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Merge pull request #500 from AkitsuFaye/master
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Add strings in bp._src.dyn.bio_models and abstract_models
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@chaoming0625
chaoming0625 authored Sep 14, 2023
2 parents d106f94 + 8b67a14 commit 01edee4
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Showing 3 changed files with 506 additions and 4 deletions.
2 changes: 1 addition & 1 deletion brainpy/_src/dyn/neurons/lif.py
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Original file line number Diff line number Diff line change
Expand Up @@ -167,7 +167,7 @@ class LifLTC(GradNeuDyn):
:math:`V_{th}` is the spike threshold, :math:`\tau` is the time constant,
and :math:`I` is the time-variant synaptic inputs.
There is an example usage: mustang u r lvd by the blonde boy
There is an example usage:
.. code-block:: python
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329 changes: 327 additions & 2 deletions brainpy/_src/dyn/synapses/abstract_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -51,7 +51,6 @@ class Delta(SynDyn, AlignPost):
"The Synapse." Principles of Computational Modelling in Neuroscience.
Cambridge: Cambridge UP, 2011. 172-95. Print.
Args:
"""

def __init__(
Expand Down Expand Up @@ -111,12 +110,73 @@ class Expon(SynDyn, AlignPost):
& \frac{d g}{d t} = -\frac{g}{\tau_{decay}}+\sum_{k} \delta(t-t_{j}^{k}).
\end{aligned}
This module can be used with interface ``brainpy.dyn.ProjAlignPreMg2``, as shown in the following example:
.. code-block:: python
import numpy as np
import brainpy as bp
import brainpy.math as bm
import matplotlib.pyplot as plt
class ExponSparseCOBA(bp.Projection):
def __init__(self, pre, post, delay, prob, g_max, tau, E):
super().__init__()
self.proj = bp.dyn.ProjAlignPreMg2(
pre=pre,
delay=delay,
syn=bp.dyn.Expon.desc(pre.num, tau=tau),
comm=bp.dnn.CSRLinear(bp.conn.FixedProb(prob, pre=pre.num, post=post.num), g_max),
out=bp.dyn.COBA(E=E),
post=post,
)
class SimpleNet(bp.DynSysGroup):
def __init__(self, syn_cls, E=0.):
super().__init__()
self.pre = bp.dyn.SpikeTimeGroup(1, indices=(0, 0, 0, 0), times=(10., 30., 50., 70.))
self.post = bp.dyn.LifRef(1, V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,
V_initializer=bp.init.Constant(-60.))
self.syn = syn_cls(self.pre, self.post, delay=None, prob=1., g_max=1., tau=5., E=E)
def update(self):
self.pre()
self.syn()
self.post()
# monitor the following variables
conductance = self.syn.proj.refs['syn'].g
current = self.post.sum_inputs(self.post.V)
return conductance, current, self.post.V
Moreover, it can also be used with interface ``ProjAlignPostMg2``:
.. code-block:: python
class ExponSparseCOBAPost(bp.Projection):
def __init__(self, pre, post, delay, prob, g_max, tau, E):
super().__init__()
self.proj = bp.dyn.ProjAlignPostMg2(
pre=pre,
delay=delay,
comm=bp.dnn.EventCSRLinear(bp.conn.FixedProb(prob, pre=pre.num, post=post.num), g_max),
syn=bp.dyn.Expon.desc(post.num, tau=tau),
out=bp.dyn.COBA.desc(E=E),
post=post,
)
.. [1] Sterratt, David, Bruce Graham, Andrew Gillies, and David Willshaw.
"The Synapse." Principles of Computational Modelling in Neuroscience.
Cambridge: Cambridge UP, 2011. 172-95. Print.
Args:
tau: float, ArrayType, Callable. The time constant of decay. [ms]
tau: float. The time constant of decay. [ms]
%s
"""

Expand Down Expand Up @@ -197,6 +257,66 @@ class DualExpon(SynDyn):
&\frac{d h}{d t}=-\frac{h}{\tau_{\text {rise }}}+ \delta\left(t_{0}-t\right),
\end{aligned}
This module can be used with interface ``brainpy.dyn.ProjAlignPreMg2``, as shown in the following example:
.. code-block:: python
import numpy as np
import brainpy as bp
import brainpy.math as bm
import matplotlib.pyplot as plt
class DualExpSparseCOBA(bp.Projection):
def __init__(self, pre, post, delay, prob, g_max, tau_decay, tau_rise, E):
super().__init__()
self.proj = bp.dyn.ProjAlignPreMg2(
pre=pre,
delay=delay,
syn=bp.dyn.DualExpon.desc(pre.num, tau_decay=tau_decay, tau_rise=tau_rise),
comm=bp.dnn.CSRLinear(bp.conn.FixedProb(prob, pre=pre.num, post=post.num), g_max),
out=bp.dyn.COBA(E=E),
post=post,
)
class SimpleNet(bp.DynSysGroup):
def __init__(self, syn_cls, E=0.):
super().__init__()
self.pre = bp.dyn.SpikeTimeGroup(1, indices=(0, 0, 0, 0), times=(10., 30., 50., 70.))
self.post = bp.dyn.LifRef(1, V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,
V_initializer=bp.init.Constant(-60.))
self.syn = syn_cls(self.pre, self.post, delay=None, prob=1., g_max=1.,
tau_decay=5., tau_rise=1., E=E)
def update(self):
self.pre()
self.syn()
self.post()
# monitor the following variables
conductance = self.syn.proj.refs['syn'].g
current = self.post.sum_inputs(self.post.V)
return conductance, current, self.post.V
indices = np.arange(1000) # 100 ms, dt= 0.1 ms
net = SimpleNet(DualExpSparseCOBA, E=0.)
conductances, currents, potentials = bm.for_loop(net.step_run, indices, progress_bar=True)
ts = indices * bm.get_dt()
fig, gs = bp.visualize.get_figure(1, 3, 3.5, 4)
fig.add_subplot(gs[0, 0])
plt.plot(ts, conductances)
plt.title('Syn conductance')
fig.add_subplot(gs[0, 1])
plt.plot(ts, currents)
plt.title('Syn current')
fig.add_subplot(gs[0, 2])
plt.plot(ts, potentials)
plt.title('Post V')
plt.show()
.. [1] Sterratt, David, Bruce Graham, Andrew Gillies, and David Willshaw.
"The Synapse." Principles of Computational Modelling in Neuroscience.
Cambridge: Cambridge UP, 2011. 172-95. Print.
Expand Down Expand Up @@ -276,6 +396,87 @@ class DualExponV2(SynDyn, AlignPost):
is the time constant of the rise phase, :math:`t_0` is the time of the pre-synaptic
spike, :math:`g_{\mathrm{max}}` is the maximal conductance.
This module can be used with interface ``brainpy.dyn.ProjAlignPreMg2``, as shown in the following example:
.. code-block:: python
import numpy as np
import brainpy as bp
import brainpy.math as bm
import matplotlib.pyplot as plt
class DualExponV2SparseCOBA(bp.Projection):
def __init__(self, pre, post, delay, prob, g_max, tau_decay, tau_rise, E):
super().__init__()
self.proj = bp.dyn.ProjAlignPreMg2(
pre=pre,
delay=delay,
syn=bp.dyn.DualExponV2.desc(pre.num, tau_decay=tau_decay, tau_rise=tau_rise),
comm=bp.dnn.CSRLinear(bp.conn.FixedProb(prob, pre=pre.num, post=post.num), g_max),
out=bp.dyn.COBA(E=E),
post=post,
)
class SimpleNet(bp.DynSysGroup):
def __init__(self, syn_cls, E=0.):
super().__init__()
self.pre = bp.dyn.SpikeTimeGroup(1, indices=(0, 0, 0, 0), times=(10., 30., 50., 70.))
self.post = bp.dyn.LifRef(1, V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,
V_initializer=bp.init.Constant(-60.))
self.syn = syn_cls(self.pre, self.post, delay=None, prob=1., g_max=1., tau_decay=5., tau_rise=1., E=E)
def update(self):
self.pre()
self.syn()
self.post()
# monitor the following variables
conductance = self.syn.proj.refs['syn'].g_rise
current = self.post.sum_inputs(self.post.V)
return conductance, current, self.post.V
indices = np.arange(1000) # 100 ms, dt= 0.1 ms
net = SimpleNet(DualExponV2SparseCOBAPost, E=0.)
conductances, currents, potentials = bm.for_loop(net.step_run, indices, progress_bar=True)
ts = indices * bm.get_dt()
fig, gs = bp.visualize.get_figure(1, 3, 3.5, 4)
fig.add_subplot(gs[0, 0])
plt.plot(ts, conductances)
plt.title('Syn conductance')
fig.add_subplot(gs[0, 1])
plt.plot(ts, currents)
plt.title('Syn current')
fig.add_subplot(gs[0, 2])
plt.plot(ts, potentials)
plt.title('Post V')
plt.show()
Moreover, it can also be used with interface ``ProjAlignPostMg2``:
.. code-block:: python
class DualExponV2SparseCOBAPost(bp.Projection):
def __init__(self, pre, post, delay, prob, g_max, tau_decay, tau_rise, E):
super().__init__()
self.proj = bp.dyn.ProjAlignPostMg2(
pre=pre,
delay=delay,
comm=bp.dnn.EventCSRLinear(bp.conn.FixedProb(prob, pre=pre.num, post=post.num), g_max),
syn=bp.dyn.DualExponV2.desc(post.num, tau_decay=tau_decay, tau_rise=tau_rise),
out=bp.dyn.COBA.desc(E=E),
post=post,
)
.. [1] Sterratt, David, Bruce Graham, Andrew Gillies, and David Willshaw.
"The Synapse." Principles of Computational Modelling in Neuroscience.
Cambridge: Cambridge UP, 2011. 172-95. Print.
Expand Down Expand Up @@ -361,6 +562,70 @@ class Alpha(DualExpon):
&\frac{d h}{d t}=-\frac{h}{\tau}+\delta\left(t_{0}-t\right)
\end{aligned}
This module can be used with interface ``brainpy.dyn.ProjAlignPreMg2``, as shown in the following example:
.. code-block:: python
import numpy as np
import brainpy as bp
import brainpy.math as bm
import matplotlib.pyplot as plt
class AlphaSparseCOBA(bp.Projection):
def __init__(self, pre, post, delay, prob, g_max, tau_decay, E):
super().__init__()
self.proj = bp.dyn.ProjAlignPreMg2(
pre=pre,
delay=delay,
syn=bp.dyn.Alpha.desc(pre.num, tau_decay=tau_decay),
comm=bp.dnn.CSRLinear(bp.conn.FixedProb(prob, pre=pre.num, post=post.num), g_max),
out=bp.dyn.COBA(E=E),
post=post,
)
class SimpleNet(bp.DynSysGroup):
def __init__(self, syn_cls, E=0.):
super().__init__()
self.pre = bp.dyn.SpikeTimeGroup(1, indices=(0, 0, 0, 0), times=(10., 30., 50., 70.))
self.post = bp.dyn.LifRef(1, V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,
V_initializer=bp.init.Constant(-60.))
self.syn = syn_cls(self.pre, self.post, delay=None, prob=1., g_max=1.,
tau_decay=5., E=E)
def update(self):
self.pre()
self.syn()
self.post()
# monitor the following variables
conductance = self.syn.proj.refs['syn'].g
current = self.post.sum_inputs(self.post.V)
return conductance, current, self.post.V
indices = np.arange(1000) # 100 ms, dt= 0.1 ms
net = SimpleNet(AlphaSparseCOBA, E=0.)
conductances, currents, potentials = bm.for_loop(net.step_run, indices, progress_bar=True)
ts = indices * bm.get_dt()
fig, gs = bp.visualize.get_figure(1, 3, 3.5, 4)
fig.add_subplot(gs[0, 0])
plt.plot(ts, conductances)
plt.title('Syn conductance')
fig.add_subplot(gs[0, 1])
plt.plot(ts, currents)
plt.title('Syn current')
fig.add_subplot(gs[0, 2])
plt.plot(ts, potentials)
plt.title('Post V')
plt.show()
.. [1] Sterratt, David, Bruce Graham, Andrew Gillies, and David Willshaw.
"The Synapse." Principles of Computational Modelling in Neuroscience.
Cambridge: Cambridge UP, 2011. 172-95. Print.
Expand Down Expand Up @@ -450,6 +715,66 @@ class NMDA(SynDyn):
The NMDA receptor has been thought to be very important for controlling
synaptic plasticity and mediating learning and memory functions [3]_.
This module can be used with interface ``brainpy.dyn.ProjAlignPreMg2``, as shown in the following example:
.. code-block:: python
import numpy as np
import brainpy as bp
import brainpy.math as bm
import matplotlib.pyplot as plt
class NMDASparseCOBA(bp.Projection):
def __init__(self, pre, post, delay, prob, g_max, tau_decay, tau_rise, E):
super().__init__()
self.proj = bp.dyn.ProjAlignPreMg2(
pre=pre,
delay=delay,
syn=bp.dyn.NMDA.desc(pre.num, tau_decay=tau_decay, tau_rise=tau_rise),
comm=bp.dnn.CSRLinear(bp.conn.FixedProb(prob, pre=pre.num, post=post.num), g_max),
out=bp.dyn.COBA(E=E),
post=post,
)
class SimpleNet(bp.DynSysGroup):
def __init__(self, syn_cls, E=0.):
super().__init__()
self.pre = bp.dyn.SpikeTimeGroup(1, indices=(0, 0, 0, 0), times=(10., 30., 50., 70.))
self.post = bp.dyn.LifRef(1, V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,
V_initializer=bp.init.Constant(-60.))
self.syn = syn_cls(self.pre, self.post, delay=None, prob=1., g_max=1.,
tau_decay=5., tau_rise=1., E=E)
def update(self):
self.pre()
self.syn()
self.post()
# monitor the following variables
conductance = self.syn.proj.refs['syn'].g
current = self.post.sum_inputs(self.post.V)
return conductance, current, self.post.V
indices = np.arange(1000) # 100 ms, dt= 0.1 ms
net = SimpleNet(NMDASparseCOBA, E=0.)
conductances, currents, potentials = bm.for_loop(net.step_run, indices, progress_bar=True)
ts = indices * bm.get_dt()
fig, gs = bp.visualize.get_figure(1, 3, 3.5, 4)
fig.add_subplot(gs[0, 0])
plt.plot(ts, conductances)
plt.title('Syn conductance')
fig.add_subplot(gs[0, 1])
plt.plot(ts, currents)
plt.title('Syn current')
fig.add_subplot(gs[0, 2])
plt.plot(ts, potentials)
plt.title('Post V')
plt.show()
.. [1] Brunel N, Wang X J. Effects of neuromodulation in a
cortical network model of object working memory dominated
Expand Down
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