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Computational optimization of two-photon holographic stimulation sites<i>in vivo</i>.

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The authors' code

Jupyter notebook · 220 lines · 6.9 KB · GPL-3.0

  1. # %%
  2. import numpy as np
  3. import matplotlib.pyplot as plt
  4. import matplotlib
  5. from matplotlib import cm #
  6. from matplotlib.patches import Circle
  7. from matplotlib.colors import Normalize
  8. from matplotlib.cm import get_cmap, ScalarMappable
  9. np.random.seed(35)
  11. # %%
  12. import stim_opt as so
  13. from stim_opt import experimental_design
  15. # %%
  16. fontsize = 12
  17. pixel_size = 411/512
  19. # %% [markdown]
  20. # # Setup simulation and plot neuron locations
  22. # %%
  23. N = 350
  24. sim = so.Simulation(N)
  26. # %%
  27. plt.figure(figsize=(3, 3))
  28. for x, y in sim.neuron_locations:
  29. circ = Circle((x, y), radius=6/pixel_size, edgecolor='teal', facecolor='None')
  30. plt.gca().add_patch(circ)
  31. plt.xlim([0, 411])
  32. plt.ylim([0, 411])
  33. plt.xticks(np.arange(0, 401, 100), fontsize=fontsize)
  34. plt.yticks(np.arange(0, 401, 100), fontsize=fontsize)
  35. # plt.grid(linestyle='--')
  36. plt.xlabel('Distance (μm)', fontsize=fontsize)
  37. plt.ylabel('Distance (μm)', fontsize=fontsize)
  38. plt.show()
  40. # %% [markdown]
  41. # ## Visualize ORFs
  43. # %%
  44. vmax = 3.
  45. scalebar = 20 / pixel_size
  47. # %%
  48. nrows = 4
  49. ncols = 8
  50. panel_size = 1.5
  52. plt.figure(figsize=(ncols * panel_size, nrows * panel_size))
  53. for n in range(nrows * ncols):
  54. grid, X1, X2, extent = so.nbfr.generate_grid(sim.neurons[n].location, grid_width=30)
  55. Z = sim.neurons[n].predict(grid).reshape(100, 100)
  56. vmax = np.max(Z)
  58. plt.subplot(nrows, ncols, n + 1)
  59. plt.imshow(Z, interpolation='nearest', aspect='auto', cmap='coolwarm', vmin=-vmax, vmax=vmax, extent=extent, origin='lower')
  60. plt.contour(X1, X2, Z, levels=5, colors='black', linewidths=0.5, extent=extent) # Add contour lines
  61. plt.gca().add_patch(Circle((sim.neurons[n].location), radius=6/pixel_size, edgecolor='teal', facecolor='None', zorder=100, lw=3, ls='-'))
  62. plt.xticks([])
  63. plt.yticks([])
  64. if n == 0:
  65. plt.plot([sim.neurons[n].location[0], sim.neurons[n].location[0]+ scalebar], [sim.neurons[n].location[1] - 25, sim.neurons[n].location[1] - 25], lw=2, color='k')
  66. for spine in plt.gca().spines.values():
  67. spine.set_linewidth(1.5)
  68. plt.show()
  70. # %% [markdown]
  71. # # Map ORFs from an artificial exeriment
  73. # %%
  74. stim_size = 10
  75. stim_powers = [1.]
  76. noise_std = 0.5
  78. # %%
  79. %%time
  81. stim_design = experimental_design.generate_stimuli(sim.neuron_locations, pixel_size=pixel_size, stim_size=stim_size, stim_powers=stim_powers)[:, :2].reshape(-1, stim_size, 2)
  83. # %%
  84. %%time
  86. stim_responses = sim.population_response(stim_design)
  87. noise = np.random.normal(0, noise_std, stim_responses.shape)
  88. models = so.Models(sim.neuron_locations)
  89. models.fit(stim_design, stim_responses + noise)
  91. # %% [markdown]
  92. # # Optimize stimulation pattern
  94. # %%
  95. target_neurons = np.array([261, 197, 43])
  97. nontarget_neurons = np.setdiff1d(np.arange(N), target_neurons)
  98. stim_opt, stim_hist, objs = so.nbfr.optimize_stimulus(models, target_neurons, save_history=True, stepsize=5e0, n_iters=30)
  99. stim_som = sim.neuron_locations[target_neurons]
  101. # %%
  102. plt.figure(figsize=(2, 2))
  103. plt.plot(objs)
  104. plt.xlabel('Iterations')
  105. plt.xticks(np.arange(0, 31, 10))
  106. plt.ylabel('Objective function')
  107. plt.show()
  109. # %% [markdown]
  110. # # Visualize results
  112. # %%
  113. population_response_opt = sim.population_response(stim_opt[np.newaxis]).squeeze()
  114. population_response_som = sim.population_response(stim_som[np.newaxis]).squeeze()
  116. # %%
  117. fov_center = [160, 80]
  118. fov_width = 90
  119. sample_fov_extent = [fov_center[0] - fov_width/2, fov_center[0] + fov_width/2, fov_center[1] - fov_width/2, fov_center[1] + fov_width/2]
  121. vmax = np.max([np.max(population_response_opt), np.max(population_response_som)])
  122. vmin = -vmax
  123. norm = Normalize(vmin=vmin, vmax=vmax)
  124. cmap_response = matplotlib.colormaps.get_cmap('coolwarm') # for stim responses
  125. cmap = matplotlib.colormaps.get_cmap('YlOrRd') # for stim opt path
  127. # %%
  128. plt.figure(figsize=(2, 2))
  129. for (x, y), response in zip(sim.neuron_locations, population_response_som):
  130. color = cmap_response(norm(response))
  131. circ = Circle((x, y), radius=6/pixel_size, edgecolor='k', facecolor=color)
  132. plt.gca().add_patch(circ)
  134. plt.xlim(sample_fov_extent[:2])
  135. plt.ylim(sample_fov_extent[2:])
  136. plt.xticks(np.arange(120, 201, 20), fontsize=fontsize)
  137. plt.yticks(np.arange(40, 121, 20), fontsize=fontsize)
  138. plt.xlabel('Distance (μm)', fontsize=fontsize)
  139. plt.ylabel('Distance (μm)', fontsize=fontsize)
  140. plt.title('Somatic stimulation')
  141. plt.show()
  143. # %%
  144. plt.figure(figsize=(2, 2))
  145. for (x, y), response in zip(sim.neuron_locations, population_response_opt):
  146. color = cmap_response(norm(response))
  147. circ = Circle((x, y), radius=6/pixel_size, edgecolor='k', facecolor=color)
  148. plt.gca().add_patch(circ)
  150. # Progressive color for stim_hist
  151. for j in range(3):
  152. N_steps = stim_hist.shape[0]
  153. for t in range(N_steps - 1):
  154. frac = t / (N_steps - 1) # normalize to 0–1
  155. color = cmap(frac)
  156. x0, y0 = stim_hist[t, j]
  157. x1, y1 = stim_hist[t+1, j]
  158. plt.plot([x0, x1], [y0, y1], '-', color=color, zorder=1000, lw=2.5)
  159. plt.plot(x0, y0, 'o', color=color, markersize=3, markerfacecolor='w', zorder=1000)
  161. plt.xlim(sample_fov_extent[:2])
  162. plt.ylim(sample_fov_extent[2:])
  163. plt.xticks(np.arange(120, 201, 20), fontsize=fontsize)
  164. plt.yticks(fontsize=fontsize)
  165. plt.xlabel('Distance (μm)', fontsize=fontsize)
  166. plt.ylabel('Distance (μm)', fontsize=fontsize)
  167. plt.title('Optimized')
  168. plt.show()
  170. # %% [markdown]
  171. # # Loss surface
  173. # %%
  174. xsteps = np.arange(sample_fov_extent[0], sample_fov_extent[1] + 0.1, 1)
  175. ysteps = np.arange(sample_fov_extent[2], sample_fov_extent[3] + 0.1, 1)
  176. xs, ys = np.meshgrid(xsteps, ysteps)
  177. sample_grid = np.c_[xs.flatten(), ys.flatten()]
  178. D = xs.shape[0]
  180. # %%
  181. # Difference between target and non-target objective functions
  182. sum_orf = np.sum([sim.neurons[n].predict(sample_grid).reshape(D, D) for n in target_neurons], 0) \
  183. - np.sum([sim.neurons[n].predict(sample_grid).reshape(D, D) for n in nontarget_neurons], 0)
  185. # %%
  186. plt.figure(figsize=(2, 2))
  187. n = 0
  189. im = plt.imshow(sum_orf, origin='lower', cmap='coolwarm', vmin=-vmax, vmax=vmax, extent=sample_fov_extent)
  190. ax = plt.gca()
  192. # Progressive color for stim_hist
  193. for j in range(3):
  194. N_steps = stim_hist.shape[0]
  195. for t in range(N_steps - 1):
  196. frac = t / (N_steps - 1) # normalize to 0–1
  197. color = cmap(frac)
  198. x0, y0 = stim_hist[t, j]
  199. x1, y1 = stim_hist[t+1, j]
  200. plt.plot([x0, x1], [y0, y1], '-', color=color, zorder=1000, mew=1)
  201. plt.plot(x0, y0, 'o', color=color, markersize=3, markerfacecolor='w', zorder=1000, mew=1)
  203. plt.xlim(sample_fov_extent[:2])
  204. plt.ylim(sample_fov_extent[2:])
  205. plt.xticks(np.arange(120, 201, 20), fontsize=fontsize)
  206. plt.yticks(fontsize=fontsize)
  207. plt.xlabel('Distance (μm)', fontsize=fontsize)
  208. plt.ylabel('Distance (μm)', fontsize=fontsize)
  210. for (x, y), response in zip(sim.neuron_locations, population_response_opt):
  211. color = cmap_response(norm(response))
  212. circ = Circle((x, y), radius=6/pixel_size, edgecolor='k', facecolor='None')
  213. plt.gca().add_patch(circ)
  214. n += 1
  216. cbar = plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
  217. cbar.ax.tick_params(labelsize=fontsize)
  218. cbar.set_label('Objective function', rotation=270, fontsize=fontsize, labelpad=12)
  220. plt.show()

example.ipynb at commit 6e5f7bb, under GPL-3.0 · at the source

Overview

Authors: Marcus A Triplett1,2,3,4, Edgar Bäumler5, Alex Prodan5, Rokas Stonis5, Darcy S Peterka1, Michael Häusser5,6, Liam Paninski1,2,3,4
ORCID iDs: Marcus A Triplett, Edgar Bäumler, Alex Prodan, Rokas Stonis, Darcy S Peterka, Michael Häusser, Liam Paninski
  1. Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States of America
  2. Grossman Center for the Statistics of Mind, Columbia University, New York, NY, United States of America
  3. Center for Theoretical Neuroscience, Columbia University, New York, NY, United States of America
  4. Department of Statistics, Columbia University, New York, NY, United States of America
  5. Wolfson Institute for Biomedical Research, University College London, London, United Kingdom
  6. School of Biomedical Sciences, The University of Hong Kong, Hong Kong Special Administrative Region of China, People’s Republic of China
Institutions: Mortimer B. Zuckerman Mind Brain Behavior Institute (United States); Columbia University (United States); University College London (United Kingdom); University of Hong Kong (Hong Kong SAR China)
Journal: Journal of neural engineering, volume 23, issue 2, article 026015
Dates: received 15 August 2025; accepted 23 February 2026; published online 25 March 2026; in print 1 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1088/1741-2552/ae4925 · PMID 41878953 · PMCID PMC13014349 · OpenAlex W7140320479
Open access: hybrid, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: optical imaging (calcium, voltage, 2-photon) (modality), mouse (organism)
Methods: Spectral & time-frequency, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: computational neuroscience, holographic optogenetics, neural stimulation, calcium imaging
MeSH: Hippocampus*, Holography*, Models, Neurological*, Neurons*, Optogenetics*, Photic Stimulation*, Animals, Computer Simulation, Mice, Photons (* major topic)
Topic: Photoreceptor and optogenetics research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: NEI NIH HHS (R01 EY033950); Wellcome Trust (224688, 201225, 695709, PRF 201225); Engineering and Physical Sciences Research Council (EP/R513143/1 and EP/W524335/1); Gatsby Charitable Foundation (GAT3708); Medical Research Council (MR/T022922/1); Kavli Foundation; European Research Council (695709, AdG 695709); National Science Foundation (PHY-2229929); Biotechnology and Biological Sciences Research Council (BB/N009835/1); NINDS NIH HHS (K99 NS135649); Boehringer Ingelheim Fonds; NIH (K99NS135649); Zuckerman Institute Team Science Funds
Citations: cited by 2 papers (Europe PMC); 44 references in the paper

Abstract

Objective. Determining the intricate structure and function of neural circuits requires the ability to precisely manipulate circuit activity. Two-photon holographic optogenetics has emerged as a powerful tool for achieving this via flexible excitation of user-defined neural ensembles. However, the precision of two-photon optogenetics has been constrained by off-target stimulation (OTS), an effect where proximal non-target neurons can be unintentionally activated due to imperfect spatial confinement of light onto target neurons. New approaches are therefore needed to resolve the OTS problem. Approach. Here, we introduce a real-time computational method for mitigating OTS that first empirically samples each neuron’s sensitivity to stimulation at proximal locations, and then optimizes stimulation sites using a fast, interpretable model based on adaptive non-negative basis function regression (NBFR). Main results. NBFR is highly scalable, completing model fitting for hundreds of neurons in just a few seconds and then optimizing stimulation sites in several hundred milliseconds per stimulus—fast enough for most closed-loop behavioral experiments. We characterize the performance of our approach in both simulations and in vivo experiments in mouse hippocampus, showing its efficacy under realistic experimental conditions. Significance. Our results thus establish NBFR-based photostimulus optimization as an important addition to an emerging computational toolkit for precise yet scalable holographic optogenetics.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

marcustriplett/photostim-optimizer

License: GPL-3.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 6e5f7bba8f92135de519fd83ab4ec6b04097fe2e, 15 August 2025
Languages: Python (6), Jupyter (1)
Size: 9 files, 7 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (setup.py), 1 notebook
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (6 files), scikit-learn (4 files), Matplotlib (3 files), SciPy (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
9 files
At the source: github.com/marcustriplett/photostim-optimizer

Code availability

Code for this manuscript is available at https://github.com/marcustriplett/photostim-optimizer.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

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Data availability statement

The data that support the findings of this study are openly available at the following URL/DOI: https://doi.org/10.6084/m9.figshare.29923262.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 4 keywords, 10 MeSH terms, 13 funders, 40 references.

Cite

This paper

Triplett, M. A., Bäumler, E., Prodan, A., Stonis, R., Peterka, D. S., Häusser, M., & Paninski, L. (2026). Computational optimization of two-photon holographic stimulation sites<i>in vivo</i>. Journal of neural engineering, 23(2), 026015. https://doi.org/10.1088/1741-2552/ae4925

BibTeX

@article{triplett2026computational,
author = {Triplett, Marcus A and Bäumler, Edgar and Prodan, Alex and Stonis, Rokas and Peterka, Darcy S and Häusser, Michael and Paninski, Liam},
title = {{Computational optimization of two-photon holographic stimulation sites\<i\>in vivo\</i\>}},
journal = {Journal of neural engineering},
year = {2026},
month = mar,
volume = {23},
number = {2},
pages = {026015},
publisher = {IOP Publishing},
issn = {1741-2560},
doi = {10.1088/1741-2552/ae4925},
url = {https://doi.org/10.1088/1741-2552/ae4925},
pmid = {41878953},
pmcid = {PMC13014349}
}

RIS

TY - JOUR
AU - Triplett, Marcus A
AU - Bäumler, Edgar
AU - Prodan, Alex
AU - Stonis, Rokas
AU - Peterka, Darcy S
AU - Häusser, Michael
AU - Paninski, Liam
TI - Computational optimization of two-photon holographic stimulation sites<i>in vivo</i>
T2 - Journal of neural engineering
J2 - J Neural Eng
PY - 2026
DA - 2026/03/25
VL - 23
IS - 2
SP - 026015
SN - 1741-2560
PB - IOP Publishing
DO - 10.1088/1741-2552/ae4925
UR - https://doi.org/10.1088/1741-2552/ae4925
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Computational optimization of two-photon holographic stimulation sites<i>in vivo</i>",
"container-title": "Journal of neural engineering",
"author": [
{
"family": "Triplett",
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],
"container-title-short": "J Neural Eng",
"volume": "23",
"issue": "2",
"page": "026015",
"DOI": "10.1088/1741-2552/ae4925",
"PMID": "41878953",
"PMCID": "PMC13014349",
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"URL": "https://doi.org/10.1088/1741-2552/ae4925",
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]
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}

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