1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
|
%global _empty_manifest_terminate_build 0
Name: python-sorn
Version: 0.7.4
Release: 1
Summary: Self-Organizing Recurrent Neural Networks
License: OSI Approved :: MIT License
URL: https://github.com/Saran-nns/sorn
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/82/6f/9e91eac1ad0241f45023034aae79684b965de0e172fe2629a18be96f834c/sorn-0.7.4.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-configparser
Requires: python3-scipy
Requires: python3-seaborn
Requires: python3-pandas
Requires: python3-networkx
%description
# Self-Organizing Recurrent Neural Networks
SORN is a class of neuro-inspired artificial network build based on plasticity mechanisms in biological brain and mimic neocortical circuits ability of learning and adaptation. SORN consists of pool of excitatory neurons and small population of inhibitory neurons which are controlled by 5 plasticity mechanisms found in neocortex, namely Spike Timing Dependent Plasticity (STDP), Intrinsic Plasticity (IP), Synaptic Scaling (SS),Synaptic Normalization(SN) and inhibitory Spike Timing Dependent Plasticity (iSTDP). Using mathematical tools, SORN network simplifies the underlying structural and functional connectivity mechanisms responsible for learning and memory in the brain
'sorn' is a Python package designed for Self Organizing Recurrent Neural Networks. It provides a research environment for computational neuroscientists to study the self-organization, adaption, learning,memory and behavior of brain circuits by reverse engineering neural plasticity mechanisms. Further to extend the potential applications of `sorn`, a demostrative example of a neuro-robotics experiment using OpenAI gym is also [documented](https://self-organizing-recurrent-neural-networks.readthedocs.io/en/latest/usage.html).
[](https://github.com/saran-nns/sorn/actions)
[](https://codecov.io/gh/Saran-nns/sorn)
[](https://self-organizing-recurrent-neural-networks.readthedocs.io/en/latest/?badge=latest)
[](https://badge.fury.io/py/sorn)
[](https://github.com/psf/black)
[](https://pepy.tech/project/sorn)
[](https://zenodo.org/badge/latestdoi/174756058)
[](https://opensource.org/licenses/MIT)
[](https://colab.research.google.com/drive/164AKTA-iCVLq-iR-treLA_Y9keRYrQkH#scrollTo=Rt2YZptMtC14)
[](https://joss.theoj.org/papers/7dc447f7a0d17d774b59c8ee15c223c2)
<h4 align="Left">SORN Reservoir</h4>
<a href="url"><img src="https://raw.githubusercontent.com/Saran-nns/sorn/master/imgs/SORN1.png" height="320" width="430"></a>
## Installation
```python
pip install sorn
```
or to install the latest version from the development branch
```python
pip install git+https://github.com/Saran-nns/sorn
```
## Dependencies
SORN supports `Python 3.7+` ONLY. For older Python versions please use the official Python client.
To install all optional dependencies,
```python
pip install 'sorn[all]'
```
## Usage
### Plasticity Phase
```python
import sorn
from sorn import Simulator
import numpy as np
# Sample input
num_features = 10
time_steps = 200
inputs = np.random.rand(num_features,time_steps)
# Simulate the network with default hyperparameters under gaussian white noise
state_dict, sim_dict = Simulator.run(inputs=inputs, phase='plasticity',
state=None, noise=True,
timesteps=time_steps,
callbacks=["ExcitatoryActivation",
"WEE",
"EEConnectionCounts"])
```
```
Network Initialized
Number of connections in Wee 3909 , Wei 1574, Wie 8000
Shapes Wee (200, 200) Wei (40, 200) Wie (200, 40)
```
### Training Phase
```python
from sorn import Trainer
# NOTE: During training phase, input to `sorn` should have second (time) dimension set to 1. ie., input shape should be (input_features,1).
inputs = np.random.rand(num_features,1)
# SORN network is frozen during training phase
state_dict, sim_dict = Trainer.run(inputs= inputs, phase='training',
state=state_dict, noise=False,
timesteps=1,
ne=100, nu=num_features,
lambda_ee=10, eta_stdp=0.001,
callbacks=["InhibitoryActivation",
"WEI",
"EIConnectionCounts"] )
```
### Network Output Descriptions
`state_dict` - Dictionary of connection weights (`Wee`, `Wei`, `Wie`) , Excitatory network activity (`X`), Inhibitory network activities(`Y`), Threshold values (`Te`, `Ti`)
`sim_dict` - Dictionary of network states and parameters collected during the simulation/training: Provided, all available options of the argument `callbacks`, then the `sim_dict` should contain the following;
"ExcitatoryActivation" - Excitatory network activity of entire simulation period
"InhibitoryActivation" - Inhibitory network activity of entire simulation period
"RecurrentActivation" - Recurrent network activity of entire simulation period
"EEConnectionCounts" - Number of active connections in the Excitatory pool at each time step
"EIConnectionCounts" - Number of active connections from Inhibitory to Excitatory pool at each time step
"TE" - Threshold values of excitatory neurons at each time step
"TI" - Threshold values of inhibitory neurons at each time step
"WEE" - Synaptic efficacies between excitatory neurons
"WEI" - Connection weights from inhibitory to excitatory neurons
"WIE" - Connection weights from excitatory to inhibitory neurons
### Documentation
For detailed documentation about development, analysis, plotting methods and a sample experiment with OpenAI Gym, please visit [SORN Documentation](https://self-organizing-recurrent-neural-networks.readthedocs.io/en/latest/)
### Citation
```Python
@article{Nambusubramaniyan2021,
doi = {10.21105/joss.03545},
url = {https://doi.org/10.21105/joss.03545},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {65},
pages = {3545},
author = {Saranraj Nambusubramaniyan},
title = {`sorn`: A Python package for Self Organizing Recurrent Neural Network},
journal = {Journal of Open Source Software}
}
```
### Contributions
I am welcoming contributions. If you wish to contribute, please create a branch with a pull request and the changes can be discussed there.
If you find a bug in the code or errors in the documentation, please open a new issue in the Github repository and report the bug or the error. Please provide sufficient information for the bug to be reproduced.
%package -n python3-sorn
Summary: Self-Organizing Recurrent Neural Networks
Provides: python-sorn
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-sorn
# Self-Organizing Recurrent Neural Networks
SORN is a class of neuro-inspired artificial network build based on plasticity mechanisms in biological brain and mimic neocortical circuits ability of learning and adaptation. SORN consists of pool of excitatory neurons and small population of inhibitory neurons which are controlled by 5 plasticity mechanisms found in neocortex, namely Spike Timing Dependent Plasticity (STDP), Intrinsic Plasticity (IP), Synaptic Scaling (SS),Synaptic Normalization(SN) and inhibitory Spike Timing Dependent Plasticity (iSTDP). Using mathematical tools, SORN network simplifies the underlying structural and functional connectivity mechanisms responsible for learning and memory in the brain
'sorn' is a Python package designed for Self Organizing Recurrent Neural Networks. It provides a research environment for computational neuroscientists to study the self-organization, adaption, learning,memory and behavior of brain circuits by reverse engineering neural plasticity mechanisms. Further to extend the potential applications of `sorn`, a demostrative example of a neuro-robotics experiment using OpenAI gym is also [documented](https://self-organizing-recurrent-neural-networks.readthedocs.io/en/latest/usage.html).
[](https://github.com/saran-nns/sorn/actions)
[](https://codecov.io/gh/Saran-nns/sorn)
[](https://self-organizing-recurrent-neural-networks.readthedocs.io/en/latest/?badge=latest)
[](https://badge.fury.io/py/sorn)
[](https://github.com/psf/black)
[](https://pepy.tech/project/sorn)
[](https://zenodo.org/badge/latestdoi/174756058)
[](https://opensource.org/licenses/MIT)
[](https://colab.research.google.com/drive/164AKTA-iCVLq-iR-treLA_Y9keRYrQkH#scrollTo=Rt2YZptMtC14)
[](https://joss.theoj.org/papers/7dc447f7a0d17d774b59c8ee15c223c2)
<h4 align="Left">SORN Reservoir</h4>
<a href="url"><img src="https://raw.githubusercontent.com/Saran-nns/sorn/master/imgs/SORN1.png" height="320" width="430"></a>
## Installation
```python
pip install sorn
```
or to install the latest version from the development branch
```python
pip install git+https://github.com/Saran-nns/sorn
```
## Dependencies
SORN supports `Python 3.7+` ONLY. For older Python versions please use the official Python client.
To install all optional dependencies,
```python
pip install 'sorn[all]'
```
## Usage
### Plasticity Phase
```python
import sorn
from sorn import Simulator
import numpy as np
# Sample input
num_features = 10
time_steps = 200
inputs = np.random.rand(num_features,time_steps)
# Simulate the network with default hyperparameters under gaussian white noise
state_dict, sim_dict = Simulator.run(inputs=inputs, phase='plasticity',
state=None, noise=True,
timesteps=time_steps,
callbacks=["ExcitatoryActivation",
"WEE",
"EEConnectionCounts"])
```
```
Network Initialized
Number of connections in Wee 3909 , Wei 1574, Wie 8000
Shapes Wee (200, 200) Wei (40, 200) Wie (200, 40)
```
### Training Phase
```python
from sorn import Trainer
# NOTE: During training phase, input to `sorn` should have second (time) dimension set to 1. ie., input shape should be (input_features,1).
inputs = np.random.rand(num_features,1)
# SORN network is frozen during training phase
state_dict, sim_dict = Trainer.run(inputs= inputs, phase='training',
state=state_dict, noise=False,
timesteps=1,
ne=100, nu=num_features,
lambda_ee=10, eta_stdp=0.001,
callbacks=["InhibitoryActivation",
"WEI",
"EIConnectionCounts"] )
```
### Network Output Descriptions
`state_dict` - Dictionary of connection weights (`Wee`, `Wei`, `Wie`) , Excitatory network activity (`X`), Inhibitory network activities(`Y`), Threshold values (`Te`, `Ti`)
`sim_dict` - Dictionary of network states and parameters collected during the simulation/training: Provided, all available options of the argument `callbacks`, then the `sim_dict` should contain the following;
"ExcitatoryActivation" - Excitatory network activity of entire simulation period
"InhibitoryActivation" - Inhibitory network activity of entire simulation period
"RecurrentActivation" - Recurrent network activity of entire simulation period
"EEConnectionCounts" - Number of active connections in the Excitatory pool at each time step
"EIConnectionCounts" - Number of active connections from Inhibitory to Excitatory pool at each time step
"TE" - Threshold values of excitatory neurons at each time step
"TI" - Threshold values of inhibitory neurons at each time step
"WEE" - Synaptic efficacies between excitatory neurons
"WEI" - Connection weights from inhibitory to excitatory neurons
"WIE" - Connection weights from excitatory to inhibitory neurons
### Documentation
For detailed documentation about development, analysis, plotting methods and a sample experiment with OpenAI Gym, please visit [SORN Documentation](https://self-organizing-recurrent-neural-networks.readthedocs.io/en/latest/)
### Citation
```Python
@article{Nambusubramaniyan2021,
doi = {10.21105/joss.03545},
url = {https://doi.org/10.21105/joss.03545},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {65},
pages = {3545},
author = {Saranraj Nambusubramaniyan},
title = {`sorn`: A Python package for Self Organizing Recurrent Neural Network},
journal = {Journal of Open Source Software}
}
```
### Contributions
I am welcoming contributions. If you wish to contribute, please create a branch with a pull request and the changes can be discussed there.
If you find a bug in the code or errors in the documentation, please open a new issue in the Github repository and report the bug or the error. Please provide sufficient information for the bug to be reproduced.
%package help
Summary: Development documents and examples for sorn
Provides: python3-sorn-doc
%description help
# Self-Organizing Recurrent Neural Networks
SORN is a class of neuro-inspired artificial network build based on plasticity mechanisms in biological brain and mimic neocortical circuits ability of learning and adaptation. SORN consists of pool of excitatory neurons and small population of inhibitory neurons which are controlled by 5 plasticity mechanisms found in neocortex, namely Spike Timing Dependent Plasticity (STDP), Intrinsic Plasticity (IP), Synaptic Scaling (SS),Synaptic Normalization(SN) and inhibitory Spike Timing Dependent Plasticity (iSTDP). Using mathematical tools, SORN network simplifies the underlying structural and functional connectivity mechanisms responsible for learning and memory in the brain
'sorn' is a Python package designed for Self Organizing Recurrent Neural Networks. It provides a research environment for computational neuroscientists to study the self-organization, adaption, learning,memory and behavior of brain circuits by reverse engineering neural plasticity mechanisms. Further to extend the potential applications of `sorn`, a demostrative example of a neuro-robotics experiment using OpenAI gym is also [documented](https://self-organizing-recurrent-neural-networks.readthedocs.io/en/latest/usage.html).
[](https://github.com/saran-nns/sorn/actions)
[](https://codecov.io/gh/Saran-nns/sorn)
[](https://self-organizing-recurrent-neural-networks.readthedocs.io/en/latest/?badge=latest)
[](https://badge.fury.io/py/sorn)
[](https://github.com/psf/black)
[](https://pepy.tech/project/sorn)
[](https://zenodo.org/badge/latestdoi/174756058)
[](https://opensource.org/licenses/MIT)
[](https://colab.research.google.com/drive/164AKTA-iCVLq-iR-treLA_Y9keRYrQkH#scrollTo=Rt2YZptMtC14)
[](https://joss.theoj.org/papers/7dc447f7a0d17d774b59c8ee15c223c2)
<h4 align="Left">SORN Reservoir</h4>
<a href="url"><img src="https://raw.githubusercontent.com/Saran-nns/sorn/master/imgs/SORN1.png" height="320" width="430"></a>
## Installation
```python
pip install sorn
```
or to install the latest version from the development branch
```python
pip install git+https://github.com/Saran-nns/sorn
```
## Dependencies
SORN supports `Python 3.7+` ONLY. For older Python versions please use the official Python client.
To install all optional dependencies,
```python
pip install 'sorn[all]'
```
## Usage
### Plasticity Phase
```python
import sorn
from sorn import Simulator
import numpy as np
# Sample input
num_features = 10
time_steps = 200
inputs = np.random.rand(num_features,time_steps)
# Simulate the network with default hyperparameters under gaussian white noise
state_dict, sim_dict = Simulator.run(inputs=inputs, phase='plasticity',
state=None, noise=True,
timesteps=time_steps,
callbacks=["ExcitatoryActivation",
"WEE",
"EEConnectionCounts"])
```
```
Network Initialized
Number of connections in Wee 3909 , Wei 1574, Wie 8000
Shapes Wee (200, 200) Wei (40, 200) Wie (200, 40)
```
### Training Phase
```python
from sorn import Trainer
# NOTE: During training phase, input to `sorn` should have second (time) dimension set to 1. ie., input shape should be (input_features,1).
inputs = np.random.rand(num_features,1)
# SORN network is frozen during training phase
state_dict, sim_dict = Trainer.run(inputs= inputs, phase='training',
state=state_dict, noise=False,
timesteps=1,
ne=100, nu=num_features,
lambda_ee=10, eta_stdp=0.001,
callbacks=["InhibitoryActivation",
"WEI",
"EIConnectionCounts"] )
```
### Network Output Descriptions
`state_dict` - Dictionary of connection weights (`Wee`, `Wei`, `Wie`) , Excitatory network activity (`X`), Inhibitory network activities(`Y`), Threshold values (`Te`, `Ti`)
`sim_dict` - Dictionary of network states and parameters collected during the simulation/training: Provided, all available options of the argument `callbacks`, then the `sim_dict` should contain the following;
"ExcitatoryActivation" - Excitatory network activity of entire simulation period
"InhibitoryActivation" - Inhibitory network activity of entire simulation period
"RecurrentActivation" - Recurrent network activity of entire simulation period
"EEConnectionCounts" - Number of active connections in the Excitatory pool at each time step
"EIConnectionCounts" - Number of active connections from Inhibitory to Excitatory pool at each time step
"TE" - Threshold values of excitatory neurons at each time step
"TI" - Threshold values of inhibitory neurons at each time step
"WEE" - Synaptic efficacies between excitatory neurons
"WEI" - Connection weights from inhibitory to excitatory neurons
"WIE" - Connection weights from excitatory to inhibitory neurons
### Documentation
For detailed documentation about development, analysis, plotting methods and a sample experiment with OpenAI Gym, please visit [SORN Documentation](https://self-organizing-recurrent-neural-networks.readthedocs.io/en/latest/)
### Citation
```Python
@article{Nambusubramaniyan2021,
doi = {10.21105/joss.03545},
url = {https://doi.org/10.21105/joss.03545},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {65},
pages = {3545},
author = {Saranraj Nambusubramaniyan},
title = {`sorn`: A Python package for Self Organizing Recurrent Neural Network},
journal = {Journal of Open Source Software}
}
```
### Contributions
I am welcoming contributions. If you wish to contribute, please create a branch with a pull request and the changes can be discussed there.
If you find a bug in the code or errors in the documentation, please open a new issue in the Github repository and report the bug or the error. Please provide sufficient information for the bug to be reproduced.
%prep
%autosetup -n sorn-0.7.4
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-sorn -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 0.7.4-1
- Package Spec generated
|
