Version 2.2.1

This release fixes bugs found in the codebase and improves the usability and functions of BrainPy.

Bug fixes

1. Fix the bug of operator customization in brainpy.math.XLACustomOp and brainpy.math.register_op. Now, it supports operator customization by using NumPy and Numba interface. For instance,

import brainpy.math as bm

def abs_eval(events, indices, indptr, post_val, values):
      return post_val

def con_compute(outs, ins):
      post_val = outs
      events, indices, indptr, _, values = ins
      for i in range(events.size):
        if events[i]:
          for j in range(indptr[i], indptr[i + 1]):
            index = indices[j]
            old_value = post_val[index]
            post_val[index] = values + old_value

event_sum = bm.XLACustomOp(eval_shape=abs_eval, con_compute=con_compute)

2. Fix the bug of brainpy.tools.DotDict. Now, it is compatible with the transformations of JAX. For instance,

import brainpy as bp
from jax import vmap

@vmap
def multiple_run(I):
  hh = bp.neurons.HH(1)
  runner = bp.dyn.DSRunner(hh, inputs=('input', I), numpy_mon_after_run=False)
  runner.run(100.)
  return runner.mon

mon = multiple_run(bp.math.arange(2, 10, 2))

New features

1. Add numpy operators brainpy.math.mat, brainpy.math.matrix, brainpy.math.asmatrix.
2. Improve translation rules of brainpylib operators, improve its running speeds.
3. Support DSView of DynamicalSystem instance. Now, it supports defining models with a slice view of a DS instance. For example,

import brainpy as bp
import brainpy.math as bm


class EINet_V2(bp.dyn.Network):
  def __init__(self, scale=1.0, method='exp_auto'):
    super(EINet_V2, self).__init__()

    # network size
    num_exc = int(3200 * scale)
    num_inh = int(800 * scale)

    # neurons
    self.N = bp.neurons.LIF(num_exc + num_inh,
                            V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,
                            method=method, V_initializer=bp.initialize.Normal(-55., 2.))

    # synapses
    we = 0.6 / scale  # excitatory synaptic weight (voltage)
    wi = 6.7 / scale  # inhibitory synaptic weight
    self.Esyn = bp.synapses.Exponential(pre=self.N[:num_exc], post=self.N,
                                        conn=bp.connect.FixedProb(0.02),
                                        g_max=we, tau=5.,
                                        output=bp.synouts.COBA(E=0.),
                                        method=method)
    self.Isyn = bp.synapses.Exponential(pre=self.N[num_exc:], post=self.N,
                                        conn=bp.connect.FixedProb(0.02),
                                        g_max=wi, tau=10.,
                                        output=bp.synouts.COBA(E=-80.),
                                        method=method)

net = EINet_V2(scale=1., method='exp_auto')
# simulation
runner = bp.dyn.DSRunner(
    net,
    monitors={'spikes': net.N.spike},
    inputs=[(net.N.input, 20.)]
  )
runner.run(100.)

# visualization
bp.visualize.raster_plot(runner.mon.ts, runner.mon['spikes'], show=True)