def extend_new_segment(self, id: int, owner: Axon, target: np.ndarray):
new_segment_origin = self.end
distance_from_target = np.linalg.norm(new_segment_origin - owner.target)
desired_weight, momentum_weight = self.calculate_desired_and_momentum_weights(distance_from_target)
desired_direction = get_unit_direction_vector(new_segment_origin, target)
momentum_direction = get_unit_direction_vector(self.origin, self.end)
desired_and_momentum = desired_weight * desired_direction + momentum_weight * momentum_direction
unit_desired_and_momentum = get_unit_vector(desired_and_momentum)
prenoise_pol = cart_to_pol(unit_desired_and_momentum)[1]
r = np.random.normal(retino.AXON_SEGMENT_LENGTH_AVG, retino.AXON_SEGMENT_LENGTH_STD, size=1)[0]
noise = np.random.normal(0, retino.AXON_SEGMENT_GROWTH_DIRECTION_NOISE_STD, size=1)[0]
if self.is_going_away():
self.noise = np.random.normal(0, 2, size=1)[0]
theta = prenoise_pol + noise
cart_result = pol_to_cart(np.asarray([r, theta]))
new_segment_end = new_segment_origin + cart_result
new_segment = AxonSegment(id, self, owner, origin=new_segment_origin, end=new_segment_end)
if np.linalg.norm(new_segment.end - target) < retino.SYNAPSE_ATTEMPT_GOLDILOCKS_RADIUS:
if owner.model is not None:
new_segment.attempt_synapses()
owner.segments.append(new_segment)
def attempt_synapses(self):
postsynapticcells = self.owner.model.postsynapticcells
origins = generate_random_points_along_line(self.origin, cart_to_pol(self.end - self.origin),
retino.AXON_SEGMENT_SYNAPSE_POOL_COUNT,
retino.AXON_SEGMENT_SYNAPSE_COUNT,
retino.AXON_SEGMENT_SYNAPSE_JITTER_STD)
for origin in origins:
i = choose_random_circle_as_connection_index(origin, [p.origin for p in postsynapticcells],
[p.size for p in postsynapticcells])
if i is not None:
connection = Synapse(i, self, i, origin=origin)
self.synapses.append(connection)
self.owner.synapses.append(connection)
postsynapticcells[i].synapses.append(connection)
def attempt_synapses(self):
postsynapticcells = self.owner.model.postsynapticcells
origins = generate_random_points_along_line(
self.origin, cart_to_pol(self.end - self.origin),
retino.AXON_SEGMENT_SYNAPSE_POOL_COUNT,
retino.AXON_SEGMENT_SYNAPSE_COUNT,
retino.AXON_SEGMENT_SYNAPSE_JITTER_STD)
for origin in origins:
i = choose_random_circle_as_connection_index(
origin, [p.origin for p in postsynapticcells],
[p.size for p in postsynapticcells])
if i is not None:
connection = Synapse(i, self, i, origin=origin)
self.synapses.append(connection)
self.owner.synapses.append(connection)
postsynapticcells[i].synapses.append(connection)
def extend_new_segment(self, id: int, owner: Axon, target: np.ndarray):
new_segment_origin = self.end
distance_from_target = np.linalg.norm(new_segment_origin -
owner.target)
desired_weight, momentum_weight = self.calculate_desired_and_momentum_weights(
distance_from_target)
desired_direction = get_unit_direction_vector(new_segment_origin,
target)
momentum_direction = get_unit_direction_vector(self.origin, self.end)
desired_and_momentum = desired_weight * desired_direction + momentum_weight * momentum_direction
unit_desired_and_momentum = get_unit_vector(desired_and_momentum)
prenoise_pol = cart_to_pol(unit_desired_and_momentum)[1]
r = np.random.normal(retino.AXON_SEGMENT_LENGTH_AVG,
retino.AXON_SEGMENT_LENGTH_STD,
size=1)[0]
noise = np.random.normal(
0, retino.AXON_SEGMENT_GROWTH_DIRECTION_NOISE_STD, size=1)[0]
if self.is_going_away():
self.noise = np.random.normal(0, 2, size=1)[0]
theta = prenoise_pol + noise
cart_result = pol_to_cart(np.asarray([r, theta]))
new_segment_end = new_segment_origin + cart_result
new_segment = AxonSegment(id,
self,
owner,
origin=new_segment_origin,
end=new_segment_end)
if np.linalg.norm(new_segment.end -
target) < retino.SYNAPSE_ATTEMPT_GOLDILOCKS_RADIUS:
if owner.model is not None:
new_segment.attempt_synapses()
owner.segments.append(new_segment)
