# Copyright 2024 BDP Ecosystem Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from typing import Optional
from braincore._common import set_module_as
from braincore._module import (Module, Projection, Dynamics, ReceiveInputProj)
from braincore.mixin import (BindCondData, Mode)
from ._utils import is_instance
__all__ = [
'VanillaProj',
]
@set_module_as('braincore')
class VanillaProj(Projection):
"""Synaptic projection which defines the synaptic computation with the dimension of pre-synaptic neuron group.
**Code Examples**
To simulate an E/I balanced network model:
.. code-block::
class EINet(bp.DynSysGroup):
def __init__(self):
super().__init__()
self.N = bp.dyn.LifRef(4000, V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,
V_initializer=bp.init.Normal(-55., 2.))
self.delay = bp.VarDelay(self.N.spike, entries={'I': None})
self.syn1 = bp.dyn.Expon(size=3200, tau=5.)
self.syn2 = bp.dyn.Expon(size=800, tau=10.)
self.E = bp.dyn.VanillaProj(comm=bp.dnn.JitFPHomoLinear(3200, 4000, prob=0.02, weight=0.6),
out=bp.dyn.COBA(E=0.),
post=self.N)
self.I = bp.dyn.VanillaProj(comm=bp.dnn.JitFPHomoLinear(800, 4000, prob=0.02, weight=6.7),
out=bp.dyn.COBA(E=-80.),
post=self.N)
def update(self, input):
spk = self.delay.at('I')
self.E(self.syn1(spk[:3200]))
self.I(self.syn2(spk[3200:]))
self.delay(self.N(input))
return self.N.spike.value
model = EINet()
indices = bm.arange(1000)
spks = bm.for_loop(lambda i: model.step_run(i, 20.), indices)
bp.visualize.raster_plot(indices, spks, show=True)
Args:
comm: The synaptic communication.
out: The synaptic output.
post: The post-synaptic neuron group.
name: str. The projection name.
mode: Mode. The computing mode.
"""
_invisible_nodes = ['post', ]
def __init__(
self,
comm: Module,
out: BindCondData,
post: ReceiveInputProj,
name: Optional[str] = None,
mode: Optional[Mode] = None,
):
super().__init__(name=name, mode=mode)
# synaptic models
is_instance(comm, Module)
is_instance(out, BindCondData)
is_instance(post, Dynamics)
self.comm = comm
# output initialization
post.add_input_fun(self.name, out)
self.out = out
self.post = post
[docs]
def update(self, x):
current = self.comm(x)
self.out.bind_cond(current)
return current
