Treffer: An open-source behavior controller for associative learning and memory (B-CALM).
Title:
An open-source behavior controller for associative learning and memory (B-CALM).
Authors:
Zhou M; Department of Neurology, University of California, San Francisco, CA, USA.; Neuroscience Graduate Program, University of California, San Francisco, CA, USA., Wu B; Department of Neurology, University of California, San Francisco, CA, USA., Jeong H; Department of Neurology, University of California, San Francisco, CA, USA., Burke DA; Department of Neurology, University of California, San Francisco, CA, USA., Namboodiri VMK; Department of Neurology, University of California, San Francisco, CA, USA. VijayMohan.KNamboodiri@ucsf.edu.; Neuroscience Graduate Program, University of California, San Francisco, CA, USA. VijayMohan.KNamboodiri@ucsf.edu.; Weill Institute for Neuroscience, Kavli Institute for Fundamental Neuroscience, Center for Integrative Neuroscience, University of California, San Francisco, CA, USA. VijayMohan.KNamboodiri@ucsf.edu.
Source:
Behavior research methods [Behav Res Methods] 2024 Apr; Vol. 56 (4), pp. 2695-2710. Date of Electronic Publication: 2023 Jul 18.
Publication Type:
Journal Article; Research Support, Non-U.S. Gov't; Research Support, N.I.H., Extramural
Language:
English
Journal Info:
Publisher: Springer Country of Publication: United States NLM ID: 101244316 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1554-3528 (Electronic) Linking ISSN: 1554351X NLM ISO Abbreviation: Behav Res Methods Subsets: MEDLINE
Imprint Name(s):
Publication: 2010- : New York : Springer
Original Publication: Austin, Tex. : Psychonomic Society, c2005-
Original Publication: Austin, Tex. : Psychonomic Society, c2005-
MeSH Terms:
References:
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Burke, D. A., Jeong, H., Wu, B., Lee, S. A., Floeder, J. R., & Namboodiri, V. M. K. (2023). Few-shot learning: Temporal scaling in behavioral and dopaminergic learning. bioRxiv. https://doi.org/10.1101/2023.03.31.535173.
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K Namboodiri VM, Hobbs T, Trujillo-Pisanty I, Simon RC, Gray MM, Stuber GD (2021) Relative salience signaling within a thalamo-orbitofrontal circuit governs learning rate. Current Biology 31:5176-5191.e5.
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Namboodiri VMK, Otis JM, Heeswijk K van, Voets ES, Alghorazi RA, Rodriguez-Romaguera J, Mihalas S, Stuber GD (2019) Single-cell activity tracking reveals that orbitofrontal neurons acquire and maintain a long-term memory to guide behavioral adaptation. Nature Neuroscience 22:1110.
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Richard, J. M., Ambroggi, F., Janak, P. H., & Fields, H. L. (2016). Ventral pallidum neurons encode incentive value and promote Cue-elicited instrumental actions. Neuron, 90, 1165–1173. (PMID: 10.1016/j.neuron.2016.04.037272388684911300)
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Schultz, B. G., & van Vugt, F. T. (2016). Tap Arduino: An Arduino microcontroller for low-latency auditory feedback in sensorimotor synchronization experiments. Behavior Research Methods, 48, 1591–1607. (PMID: 10.3758/s13428-015-0671-326542971)
Slotnick, B. (2009). A simple 2-transistor touch or lick detector circuit. Journal of the Experimental Analysis of Behavior, 91, 253–255. (PMID: 10.1901/jeab.2009.91-253197948372648519)
Solari, N., Sviatkó, K., Laszlovszky, T., Hegedüs, P., & Hangya, B. (2018). Open source tools for temporally controlled rodent behavior suitable for electrophysiology and Optogenetic manipulations. Frontiers in Systems Neuroscience, 12, 18. (PMID: 10.3389/fnsys.2018.00018298673835962774)
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Belsey, P. P., Nicholas, M. A., & Yttri, E. A. (2020). Open-source joystick Manipulandum for decision-making, reaching, and motor control studies in mice. eNeuro, 7.
Brunton, B. W., Botvinick, M. M., & Brody, C. D. (2013). Rats and humans can optimally accumulate evidence for decision-making. Science, 340, 95–98. (PMID: 10.1126/science.123391223559254)
Burgess, C. P., Lak, A., Steinmetz, N. A., Zatka-Haas, P., Bai Reddy, C., Jacobs, E. A. K., Linden, J. F., Paton, J. J., Ranson, A., Schröder, S., Soares, S., Wells, M. J., Wool, L. E., Harris, K. D., & Carandini, M. (2017). High-yield methods for accurate two-alternative visual psychophysics in head-fixed mice. Cell Reports, 20, 2513–2524. (PMID: 10.1016/j.celrep.2017.08.04728877482)
Burke, D. A., Jeong, H., Wu, B., Lee, S. A., Floeder, J. R., & Namboodiri, V. M. K. (2023). Few-shot learning: Temporal scaling in behavioral and dopaminergic learning. bioRxiv. https://doi.org/10.1101/2023.03.31.535173.
Chung, S.-H., & Herrnstein, R. J. (1967). Choice and delay of reinforcement. Journal of the Experimental Analysis of Behavior, 10, 67–74. (PMID: 10.1901/jeab.1967.10-67168113071338319)
Crouse, R. B., Kim, K., Batchelor, H. M., Girardi, E. M., Kamaletdinova, R., Chan, J., Rajebhosale, P., Pittenger, S. T., Role, L. W., Talmage, D. A., Jing, M., Li, Y., Gao, X.-B., Mineur, Y. S., & Picciotto, M. R. (2020). Acetylcholine is released in the basolateral amygdala in response to predictors of reward and enhances the learning of cue-reward contingency. eLife, 9, e57335.
D’Ausilio, A. (2012). Arduino: A low-cost multipurpose lab equipment. Behavior Research Methods, 44, 305–313. (PMID: 10.3758/s13428-011-0163-z22037977)
Elliott, C., Vijayakumar, V., Zink, W., & Hansen, R. (2007). National Instruments LabVIEW: A programming environment for laboratory automation and measurement. JALA. Journal of the Association for Laboratory Automation, 12, 17–24. (PMID: 10.1016/j.jala.2006.07.012)
Fanselow, M. S., & Wassum, K. M. (2016). The origins and Organization of Vertebrate Pavlovian Conditioning. Cold Spring Harbor Perspectives in Biology, 8, a021717. (PMID: 10.1101/cshperspect.a0217174691796)
Ghazizadeh, A., Ambroggi, F., Odean, N., & Fields, H. L. (2012). Prefrontal cortex mediates extinction of responding by two distinct neural mechanisms in Accumbens Shell. The Journal of Neuroscience, 32, 726–737. (PMID: 10.1523/JNEUROSCI.3891-11.2012222381086621084)
Goldstein, R. Z., & Volkow, N. D. (2002). Drug addiction and its underlying neurobiological basis: Neuroimaging evidence for the involvement of the frontal cortex. The American Journal of Psychiatry, 159, 1642–1652. (PMID: 10.1176/appi.ajp.159.10.1642123596671201373)
Gordon-Fennell, A., Barbakh, J. M., Utley, M., Singh, S., Bazzino, P., Gowrishankar, R., Bruchas, M. R., Roitman, M. F., & Stuber, G. D. (2023). An open-source platform for head-fixed operant and Consummatory behavior. bioRxiv. https://doi.org/10.1101/2023.01.13.523828.
Herrnstein, R. J. (1961). Relative and absolute strength of response as a function of frequency of reinforcement. Journal of the Experimental Analysis of Behavior, 4, 267–272. (PMID: 10.1901/jeab.1961.4-267137137751404074)
Jaramillo, S., & Zador, A. M. (2011). The auditory cortex mediates the perceptual effects of acoustic temporal expectation. Nature Neuroscience, 14, 246–251. (PMID: 10.1038/nn.268821170056)
Jeong, H., Taylor, A., Floeder, J. R., Lohmann, M., Mihalas, S., Wu, B., Zhou, M., Burke, D. A., & Namboodiri, V. M. K. (2022). Mesolimbic dopamine release conveys causal associations. Science, 378, eabq6740. (PMID: 10.1126/science.abq6740364805999910357)
K Namboodiri VM, Hobbs T, Trujillo-Pisanty I, Simon RC, Gray MM, Stuber GD (2021) Relative salience signaling within a thalamo-orbitofrontal circuit governs learning rate. Current Biology 31:5176-5191.e5.
Namboodiri, K., & Stuber, G. D. (2021). The learning of prospective and retrospective cognitive maps within neural circuits. Neuron. 109, 22, 3552–3575.
Kim, K.-U., Huh, N., Jang, Y., Lee, D., & Jung, M. W. (2015). Effects of fictive reward on rat’s choice behavior. Scientific Reports, 5, 8040. (PMID: 10.1038/srep08040256239294894400)
Laguesse, S., Morisot, N., Shin, J. H., Liu, F., Adrover, M. F., Sakhai, S. A., Lopez, M. F., Phamluong, K., Griffin, W. C., Becker, H. C., Bender, K. J., Alvarez, V. A., & Ron, D. (2017). Prosapip1-dependent synaptic adaptations in the nucleus Accumbens drive alcohol intake, seeking, and reward. Neuron, 96, 145–159.e8. (PMID: 10.1016/j.neuron.2017.08.037288903456014831)
Livneh, Y., Ramesh, R. N., Burgess, C. R., Levandowski, K. M., Madara, J. C., Fenselau, H., Goldey, G. J., Diaz, V. E., Jikomes, N., Resch, J. M., Lowell, B. B., & Andermann, M. L. (2017). Homeostatic circuits selectively gate food cue responses in insular cortex. Nature, 546, 611–616. (PMID: 10.1038/nature22375286142995577930)
MacNiven, K. H., Jensen, E. L. S., Borg, N., Padula, C. B., Humphreys, K., & Knutson, B. (2018). Association of Neural Responses to drug cues with subsequent relapse to stimulant use. JAMA Network Open, 1, e186466. (PMID: 10.1001/jamanetworkopen.2018.6466306463316324538)
Matell, M. S., & Portugal, G. S. (2007). Impulsive responding on the peak-interval procedure. Behavioural Processes, 74, 198–208. (PMID: 10.1016/j.beproc.2006.08.00917023122)
Matikainen-Ankney, B. A., et al. (2021). An open-source device for measuring food intake and operant behavior in rodent home-cages. eLife, 10, e66173. (PMID: 10.7554/eLife.66173337795478075584)
Mohebi, A., Pettibone, J. R., Hamid, A. A., Wong, J.-M. T., Vinson, L. T., Patriarchi, T., Tian, L., Kennedy, R. T., & Berke, J. D. (2019). Dissociable dopamine dynamics for learning and motivation. Nature, 570, 65–70. (PMID: 10.1038/s41586-019-1235-y311185136555489)
Monroe, S. C., & Radke, A. K. (2021). Aversion-resistant fentanyl self-administration in mice. Psychopharmacology (Berl), 238, 699–710. (PMID: 10.1007/s00213-020-05722-633226446)
Namboodiri, V. M. K., Huertas, M. A., Monk, K. J., Shouval, H. Z., & Hussain Shuler, M. G. (2015). Visually cued action timing in the primary visual cortex. Neuron, 86, 319–330. (PMID: 10.1016/j.neuron.2015.02.043258196114393368)
Namboodiri VMK, Otis JM, Heeswijk K van, Voets ES, Alghorazi RA, Rodriguez-Romaguera J, Mihalas S, Stuber GD (2019) Single-cell activity tracking reveals that orbitofrontal neurons acquire and maintain a long-term memory to guide behavioral adaptation. Nature Neuroscience 22:1110.
Nikbakht, N., & Diamond, M. E. (2021). Conserved visual capacity of rats under red light. eLife, 10, e66429. (PMID: 10.7554/eLife.66429342827248360654)
Otis, J. M., Namboodiri, V. M. K., Matan, A. M., Voets, E. S., Mohorn, E. P., Kosyk, O., McHenry, J. A., Robinson, J. E., Resendez, S. L., Rossi, M. A., & Stuber, G. D. (2017). Prefrontal cortex output circuits guide reward seeking through divergent cue encoding. Nature, 543, 103–107. (PMID: 10.1038/nature21376282257525772935)
Pavlov, I. P. (1927). Conditioned reflexes: An investigation of the physiological activity of the cerebral cortex, conditioned reflexes: An investigation of the physiological activity of the cerebral cortex. Oxford, England: Oxford Univ. Press.
Perry, C. J., Zbukvic, I., Kim, J. H., & Lawrence, A. J. (2014). Role of cues and contexts on drug-seeking behaviour. British Journal of Pharmacology, 171, 4636–4672. (PMID: 10.1111/bph.12735247499414209936)
Poddar, R., Kawai, R., & Ölveczky, B. P. (2013). A fully automated high-throughput training system for rodents. PLoS One, 8, e83171. (PMID: 10.1371/journal.pone.0083171243494513857823)
Richard, J. M., Ambroggi, F., Janak, P. H., & Fields, H. L. (2016). Ventral pallidum neurons encode incentive value and promote Cue-elicited instrumental actions. Neuron, 90, 1165–1173. (PMID: 10.1016/j.neuron.2016.04.037272388684911300)
Roberts, S. (1981). Isolation of an internal clock. Journal of Experimental Psychology. Animal Behavior Processes, 7, 242–268. (PMID: 10.1037/0097-7403.7.3.2427252428)
Saunders, J. L., & Wehr, M. (2019). Autopilot: Automating behavioral experiments with lots of raspberry Pis. bioRxiv. https://doi.org/10.1101/807693.
Schultz, B. G., & van Vugt, F. T. (2016). Tap Arduino: An Arduino microcontroller for low-latency auditory feedback in sensorimotor synchronization experiments. Behavior Research Methods, 48, 1591–1607. (PMID: 10.3758/s13428-015-0671-326542971)
Slotnick, B. (2009). A simple 2-transistor touch or lick detector circuit. Journal of the Experimental Analysis of Behavior, 91, 253–255. (PMID: 10.1901/jeab.2009.91-253197948372648519)
Solari, N., Sviatkó, K., Laszlovszky, T., Hegedüs, P., & Hangya, B. (2018). Open source tools for temporally controlled rodent behavior suitable for electrophysiology and Optogenetic manipulations. Frontiers in Systems Neuroscience, 12, 18. (PMID: 10.3389/fnsys.2018.00018298673835962774)
Staddon, J. E. R., & Cerutti, D. T. (2003). Operant conditioning. Annual Review of Psychology, 54, 115–144. (PMID: 10.1146/annurev.psych.54.101601.14512412415075)
Swanson K, White SR, Preston MW, Wilson J, Mitchell M, Laubach M (2021) An Open Source Platform for Presenting Dynamic Visual Stimuli. eNeuro 8:ENEURO.0563-20.2021.
The International Brain Laboratory et al. (2021). Standardized and reproducible measurement of decision-making in mice. eLife, 10, e63711. (PMID: 10.7554/eLife.63711)
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Grant Information:
R00 MH118422 United States MH NIMH NIH HHS; R01 AA029661 United States AA NIAAA NIH HHS; R01 MH129582 United States MH NIMH NIH HHS
Contributed Indexing:
Keywords: Arduino microcontroller; Associative learning; Behavior controller; Choice task; Interval timing; Operant conditioning; Pavlovian conditioning
Entry Date(s):
Date Created: 20230718 Date Completed: 20240529 Latest Revision: 20250402
Update Code:
20250407
PubMed Central ID:
PMC10898869
DOI:
10.3758/s13428-023-02182-6
PMID:
37464151
Database:
MEDLINE
Weitere Informationen
Associative learning and memory, i.e., learning and remembering the associations between environmental stimuli, self-generated actions, and outcomes such as rewards or punishments, are critical for the well-being of animals. Hence, the neural mechanisms underlying these processes are extensively studied using behavioral tasks in laboratory animals. Traditionally, these tasks have been controlled using commercial hardware and software, which limits scalability and accessibility due to their cost. More recently, due to the revolution in microcontrollers or microcomputers, several general-purpose and open-source solutions have been advanced for controlling neuroscientific behavioral tasks. While these solutions have great strength due to their flexibility and general-purpose nature, for the same reasons, they suffer from some disadvantages including the need for considerable programming expertise, limited online visualization, or slower than optimal response latencies for any specific task. Here, to mitigate these concerns, we present an open-source behavior controller for associative learning and memory (B-CALM). B-CALM provides an integrated suite that can control a host of associative learning and memory behaviors. As proof of principle for its applicability, we show data from head-fixed mice learning Pavlovian conditioning, operant conditioning, discrimination learning, as well as a timing task and a choice task. These can be run directly from a user-friendly graphical user interface (GUI) written in MATLAB that controls many independently running Arduino Mega microcontrollers in parallel (one per behavior box). In sum, B-CALM will enable researchers to execute a wide variety of associative learning and memory tasks in a scalable, accurate, and user-friendly manner.
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