npbrain 0.2.8.0

NumpyBrain: A lightweight SNN simulation framework.

Note: NumpyBrain is a project under development. More features are coming soon. Contributions are welcome.

Why to use NumpyBrain

NumpyBrain is a microkernel framework for SNN (spiking neural network) simulation purely based on native python. It only relies on NumPy. However, if you want to get faster performance,you can additionally install Numba. With Numba, the speed of C or FORTRAN can be obtained in the simulation.

NumpyBrain wants to provide a highly flexible and efficient SNN simulation framework for Python users. It endows the users with the fully data/logic flow control. The core of the framework is a micro-kernel, and it’s easy to understand (see How NumpyBrain works). Based on the kernel, the extension of the new models or the customization of the data/logic flows are very simple for users. Ample examples (such as LIF neuron, HH neuron, or AMPA synapse, GABA synapse and GapJunction) are also provided. Besides the consideration of flexibility, for accelerating the running speed of NumPy codes, Numba is used. For most of the times, models running on Numba backend is very fast (see examples/benchmark).

More details about NumpyBrain please see our document.

Installation

Install NumpyBrain using pip:

$> pip install npbrain
$> # or
$> pip install git+https://github.com/chaoming0625/NumpyBrain

Install NumpyBrain using conda:

$> conda install -c oujago npbrain

Install from source code:

$> python setup.py install

The following packages need to be installed to use NumpyBrain:

• Python >= 3.5

• NumPy

• Numba

• Matplotlib

Getting started: 30 seconds to NumpyBrain

First of all, import the package, and set the numerical backend you prefer:

import numpy as np
import npbrain as nn

nn.profile.set_backend('numba')  # or "numpy"

Next, define two neuron groups:

lif1 = nn.LIF(500, noise=0.5, method='Ito_milstein')  # or method='euler'
lif2 = nn.LIF(1000, noise=1.1, method='Ito_milstein')

Then, create one Synapse to connect them both.

conn = nn.connect.fixed_prob(lif1.num, lif2.num, prob=0.2)
syn = nn.VoltageJumpSynapse(lif1, lif2, weights=0.2, connection=conn)

In order to inspect the dynamics of two LIF neuron groups, we use StateMonitor to record the membrane potential and the spiking events.

mon_lif1 = nn.StateMonitor(lif1, ['V', 'spike'])
mon_lif2 = nn.StateMonitor(lif2, ['V', 'spike'])

All above definitions help us to construct a network. Providing the name of the simulation object (for example, mon1=mon_lif1) can make us easy to access it by using net.mon1.

net = nn.Network(syn, lif1, lif2, mon1=mon_lif1, mon2=mon_lif2)

We can simulate the whole network just use .run(duration) function. Here, we set the inputs of lif1 object to 15., and open the report mode.

net.run(duration=100, inputs=(lif1, 15.), report=True)

Finally, visualize the running results:

fig, gs = nn.visualize.get_figure(n_row=2, n_col=1, len_row=3, len_col=8)
ts = net.run_time()
nn.visualize.plot_potential(net.mon1, ts, ax=fig.add_subplot(gs[0, 0]))
nn.visualize.plot_raster(net.mon1, ts, ax=fig.add_subplot(gs[1, 0]), show=True)

It shows

Define a Hodgkin–Huxley neuron model

import numpy as np
import npbrain as nn

def HH(geometry, method=None, noise=0., E_Na=50., g_Na=120., E_K=-77.,
       g_K=36., E_Leak=-54.387, g_Leak=0.03, C=1.0, Vr=-65., Vth=20.):

    var2index = {'V': 0, 'm': 1, 'h': 2, 'n': 3}
    num, geometry = nn.format_geometry(geometry)
    state = nn.initial_neu_state(4, num)

    @nn.update(method=method)
    def int_m(m, t, V):
        alpha = 0.1 * (V + 40) / (1 - np.exp(-(V + 40) / 10))
        beta = 4.0 * np.exp(-(V + 65) / 18)
        return alpha * (1 - m) - beta * m

    @nn.update(method=method)
    def int_h(h, t, V):
        alpha = 0.07 * np.exp(-(V + 65) / 20.)
        beta = 1 / (1 + np.exp(-(V + 35) / 10))
        return alpha * (1 - h) - beta * h

    @nn.update(method=method)
    def int_n(n, t, V):
        alpha = 0.01 * (V + 55) / (1 - np.exp(-(V + 55) / 10))
        beta = 0.125 * np.exp(-(V + 65) / 80)
        return alpha * (1 - n) - beta * n

    @nn.update(method=method, noise=noise / C)
    def int_V(V, t, Icur, Isyn):
        return (Icur + Isyn) / C

    def update_state(neu_state, t):
        V, Isyn = neu_state[0], neu_state[-1]
        m = nn.clip(int_m(neu_state[1], t, V), 0., 1.)
        h = nn.clip(int_h(neu_state[2], t, V), 0., 1.)
        n = nn.clip(int_n(neu_state[3], t, V), 0., 1.)
        INa = g_Na * m * m * m * h * (V - E_Na)
        IK = g_K * n ** 4 * (V - E_K)
        IL = g_Leak * (V - E_Leak)
        Icur = - INa - IK - IL
        V = int_V(V, t, Icur, Isyn)
        neu_state[0] = V
        neu_state[1] = m
        neu_state[2] = h
        neu_state[3] = n
        nn.judge_spike(neu_state, Vth, t)

    return nn.Neurons(**locals())

Acknowledgements

We would like to thank

• Risheng Lian

• Longping Liu

for valuable comments and discussions on the project.