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Broadband encoding and high-speed probabilistic bit generation with integrated microwave neurons.

Code ↔ Paper

7 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 7 matches
  1. [1] § Methods › Training and closed-loop inference for the turn-based submarine navigation game ↔ Code_for_Submarine_turn_based_game.zip/MNN_plays_submarine_game/MNN_plays_with_angle_encoding/Train_one_simple_model_with_angle_encoding.py, lines 184–245 · score 0.76 · cross entropy loss, fold cross validation, Adam, dimensional, argmax, optimization
  2. [2] § Methods › Training and closed-loop inference for the turn-based submarine navigation game ↔ Code_for_Submarine_turn_based_game.zip/Baseline_plays_submarine_game/Baseline_plays_with_angles_encoding/Training_baseline_to_play_submarine_game_with_angle_encoding.py, lines 182–253 · score 0.74 · cross entropy loss, fold cross validation, Adam, argmax, optimization, class
  3. [3] § Methods › Training and closed-loop inference for the turn-based submarine navigation game ↔ Code_for_Submarine_turn_based_game.zip/Baseline_plays_submarine_game/Baseline_plays_without_angle_encoding/Evaluate_baseline_play_submarine_game_without_angle_encoding.py, lines 121–218 · score 0.62 · speed constraint, failure, movement, position, bounces, player
  4. [4] § Methods › Modeling feature extraction mechanisms through the MNN’s nonlinear dynamics ↔ Code for coupled mode simulation with analog pulse tokens.zip/Run_CMT_model_for_data_tokens.py, lines 49–103 · score 0.57 · fast bit, coupled mode, wave, zero, simulate, analog
  5. [5] § Methods › Training and closed-loop inference for the turn-based submarine navigation game ↔ Code_for_Submarine_turn_based_game.zip/Baseline_plays_submarine_game/Baseline_plays_without_angle_encoding/Search_algorithms_to_map_submarine_states_and_pulse_tokens_without_angle.py, lines 1–49 · score 0.57 · angle bins, submarine states, Discrete, greedy, mapped, game
  6. [6] § Methods › Modeling feature extraction mechanisms through the MNN’s nonlinear dynamics ↔ Code for coupled mode simulation with analog pulse tokens.zip/ode45_numpy.py, lines 9–50 · score 0.52 · Runge Kutta Fehlberg
  7. [7] § Methods › Chip design, experimental data acquisition, and baseline generation ↔ Code for coupled mode simulation with analog pulse tokens.zip/Run_CMT_model_for_data_tokens.py, lines 49–103 · score 0.51 · analog pulse, parametric coupling, repetition, durations, amplitudes, tokens

Paper

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The authors' code

Python · 192 lines · 6.5 KB · CC-BY-4.0 · 2 matches

  1. import os
  2. import numpy as np
  3. import matplotlib.pyplot as plt
  4. from equations import Equations_complexSTD
  5. from ode45_numpy import ode45_numpy
  6. from multiprocessing import Pool, cpu_count
  7. from tqdm import tqdm
  9. DISPLAY = False
  11. # period of the oscillator
  12. OSC_PERIOD = 0.78
  13. F_IN = 1.0 / (8 * OSC_PERIOD) # = 0.3205128...
  14. PRE_WINDOW = 400.0 # how much time *before* starting_time to keep
  15. NUM_SWEEPS = 100 # number of start-time phases to sweep
  16. BASE_START = 20000 # base start time for fast bits
  17. NUM_PUMPS = 1 # number of 8-bit repetitions after starting time (excited)
  18. TOTAL_PUMPS = 10 # total number of 8-bit "slots" (excited + zero)
  21. # ================== GLOBAL SETUP (RESULTS, RNG, FAST BITS, INITIAL CONDITION) ==================
  23. BASE_DIR = os.path.dirname(os.path.abspath(__file__))
  25. # Seed once for reproducibility (affects initial condition)
  26. np.random.seed(42)
  28. # FIXED fast-bit pattern 01010101
  29. fast_bits = np.array([1, 0, 0, 0, 1, 0, 0, 0], dtype=int)
  30. bitstream_str = ''.join(str(int(b)) for b in fast_bits)
  32. # Results folder: name includes the bitstream, e.g. results_01010101
  33. RESULTS_DIR = os.path.join(BASE_DIR, f"results_{bitstream_str}")
  34. os.makedirs(RESULTS_DIR, exist_ok=True)
  35. print("Saving to:", RESULTS_DIR)
  36. print("Using fast_bits =", fast_bits)
  38. # Build a *reference* equations object just to know N
  39. _eq_ref = Equations_complexSTD()
  40. N_OSC = _eq_ref.N
  42. # >>> SAME INITIAL CONDITION FOR ALL RUNS <<<
  43. y_complex_init_global = np.random.rand(N_OSC) + 1j * np.random.rand(N_OSC)
  44. print("Using the SAME initial condition for all trials.")
  47. # ================== CORE SIM FUNCTION ==================
  49. def run_sim_measure_starting(fast_bits, param_amplitude=10, starting_time=20000, num_pumps=20):
  50. """
  51. Run the complex oscillator, with:
  52. - NO slow bits
  53. - fast bits only, turned on at `starting_time`
  54. - `num_pumps` repetitions of the 8-bit fast_bits sequence after starting_time
  55. - remaining (TOTAL_PUMPS - num_pumps) pumps are all zero (no Vin)
  56. - SAME initial condition for all runs (y_complex_init_global)
  57. """
  59. # total time:
  60. # - run until starting_time
  61. # - then run long enough for TOTAL_PUMPS * 8 bits at rate F_IN
  62. t_end = starting_time + (TOTAL_PUMPS * 8 / F_IN)
  63. tspan = np.arange(0, t_end, 0.01)
  65. equations_complex = Equations_complexSTD()
  66. equations_complex.update(f_in=F_IN)
  67. equations_complex.update(kappa=0.02)
  68. equations_complex.parameter_coupling_amplitude = param_amplitude
  70. # NO slow bits: parameter coupler = 0
  71. def parameter_coupler(t):
  72. return 0.0
  74. equations_complex.parameter_coupler = parameter_coupler
  76. # Fast bits only, switched on at starting_time, but only for num_pumps pumps.
  77. # After num_pumps * 8 bits, Vin is forced back to 0
  78. def Vin(t):
  79. if t < starting_time:
  80. return 0.0
  82. excited_duration = num_pumps * 8 / F_IN # time span with excitation
  84. if t < starting_time + excited_duration:
  85. # index into 8-bit fast pattern, repeated
  86. idx = int((t - starting_time) * F_IN) % len(fast_bits)
  87. return equations_complex.Vin_amplitude * fast_bits[idx]
  88. else:
  89. # remaining pumps: no excitation
  90. return 0.0
  92. equations_complex.Vin = Vin
  94. # >>> SAME INITIAL CONDITION EACH RUN (COPY SO THREADS DON'T SHARE MUTABLE STATE)
  95. y_complex_init = y_complex_init_global.copy()
  97. tt, yy = ode45_numpy(equations_complex, tspan, y_complex_init,
  98. Tol=1e-7, show_process=DISPLAY)
  100. # record the actual square wave that was fed
  101. square_wave = np.array([Vin(t) for t in tt], dtype=float)
  103. return tt, yy, square_wave
  106. def plot_time_series(img_name, tt, yy, square_wave, start_time=20000):
  107. # show a window of 16 fast bits before start_time and everything after
  108. bit_interval = 1 / F_IN
  109. indices = (tt >= start_time - 16 * bit_interval)
  110. _tt, yy, square_wave = tt[indices], yy[indices], square_wave[indices]
  112. plt.figure(figsize=(10, 6))
  113. plt.plot(_tt, np.real(yy[:, 1]), label='Output Signal (Re(y1))', alpha=0.6)
  114. plt.plot(_tt, square_wave, label='Input Signal (fast bits)', alpha=0.9)
  116. # draw vertical lines at the start of each fast bit
  117. for i in range(int((_tt[-1] - start_time) / bit_interval) + 1):
  118. x = start_time + i * bit_interval
  119. if x < _tt[0] or x > _tt[-1]:
  120. continue
  121. # extra-thick line every 8 bits (one 8-bit word)
  122. if i % 8 == 0:
  123. plt.axvline(x=x, color='black', linestyle='--', alpha=0.9, linewidth=2)
  124. else:
  125. plt.axvline(x=x, color='gray', linestyle='--', alpha=0.9)
  127. plt.xlabel('Time')
  128. plt.ylabel('Amplitude')
  129. plt.legend()
  130. plt.tight_layout()
  131. plt.savefig(img_name)
  132. plt.close()
  135. def run_and_save_trials(fast_bits, param_amplitude, starting_time, num_pumps, trial_id):
  136. tt, yy, square_wave = run_sim_measure_starting(
  137. fast_bits=fast_bits,
  138. param_amplitude=param_amplitude,
  139. starting_time=starting_time,
  140. num_pumps=num_pumps
  141. )
  143. print_name = os.path.join(
  144. RESULTS_DIR,
  145. f'sim_{int(F_IN * 10)}_start_{starting_time:.4f}_trial_{trial_id:03d}'
  146. )
  148. # plot time series for this trial
  149. plot_time_series(print_name + '.png', tt, yy, square_wave, start_time=starting_time)
  151. # save only the last PRE_WINDOW time units before the end (centered near starting_time+tail)
  152. indices = tt >= (starting_time - PRE_WINDOW)
  153. np.savez(
  154. print_name + '.npz',
  155. tt=tt[indices].astype(np.float32),
  156. yy=yy[indices].astype(np.complex64),
  157. fast_bits=fast_bits.astype(np.int8),
  158. square_wave=square_wave[indices].astype(np.float32),
  159. starting_time=np.float64(starting_time),
  160. num_pumps=np.int16(num_pumps)
  161. )
  163. print(f"Saved {print_name}.npz and .png")
  166. # ================== SWEEP START TIMES OVER ONE PERIOD ==================
  168. inputs = []
  169. num_trials = NUM_SWEEPS # <<< only this controls how many points
  171. # NUM_SWEEPS start times: 20000 → 20000 + 0.78 (one oscillator period)
  172. all_starting_times = BASE_START + np.linspace(0.0, OSC_PERIOD, num_trials)
  174. for trial in range(num_trials):
  175. start_t = float(all_starting_times[trial])
  176. inputs.append((fast_bits, 10, start_t, NUM_PUMPS, trial))
  179. if __name__ == "__main__":
  180. MAX_PROCS = 40
  181. num_proc = min(MAX_PROCS, cpu_count())
  183. print(f"Using {num_proc} processes on this machine.")
  185. with Pool(processes=num_proc) as pool:
  186. list(
  187. tqdm(
  188. pool.starmap(run_and_save_trials, inputs),
  189. total=len(inputs),
  190. desc="Sweeping start times"
  191. )
  192. )

Run_CMT_model_for_data_tokens.py, under CC-BY-4.0 · at the source

Overview

Authors: Bala Govind1,2, Pablo Raigoza3, Alyssa Apsel1
ORCID iDs: Bala Govind, Alyssa Apsel
  1. School of Electrical and Computer Engineering, Cornell University, Ithaca, NY USA
  2. Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, NY USA
  3. College of Computing and Information Science, Cornell University, Ithaca, NY USA
Institutions: Cornell University (United States)
Journal: Nature communications, volume 17, issue 1, article 9252
Dates: received 9 February 2026; accepted 3 July 2026; published online 29 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-75783-2 · PMID 42669674 · PMCID PMC13527087 · OpenAlex W7171720173
Open access: gold, a free copy (OpenAlex)
Status: code verified
Methods: Machine learning, Connectivity, Spectral & time-frequency, fMRI & imaging
Keywords: Electrical and electronic engineering, Computational science, Computational nanotechnology, Electronic and spintronic devices
Topic: Wireless Signal Modulation Classification (Artificial Intelligence, Computer Science), according to OpenAlex
Funding: United States Department of Defense | Defense Advanced Research Projects Agency (DARPA) (FA8650-21-C-7007); National Science Foundation (NSF) (NNCI-2025233)
Citations: not cited yet (Europe PMC); 38 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

Zenodo 17906138

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 15 files
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (18 files), pandas (15 files), Matplotlib (12 files), PyTorch (11 files), SciPy (11 files), seaborn (11 files), scikit-learn (10 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
18 files
At the source: zenodo.org/records/17906138

bmurmann/ADC-survey

License: BSD-3-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 00b4b855de5ce6bdf584a6aace354380022f1de4, 1 August 2026
Languages: Jupyter (5)
Size: 23 files, 5 scripts
Software Heritage: not archived
Found in: the references
Holds: README, license file, 5 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (5 files), NumPy (5 files), pandas (5 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
7 files
At the source: github.com/bmurmann/ADC-survey

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-75783-2.

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  • 7 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Read it in the paper: doi.org/10.1038/s41467-026-75783-2.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 4 keywords, 2 funders, 12 references.

Cite

This paper

Govind, B., Raigoza, P., & Apsel, A. (2026). Broadband encoding and high-speed probabilistic bit generation with integrated microwave neurons. Nature communications, 17(1), 9252. https://doi.org/10.1038/s41467-026-75783-2

BibTeX

@article{govind2026broadband,
author = {Govind, Bala and Raigoza, Pablo and Apsel, Alyssa},
title = {{Broadband encoding and high-speed probabilistic bit generation with integrated microwave neurons}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {9252},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-75783-2},
url = {https://doi.org/10.1038/s41467-026-75783-2},
pmid = {42669674},
pmcid = {PMC13527087}
}

RIS

TY - JOUR
AU - Govind, Bala
AU - Raigoza, Pablo
AU - Apsel, Alyssa
TI - Broadband encoding and high-speed probabilistic bit generation with integrated microwave neurons
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/29
VL - 17
IS - 1
SP - 9252
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-75783-2
UR - https://doi.org/10.1038/s41467-026-75783-2
LA - en
ER -

CSL-JSON

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