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i-do-ai/
fci-neuron-tcns

Name: fci-neuron-tcns
Availability: InStock

pytorch

neuroscience

computational-neuroscience

temporal-convolutional-network

cc-by-nc-nd-4.0

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FCI neuron TCNs

The trained networks behind the Functional Complexity Index values reported in

Dendritic morphology and synaptic nonlinearities enhance functional complexity in human cortical neurons Ido Aizenbud, Daniela Yoeli, David Beniaguev, Christiaan P. J. de Kock, Michael London, Idan Segev. PNAS 123(28), e2533168123 (2026).

Code: https://github.com/ido4848/FCI. Training data: i-do-ai/fci-neuron-simulations.

For each of the 24 neuron models: the three seeds of the depth-3, width-128 temporal convolutional network of the paper (neuron_tcn_d3_w128_fk54_lk12_sd0_5_bs180_lr0_01_wd1e_08_vlw0_02_t{0,1,2} internally), trained to predict the neuron's output spikes and somatic voltage from its synaptic input. FCI = log10(1000 (1 - AUC)) / log10(1000 (1 - 0.9)), from the AUC on the normalized test set (a subset of the test simulations whose mean output rate is exactly 1 Hz).

Layout

models/<model>/results.csv, final_results.pkl                        final AUCs of all 12 networks trained on the model (depths 1, 2, 3, 7 x 3 seeds)
models/<model>/d3_w128_t<seed>/args.pkl, final_results.pkl           the network's training arguments and final results
models/<model>/d3_w128_t<seed>/models/model_<epoch>_<steps>          the best checkpoint (torch.save)
models/<model>/d3_w128_t<seed>/models/model_*_light.pkl              every evaluation along training (best_save + eval_history, plain pickle)

<model> is the model's folder name under simulating_neurons/neuron_models/ in the code repository. A d3_w128_t<seed> folder has the layout train_neuron_tcn.py writes, so

python calculate_fci.py --neuron_tcn_folder models/<model>/d3_w128_t0 --use_normalized

prints the FCI of that seed's checkpoint. The checkpoint is a dict with model_state_dict, args, in_chans, best_save, eval_history, epoch, step, ...; load it with Tcn.get_tcn_from_file(path, 'cpu') from training_nets/train_neuron_tcn.py. Compared with the checkpoints written during training, the optimizer state was removed and the weights were cast from float64 to float32 (355 MB → 59 MB per network; the network runs in float64 and its outputs change by ~1e-7). One checkpoint (Rat_L6_IPC_BBP_Mandge_diams_fixed_passive_dends_simple_soma, seed 1) was pruned before the release and only its light checkpoints, i.e. its AUC, survive.

Which number is in the paper

Every network was evaluated on the normalized test set every ~50,000 steps and its model_*_light.pkl files keep that history (eval_history['normalized_test'][step]['auc']). The checkpoint shipped for a seed is best_save: the evaluation with the highest normalized AUC over the whole run. The paper (Fig. 2A) took, per seed, the highest normalized AUC among the evaluations made within the first 500,000 training steps, and printed above each neuron the minimum FCI over its three seeds - the "Fig. 2A" column below, which reproduces the paper's 24 values exactly. Where a run kept improving after step 500,000 the shipped checkpoint is a little better than the paper's number for that seed.

python

import pickle, glob, numpy as np
from calculate_fci import auc_to_fci
light = max(glob.glob('models/<model>/d3_w128_t0/models/model_*_light.pkl'), key=lambda f: int(f.split('_')[-3]))
history = pickle.load(open(light, 'rb'))['eval_history']['normalized_test']
paper_auc = max(v['auc'] for step, v in history.items() if step <= 500000)
print(auc_to_fci(paper_auc))

Networks

Model	Seed	Checkpoint (step)	Normalized AUC	FCI	Paper: normalized AUC (step)	Paper: FCI	Fig. 2A
`Rat_L2_TPC_BBP_Mandge_diams_fixed_passive_dends_simple_soma`	d3_w128_t0	model_7_500040 (500,040)	0.99768	0.1830	0.99760 (300,300)	0.1902
`Rat_L2_TPC_BBP_Mandge_diams_fixed_passive_dends_simple_soma`	d3_w128_t1	model_6_450420 (450,420)	0.99763	0.1877	0.99763 (450,420)	0.1877	0.1877
`Rat_L2_TPC_BBP_Mandge_diams_fixed_passive_dends_simple_soma`	d3_w128_t2	model_7_500160 (500,160)	0.99740	0.2078	0.99728 (450,420)	0.2174
`Rat_L23_PC_cADpyr229_1_BBP_passive_dends_simple_soma`	d3_w128_t0	model_6_450495 (450,495)	0.99717	0.2260	0.99717 (450,495)	0.2260	0.2260
`Rat_L23_PC_cADpyr229_1_BBP_passive_dends_simple_soma`	d3_w128_t1	model_4_350415 (350,415)	0.99708	0.2327	0.99708 (350,415)	0.2327
`Rat_L23_PC_cADpyr229_1_BBP_passive_dends_simple_soma`	d3_w128_t2	model_7_500085 (500,085)	0.99729	0.2162	0.99706 (350,415)	0.2339
`Rat_L23_PC_cADpyr229_5_BBP_passive_dends_simple_soma`	d3_w128_t0	model_5_450450 (450,450)	0.99728	0.2170	0.99728 (450,450)	0.2170
`Rat_L23_PC_cADpyr229_5_BBP_passive_dends_simple_soma`	d3_w128_t1	model_5_450480 (450,480)	0.99730	0.2156	0.99730 (450,480)	0.2156	0.2156
`Rat_L23_PC_cADpyr229_5_BBP_passive_dends_simple_soma`	d3_w128_t2	model_8_500160 (500,160)	0.99705	0.2347	0.99686 (350,370)	0.2487
`Rat_L4_TPC_BBP_Mandge_passive_dends_simple_soma`	d3_w128_t0	model_3_300300 (300,300)	0.99745	0.2032	0.99745 (300,300)	0.2032	0.2032
`Rat_L4_TPC_BBP_Mandge_passive_dends_simple_soma`	d3_w128_t1	model_5_400380 (400,380)	0.99727	0.2177	0.99727 (400,380)	0.2177
`Rat_L4_TPC_BBP_Mandge_passive_dends_simple_soma`	d3_w128_t2	model_3_250200 (250,200)	0.99691	0.2452	0.99691 (250,200)	0.2452
`Rat_L4_PC_cADpyr230_1_BBP_passive_dends_simple_soma`	d3_w128_t0	model_5_400455 (400,455)	0.99674	0.2568	0.99674 (400,455)	0.2568
`Rat_L4_PC_cADpyr230_1_BBP_passive_dends_simple_soma`	d3_w128_t1	model_4_300330 (300,330)	0.99675	0.2560	0.99675 (300,330)	0.2560
`Rat_L4_PC_cADpyr230_1_BBP_passive_dends_simple_soma`	d3_w128_t2	model_9_500355 (500,355)	0.99719	0.2242	0.99703 (350,370)	0.2362	0.2362
`Rat_L4_PC_cADpyr230_2_BBP_passive_dends_simple_soma`	d3_w128_t0	model_5_400440 (400,440)	0.99673	0.2570	0.99673 (400,440)	0.2570
`Rat_L4_PC_cADpyr230_2_BBP_passive_dends_simple_soma`	d3_w128_t1	model_7_500160 (500,160)	0.99714	0.2283	0.99675 (300,480)	0.2560
`Rat_L4_PC_cADpyr230_2_BBP_passive_dends_simple_soma`	d3_w128_t2	model_7_500160 (500,160)	0.99705	0.2348	0.99691 (300,360)	0.2452	0.2452
`Rat_L5_TPC_BBP_Mandge_passive_dends_simple_soma`	d3_w128_t0	model_5_400320 (400,320)	0.99574	0.3148	0.99574 (400,320)	0.3148
`Rat_L5_TPC_BBP_Mandge_passive_dends_simple_soma`	d3_w128_t1	model_5_400320 (400,320)	0.99654	0.2697	0.99654 (400,320)	0.2697
`Rat_L5_TPC_BBP_Mandge_passive_dends_simple_soma`	d3_w128_t2	model_5_450360 (450,360)	0.99682	0.2510	0.99682 (450,360)	0.2510	0.2510
`Rat_L5_TTPC1_cADpyr232_1_BBP_diams_fixed_passive_dends_simple_soma`	d3_w128_t0	model_4_350280 (350,280)	0.99627	0.2857	0.99627 (350,280)	0.2857
`Rat_L5_TTPC1_cADpyr232_1_BBP_diams_fixed_passive_dends_simple_soma`	d3_w128_t1	model_5_450360 (450,360)	0.99682	0.2509	0.99682 (450,360)	0.2509	0.2509
`Rat_L5_TTPC1_cADpyr232_1_BBP_diams_fixed_passive_dends_simple_soma`	d3_w128_t2	model_7_500040 (500,040)	0.99622	0.2886	0.99618 (450,360)	0.2910
`Rat_L5b_PC_2_Hay_passive_dends_simple_soma`	d3_w128_t0	model_5_400365 (400,365)	0.99634	0.2815	0.99634 (400,365)	0.2815
`Rat_L5b_PC_2_Hay_passive_dends_simple_soma`	d3_w128_t1	model_6_450405 (450,405)	0.99706	0.2342	0.99706 (450,405)	0.2342	0.2342
`Rat_L5b_PC_2_Hay_passive_dends_simple_soma`	d3_w128_t2	model_7_500100 (500,100)	0.99709	0.2322	0.99662 (400,365)	0.2643
`Rat_L6_UPC_BBP_Mandge_passive_dends_simple_soma`	d3_w128_t0	model_7_500175 (500,175)	0.99661	0.2652	0.99629 (450,495)	0.2845
`Rat_L6_UPC_BBP_Mandge_passive_dends_simple_soma`	d3_w128_t1	model_7_500040 (500,040)	0.99702	0.2374	0.99698 (450,540)	0.2399	0.2399
`Rat_L6_UPC_BBP_Mandge_passive_dends_simple_soma`	d3_w128_t2	model_8_500175 (500,175)	0.99666	0.2618	0.99614 (250,245)	0.2930
`Rat_L6_TPC_BBP_Mandge_passive_dends_simple_soma`	d3_w128_t0	model_3_300360 (300,360)	0.99685	0.2495	0.99685 (300,360)	0.2495
`Rat_L6_TPC_BBP_Mandge_passive_dends_simple_soma`	d3_w128_t1	model_9_500280 (500,280)	0.99720	0.2237	0.99717 (300,360)	0.2260
`Rat_L6_TPC_BBP_Mandge_passive_dends_simple_soma`	d3_w128_t2	model_5_450540 (450,540)	0.99743	0.2052	0.99743 (450,540)	0.2052	0.2052
`Rat_L6_IPC_BBP_Mandge_diams_fixed_passive_dends_simple_soma`	d3_w128_t0	model_5_450600 (450,600)	0.99739	0.2081	0.99739 (450,600)	0.2081
`Rat_L6_IPC_BBP_Mandge_diams_fixed_passive_dends_simple_soma`	d3_w128_t1	light checkpoints only (450,540)	0.99752	0.1972	0.99752 (450,540)	0.1972	0.1972
`Rat_L6_IPC_BBP_Mandge_diams_fixed_passive_dends_simple_soma`	d3_w128_t2	model_5_400440 (400,440)	0.99744	0.2039	0.99744 (400,440)	0.2039
`Human_L23_PC_0603_11_937_Eyal_passive_dends_simple_soma`	d3_w128_t0	model_7_500160 (500,160)	0.99282	0.4280	0.99275 (450,300)	0.4303
`Human_L23_PC_0603_11_937_Eyal_passive_dends_simple_soma`	d3_w128_t1	model_3_250140 (250,140)	0.99277	0.4294	0.99277 (250,140)	0.4294	0.4294
`Human_L23_PC_0603_11_937_Eyal_passive_dends_simple_soma`	d3_w128_t2	model_7_500130 (500,130)	0.99286	0.4269	0.99234 (300,210)	0.4422
`Human_L23_PC_1303_03_448_Eyal_passive_dends_simple_soma`	d3_w128_t0	model_6_451035 (451,035)	0.99319	0.4165	0.99319 (451,035)	0.4165	0.4165
`Human_L23_PC_1303_03_448_Eyal_passive_dends_simple_soma`	d3_w128_t1	model_5_450495 (450,495)	0.99301	0.4222	0.99301 (450,495)	0.4222
`Human_L23_PC_1303_03_448_Eyal_passive_dends_simple_soma`	d3_w128_t2	model_3_250335 (250,335)	0.99267	0.4326	0.99267 (250,335)	0.4326
`Human_L3_PC_0_BBP_passive_dends_simple_soma`	d3_w128_t0	model_7_500040 (500,040)	0.99342	0.4092	0.99311 (400,560)	0.4190	0.4190
`Human_L3_PC_0_BBP_passive_dends_simple_soma`	d3_w128_t1	model_5_400560 (400,560)	0.99268	0.4324	0.99268 (400,560)	0.4324
`Human_L3_PC_0_BBP_passive_dends_simple_soma`	d3_w128_t2	model_7_500160 (500,160)	0.99257	0.4354	0.99202 (350,520)	0.4510
`Human_L4_PC_BBP_Mandge_diams_fixed_passive_dends_simple_soma`	d3_w128_t0	model_7_500085 (500,085)	0.99383	0.3951	0.99326 (300,330)	0.4143
`Human_L4_PC_BBP_Mandge_diams_fixed_passive_dends_simple_soma`	d3_w128_t1	model_5_450405 (450,405)	0.99381	0.3957	0.99381 (450,405)	0.3957	0.3957
`Human_L4_PC_BBP_Mandge_diams_fixed_passive_dends_simple_soma`	d3_w128_t2	model_6_450540 (450,540)	0.99323	0.4154	0.99323 (450,540)	0.4154
`Human_L4_PC_539661667_Allen_passive_dends_simple_soma`	d3_w128_t0	model_4_350250 (350,250)	0.99451	0.3697	0.99451 (350,250)	0.3697
`Human_L4_PC_539661667_Allen_passive_dends_simple_soma`	d3_w128_t1	model_5_450330 (450,330)	0.99469	0.3626	0.99469 (450,330)	0.3626	0.3626
`Human_L4_PC_539661667_Allen_passive_dends_simple_soma`	d3_w128_t2	model_4_350250 (350,250)	0.99428	0.3786	0.99428 (350,250)	0.3786
`Human_L4_PC_569818704_Allen_passive_dends_simple_soma`	d3_w128_t0	model_7_500175 (500,175)	0.99438	0.3747	0.99436 (450,495)	0.3757	0.3757
`Human_L4_PC_569818704_Allen_passive_dends_simple_soma`	d3_w128_t1	model_5_400320 (400,320)	0.99378	0.3969	0.99378 (400,320)	0.3969
`Human_L4_PC_569818704_Allen_passive_dends_simple_soma`	d3_w128_t2	model_7_500040 (500,040)	0.99395	0.3909	0.99393 (300,375)	0.3917
`Human_L5_PC_BBP_Mandge_passive_dends_simple_soma`	d3_w128_t0	model_7_500040 (500,040)	0.99494	0.3521	0.99458 (400,500)	0.3672
`Human_L5_PC_BBP_Mandge_passive_dends_simple_soma`	d3_w128_t1	model_4_350370 (350,370)	0.99419	0.3822	0.99419 (350,370)	0.3822
`Human_L5_PC_BBP_Mandge_passive_dends_simple_soma`	d3_w128_t2	model_5_450495 (450,495)	0.99471	0.3618	0.99471 (450,495)	0.3618	0.3618
`Human_L5_PC_0_BBP_passive_dends_simple_soma`	d3_w128_t0	model_6_450420 (450,420)	0.99455	0.3683	0.99455 (450,420)	0.3683
`Human_L5_PC_0_BBP_passive_dends_simple_soma`	d3_w128_t1	model_5_450405 (450,405)	0.99458	0.3672	0.99458 (450,405)	0.3672	0.3672
`Human_L5_PC_0_BBP_passive_dends_simple_soma`	d3_w128_t2	model_4_350310 (350,310)	0.99418	0.3826	0.99418 (350,310)	0.3826
`Human_L5_PC_790872626_Allen_passive_dends_simple_soma`	d3_w128_t0	model_5_450405 (450,405)	0.99333	0.4121	0.99333 (450,405)	0.4121
`Human_L5_PC_790872626_Allen_passive_dends_simple_soma`	d3_w128_t1	model_7_500085 (500,085)	0.99354	0.4051	0.99349 (450,405)	0.4068
`Human_L5_PC_790872626_Allen_passive_dends_simple_soma`	d3_w128_t2	model_5_400350 (400,350)	0.99388	0.3934	0.99388 (400,350)	0.3934	0.3934
`Human_L6_PC_558211203_Allen_passive_dends_simple_soma`	d3_w128_t0	model_7_500085 (500,085)	0.99307	0.4205	0.99275 (350,415)	0.4302
`Human_L6_PC_558211203_Allen_passive_dends_simple_soma`	d3_w128_t1	model_7_500175 (500,175)	0.99373	0.3988	0.99343 (400,860)	0.4088
`Human_L6_PC_558211203_Allen_passive_dends_simple_soma`	d3_w128_t2	model_6_450765 (450,765)	0.99368	0.4004	0.99368 (450,765)	0.4004	0.4004
`Human_L6_PC_548494556_Allen_passive_dends_simple_soma`	d3_w128_t0	model_5_450495 (450,495)	0.99574	0.3146	0.99574 (450,495)	0.3146	0.3146
`Human_L6_PC_548494556_Allen_passive_dends_simple_soma`	d3_w128_t1	model_5_400410 (400,410)	0.99505	0.3472	0.99505 (400,410)	0.3472
`Human_L6_PC_548494556_Allen_passive_dends_simple_soma`	d3_w128_t2	model_7_500175 (500,175)	0.99522	0.3396	0.99522 (450,495)	0.3399
`Human_L6_PC_528614014_Allen_passive_dends_simple_soma`	d3_w128_t0	model_7_500130 (500,130)	0.99553	0.3251	0.99488 (300,300)	0.3545
`Human_L6_PC_528614014_Allen_passive_dends_simple_soma`	d3_w128_t1	model_6_450390 (450,390)	0.99548	0.3274	0.99548 (450,390)	0.3274	0.3274
`Human_L6_PC_528614014_Allen_passive_dends_simple_soma`	d3_w128_t2	model_7_500130 (500,130)	0.99548	0.3274	0.99548 (350,340)	0.3277

Citation

bibtex

@article{aizenbud2026fci,
    title   = {Dendritic morphology and synaptic nonlinearities enhance functional complexity in human cortical neurons},
    author  = {Aizenbud, Ido and Yoeli, Daniela and Beniaguev, David and de Kock, Christiaan P. J. and London, Michael and Segev, Idan},
    journal = {Proceedings of the National Academy of Sciences},
    volume  = {123},
    number  = {28},
    pages   = {e2533168123},
    year    = {2026},
    doi     = {10.1073/pnas.2533168123}
}

i-do-ai/fci-neuron-tcns