitatory_to_mso(
        mso_group=mso_group_right,
        ipsi_group=anf_group_right,
        contra_group=anf_group_left,
        strength=str_e,
        contra_delay=const_dt)

    sp_mon_left = br.SpikeMonitor(mso_group_left, record=True)
    sp_mon_right = br.SpikeMonitor(mso_group_right, record=True)

    network = ([
        mso_group_left, mso_group_right, anf_group_left, anf_group_right,
        syn_mso_l_ex_ipsi, syn_mso_l_ex_contra, syn_mso_l_in_ipsi,
        syn_mso_l_in_contra, syn_mso_r_ex_ipsi, syn_mso_r_ex_contra,
        syn_mso_r_in_ipsi, syn_mso_r_in_contra, sp_mon_left, sp_mon_right
    ] + gbc_group_left['neuron_groups'] + gbc_group_right['neuron_groups'])

    run(duration, network)

    mso_train_left = thorns.make_trains(sp_mon_left)
    mso_train_right = thorns.make_trains(sp_mon_right)

    return {'spikes_left': mso_train_left, 'spikes_right': mso_train_right}