Version 2.3.5

This release continues to add support for brain-inspired computation.

New Features

1. brainpy.share for sharing data across submodules

In this release, we abstract the shared data as a brainpy.share object.

This object together with brainpy.Delay we will introduce below constitutes the support that enables us to define SNN models like ANN ones.

2. brainpy.Delay for delay processing

Delay is abstracted as a dynamical system, which can be updated/retrieved by users.

import brainpy as bp

class EINet(bp.DynamicalSystemNS):
  def __init__(self, scale=1.0, e_input=20., i_input=20., delay=None):
    super().__init__()

    self.bg_exc = e_input
    self.bg_inh = i_input

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

    # neurons
    pars = dict(V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,
                V_initializer=bp.init.Normal(-55., 2.), input_var=False)
    self.E = bp.neurons.LIF(num_exc, **pars)
    self.I = bp.neurons.LIF(num_inh, **pars)

    # synapses
    we = 0.6 / scale  # excitatory synaptic weight (voltage)
    wi = 6.7 / scale  # inhibitory synaptic weight
    self.E2E = bp.experimental.Exponential(
      bp.conn.FixedProb(0.02, pre=self.E.size, post=self.E.size),
      g_max=we, tau=5., out=bp.experimental.COBA(E=0.)
    )
    self.E2I = bp.experimental.Exponential(
      bp.conn.FixedProb(0.02, pre=self.E.size, post=self.I.size, ),
      g_max=we, tau=5., out=bp.experimental.COBA(E=0.)
    )
    self.I2E = bp.experimental.Exponential(
      bp.conn.FixedProb(0.02, pre=self.I.size, post=self.E.size),
      g_max=wi, tau=10., out=bp.experimental.COBA(E=-80.)
    )
    self.I2I = bp.experimental.Exponential(
      bp.conn.FixedProb(0.02, pre=self.I.size, post=self.I.size),
      g_max=wi, tau=10., out=bp.experimental.COBA(E=-80.)
    )
    self.delayE = bp.Delay(self.E.spike, entries={'E': delay})
    self.delayI = bp.Delay(self.I.spike, entries={'I': delay})

  def update(self):
    e_spike = self.delayE.at('E')
    i_spike = self.delayI.at('I')
    e_inp = self.E2E(e_spike, self.E.V) + self.I2E(i_spike, self.E.V) + self.bg_exc
    i_inp = self.I2I(i_spike, self.I.V) + self.E2I(e_spike, self.I.V) + self.bg_inh
    self.delayE(self.E(e_inp))
    self.delayI(self.I(i_inp))

3. brainpy.checkpoints.save_pytree and brainpy.checkpoints.load_pytree for saving/loading target from the filename

Now we can directly use brainpy.checkpoints.save_pytree to save a network state into the file path we specified.

Similarly, we can use brainpy.checkpoints.load_pytree to load states from the given file path.

4. More ANN layers

• brainpy.layers.ConvTranspose1d
• brainpy.layers.ConvTranspose2d
• brainpy.layers.ConvTranspose3d
• brainpy.layers.Conv1dLSTMCell
• brainpy.layers.Conv2dLSTMCell
• brainpy.layers.Conv3dLSTMCell

5. More compatible dense operators

PyTorch operators:

• brainpy.math.Tensor
• brainpy.math.flatten
• brainpy.math.cat
• brainpy.math.abs
• brainpy.math.absolute
• brainpy.math.acos
• brainpy.math.arccos
• brainpy.math.acosh
• brainpy.math.arccosh
• brainpy.math.add
• brainpy.math.addcdiv
• brainpy.math.addcmul
• brainpy.math.angle
• brainpy.math.asin
• brainpy.math.arcsin
• brainpy.math.asinh
• brainpy.math.arcsin
• brainpy.math.atan
• brainpy.math.arctan
• brainpy.math.atan2
• brainpy.math.atanh

TensorFlow operators:

• brainpy.math.concat
• brainpy.math.reduce_sum
• brainpy.math.reduce_max
• brainpy.math.reduce_min
• brainpy.math.reduce_mean
• brainpy.math.reduce_all
• brainpy.math.reduce_any
• brainpy.math.reduce_logsumexp
• brainpy.math.reduce_prod
• brainpy.math.reduce_std
• brainpy.math.reduce_variance
• brainpy.math.reduce_euclidean_norm
• brainpy.math.unsorted_segment_sqrt_n
• brainpy.math.segment_mean
• brainpy.math.unsorted_segment_sum
• brainpy.math.unsorted_segment_prod
• brainpy.math.unsorted_segment_max
• brainpy.math.unsorted_segment_min
• brainpy.math.unsorted_segment_mean
• brainpy.math.segment_sum
• brainpy.math.segment_prod
• brainpy.math.segment_max
• brainpy.math.segment_min
• brainpy.math.clip_by_value
• brainpy.math.cast

Others

• Remove the hard requirements of brainpylib and numba.