Latest paper is here: https://arxiv.org/abs/1805.09001

code for figures and experiments

Figure 3: typical lambda function and their f.p.

conn_09_typical_funcs_convergence.py with different plasticity functions PF32, PF12, PF30, PF15.

Figure 4: one-to-one mapping from stimulus to strength

conn_08_s_x_relation_pf_30.py

conn_08_s_x_relation_pf_32.py

Figure 5: different stimulus

conn_09_typical_funcs_convergence.py with plasticity functions PF12.

conn_10_s_x_relation_sin_pf.py to plot theta function.

Figure 6: Theta function of discontinuous lambda

conn_14_s_x_relation_discont_pf.py

Figure 7: lambda of choice

conn_12_pf_of_year.py constructs linear and threshhold-like thelta functions.

Figure 9: neural network to f.p. on different lambda

nn_02_different_e_to_fp.py with different plasticity functions PF32, PF12, PF30, PF15.

meshed-NN

training: nn_meshed_train_batch.sh

testing: nn_meshed_test_batch.sh

per-digit testing: nn_meshed_test_number_batch.sh

growable-NN

training: nn_growable_train_batch.sh

testing: nn_growable_test_batch.sh

Figure 12: meshed-NN classifier for digit 6

nn_meshed_dist_6_digit.py

Figure 13: meshed-NN classifier for all digits

nn_meshed_dist_digit.py

Figure 15: Optimization of growable classifier

nn_growable_6_digit_80_81.py

Name		Name	Last commit message	Last commit date
Latest commit History 75 Commits
backup		backup
pkl		pkl
.gitignore		.gitignore
README.md		README.md
__init__.py		__init__.py
average_image.png		average_image.png
calculate_sample_mean.py		calculate_sample_mean.py
conn_01.png		conn_01.png
conn_01_different_e_to_fp.py		conn_01_different_e_to_fp.py
conn_02.png		conn_02.png
conn_02_multiple_fp.py		conn_02_multiple_fp.py
conn_03.png		conn_03.png
conn_03_pf_y=x.py		conn_03_pf_y=x.py
conn_04.png		conn_04.png
conn_04_fp_on_0_and_1.py		conn_04_fp_on_0_and_1.py
conn_05.png		conn_05.png
conn_05_discontinuity.py		conn_05_discontinuity.py
conn_06.png		conn_06.png
conn_06_pf_y=-x.py		conn_06_pf_y=-x.py
conn_07.png		conn_07.png
conn_07_e_trajectory.py		conn_07_e_trajectory.py
conn_08_pf30.png		conn_08_pf30.png
conn_08_pf32.png		conn_08_pf32.png
conn_08_s_x_relation_pf_30.py		conn_08_s_x_relation_pf_30.py
conn_08_s_x_relation_pf_32.py		conn_08_s_x_relation_pf_32.py
conn_09_pf12.png		conn_09_pf12.png
conn_09_typical_funcs_convergence.py		conn_09_typical_funcs_convergence.py
conn_10_pf12.png		conn_10_pf12.png
conn_10_pf15.png		conn_10_pf15.png
conn_10_s_x_relation_sin_pf.py		conn_10_s_x_relation_sin_pf.py
conn_11.png		conn_11.png
conn_11_s_x_relation_counter_example.py		conn_11_s_x_relation_counter_example.py
conn_12.png		conn_12.png
conn_12_pf_of_year.py		conn_12_pf_of_year.py
conn_13_pf15.png		conn_13_pf15.png
conn_13_trajectory_detail.py		conn_13_trajectory_detail.py
conn_14_pf15.png		conn_14_pf15.png
conn_14_s_x_relation_discont_pf.py		conn_14_s_x_relation_discont_pf.py
connection.py		connection.py
neural_network.py		neural_network.py
nn.py		nn.py
nn_01_propagation_depth.py		nn_01_propagation_depth.py
nn_02_different_e_to_fp.py		nn_02_different_e_to_fp.py
nn_03_multiple_upstream_stimuli.py		nn_03_multiple_upstream_stimuli.py
nn_04_circle_path.py		nn_04_circle_path.py
nn_05_propagation_depth_2.py		nn_05_propagation_depth_2.py
nn_06_propagation_depth_3.py		nn_06_propagation_depth_3.py
nn_06_propagation_depth_3.sh		nn_06_propagation_depth_3.sh
nn_06_propagation_depth_4.py		nn_06_propagation_depth_4.py
nn_06_propagation_depth_plotting.py		nn_06_propagation_depth_plotting.py
nn_06_propagation_depth_plotting_std.py		nn_06_propagation_depth_plotting_std.py
nn_06_propagation_depth_typical_4.py		nn_06_propagation_depth_typical_4.py
nn_07_fp_count.py		nn_07_fp_count.py
nn_07_fp_count_2.py		nn_07_fp_count_2.py
nn_08.sh		nn_08.sh
nn_08_too_many_fp.py		nn_08_too_many_fp.py
nn_09_neuron_string.py		nn_09_neuron_string.py
nn_growable.log		nn_growable.log
nn_growable.py		nn_growable.py
nn_growable_6_NN.py		nn_growable_6_NN.py
nn_growable_6_digit.py		nn_growable_6_digit.py
nn_growable_6_digit_80_81.py		nn_growable_6_digit_80_81.py
nn_growable_6_digit_z_score.png		nn_growable_6_digit_z_score.png
nn_growable_6_digit_z_score.py		nn_growable_6_digit_z_score.py
nn_growable_classification.py		nn_growable_classification.py
nn_growable_classification_mixed_strategy.py		nn_growable_classification_mixed_strategy.py
nn_growable_dist.py		nn_growable_dist.py
nn_growable_test.log		nn_growable_test.log
nn_growable_test_6_batch.sh		nn_growable_test_6_batch.sh
nn_growable_test_batch.sh		nn_growable_test_batch.sh
nn_growable_train.py		nn_growable_train.py
nn_growable_train_batch.sh		nn_growable_train_batch.sh
nn_growable_train_not_average_image.py		nn_growable_train_not_average_image.py
nn_growable_z_dist_batch.sh		nn_growable_z_dist_batch.sh
nn_meshed.py		nn_meshed.py
nn_meshed_classification.py		nn_meshed_classification.py
nn_meshed_dist_6_NN.py		nn_meshed_dist_6_NN.py
nn_meshed_dist_6_digit.py		nn_meshed_dist_6_digit.py
nn_meshed_dist_NN.py		nn_meshed_dist_NN.py
nn_meshed_dist_digit.py		nn_meshed_dist_digit.py
nn_meshed_test.log		nn_meshed_test.log
nn_meshed_test_batch.sh		nn_meshed_test_batch.sh
nn_meshed_test_number.log		nn_meshed_test_number.log
nn_meshed_test_number_batch.sh		nn_meshed_test_number_batch.sh
nn_meshed_train.log		nn_meshed_train.log
nn_meshed_train.py		nn_meshed_train.py
nn_meshed_train_batch.sh		nn_meshed_train_batch.sh
nn_meshed_z_dist_6_digit.png		nn_meshed_z_dist_6_digit.png
nn_meshed_z_dist_digit.png		nn_meshed_z_dist_digit.png
nn_records.txt		nn_records.txt
simu_01.py		simu_01.py
simu_05.py		simu_05.py
skl.py		skl.py
strengthen_functions.py		strengthen_functions.py
utils.py		utils.py

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Latest paper is here: https://arxiv.org/abs/1805.09001

code for figures and experiments

Figure 3: typical lambda function and their f.p.

Figure 4: one-to-one mapping from stimulus to strength

Figure 5: different stimulus

Figure 6: Theta function of discontinuous lambda

Figure 7: lambda of choice

Figure 9: neural network to f.p. on different lambda

meshed-NN

growable-NN

Figure 12: meshed-NN classifier for digit 6

Figure 13: meshed-NN classifier for all digits

Figure 15: Optimization of growable classifier

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Latest paper is here: https://arxiv.org/abs/1805.09001

code for figures and experiments

Figure 3: typical lambda function and their f.p.

Figure 4: one-to-one mapping from stimulus to strength

Figure 5: different stimulus

Figure 6: Theta function of discontinuous lambda

Figure 7: lambda of choice

Figure 9: neural network to f.p. on different lambda

meshed-NN

growable-NN

Figure 12: meshed-NN classifier for digit 6

Figure 13: meshed-NN classifier for all digits

Figure 15: Optimization of growable classifier

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