def test_homo_values(self):
bp.math.random.seed(1345)
size = 200
conn = bp.conn.FixedProb(prob=0.5, seed=123)
# conn = bp.conn.All2All()
conn(pre_size=size, post_size=size)
post_ids, indptr = conn.require('pre2post')
sps = bm.random.random(size).value < 0.5
# print(sps)
value = 3.0233
a = event_sum(sps, (post_ids.value, indptr.value), size, value)
print(a)
def pre2post_event_sum(events, pre2post, post_num, values=1.):
"""The pre-to-post synaptic computation with event-driven summation.
When ``values`` is a scalar, this function is equivalent to
.. highlight:: python
.. code-block:: python
post_val = np.zeros(post_num)
post_ids, idnptr = pre2post
for i in range(pre_num):
if events[i]:
for j in range(idnptr[i], idnptr[i+1]):
post_val[post_ids[i]] += values
When ``values`` is a vector (with the length of ``len(post_ids)``),
this function is equivalent to
.. highlight:: python
.. code-block:: python
post_val = np.zeros(post_num)
post_ids, idnptr = pre2post
for i in range(pre_num):
if events[i]:
for j in range(idnptr[i], idnptr[i+1]):
post_val[post_ids[i]] += values[j]
Parameters
----------
events: JaxArray, jax.numpy.ndarray, Variable
The events, must be bool.
pre2post: tuple of JaxArray, tuple of jax.numpy.ndarray
A tuple contains the connection information of pre-to-post.
post_num: int
The number of post-synaptic group.
values: float, JaxArray, jax.numpy.ndarray
The value to make summation.
Returns
-------
out: JaxArray, jax.numpy.ndarray
A tensor with the shape of ``post_num``.
"""
_check_brainpylib(pre2post_event_sum.__name__)
indices, idnptr = pre2post
events = as_device_array(events)
indices = as_device_array(indices)
idnptr = as_device_array(idnptr)
values = as_device_array(values)
return brainpylib.event_sum(events, (indices, idnptr), post_num, values)
def test_heter_value(self):
bp.math.random.seed(3)
size = 200
conn = bp.conn.FixedProb(prob=0.5, seed=3)
# conn = bp.conn.One2One()
conn(pre_size=size, post_size=size)
post_ids, indptr = conn.require('pre2post')
# sps = bm.random.randint(0, 2, size).value < 1
sps = bm.random.random(size).value < 0.5
values = bm.random.rand(post_ids.size)
# values = bm.ones(post_ids.size)
a = event_sum(sps, (post_ids.value, indptr.value), size, values.value)
print(a)