Behaviorist approaches to investigating memory and learning: A primer for synthetic biology and bioengineering

References

• Yokawa K, Derrien-Maze N, Mancuso S, et al. Binary Decisions in Maize Root Behavior: y-Maze System as Tool for Unconventional Computation in Plants. Int J Unconv Comput. 2014;10:381–390.
   Web of Science ®Google Scholar
• Reid CR, Beekman M, Latty T, et al. Amoeboid organism uses extracellular secretions to make smart foraging decisions. Behav Ecol. 2013;24(4):812–818.
   Web of Science ®Google Scholar
• Nakagaki T. Smart behavior of true slime mold in a labyrinth [Review]. Res Microbiol. 2001;152(9):767–770.
   PubMed Web of Science ®Google Scholar
• Vallverdu J, Castro O, Mayne R, et al. Slime mould: the fundamental mechanisms of biological cognition. Biosystems. 2018;165:57–70.
   PubMed Web of Science ®Google Scholar
• Vogel D, and Dussutour A. Direct transfer of learned behaviour via cell fusion in non-neural organisms. Proc R Soc B. 2016; 283:20162382.
   PubMed Web of Science ®Google Scholar
• Calvo P, and Friston K. Predicting green: really radical (plant) predictive processing. J R Soc Interface. 2017; 14:20170096.
   PubMed Web of Science ®Google Scholar
• Marder M. Plant intentionality and the phenomenological framework of plant intelligence. Plant Signal Behav. 2012;7(11):1365–1372.
   PubMedGoogle Scholar
• Garzon PC, Keijzer F. Plants: adaptive behavior, root-brains, and minimal cognition. Adapt Behav. 2011;19(3):155–171.
   Web of Science ®Google Scholar
• Cvrckova F, Lipavska H, Zarsky V. Plant intelligence: why, why not or where? Plant Signal Behav. 2009;4(5):394–399.
   PubMedGoogle Scholar
• Ford BJ. Cellular intelligence: microphenomenology and the realities of being. Prog Biophys Mol Biol. 2017;131:273–287.
   PubMed Web of Science ®Google Scholar
• Baluška F, Levin M. On having no head: cognition throughout biological systems. Front Psychol. 2016;7:902.
   PubMed Web of Science ®Google Scholar
• Calvo P, Baluska F. Conditions for minimal intelligence across eukaryota: a cognitive science perspective. Front Psychol. 2015;6:1329.
   PubMed Web of Science ®Google Scholar
• Trewavas AJ, Baluska F. The ubiquity of consciousness. EMBO Rep. 2011;12(12):1221–1225.
   PubMed Web of Science ®Google Scholar
• Baluska F, Lev-Yadun S, Mancuso S. Swarm intelligence in plant roots. Trends Ecol Evol. 2010;25(12):682–683.
   PubMed Web of Science ®Google Scholar
• Reid CR, MacDonald H, Mann RP, et al. Decision-making without a brain: how an amoeboid organism solves the two-armed bandit. J R Soc Interface. 2016;13(119):20160030.
   PubMed Web of Science ®Google Scholar
• Reid CR, Garnier S, Beekman M, et al. Information integration and multiattribute decision making in non-neuronal organisms. Animal Behav. 2015;100:44–50.
   Web of Science ®Google Scholar
• Latty T, Beekman M. Slime moulds use heuristics based on within-patch experience to decide when to leave. J Exp Biol. 2015;218(Pt 8):1175–1179.
   PubMed Web of Science ®Google Scholar
• Beekman M, Latty T. Brainless but multi-headed: decision making by the acellular slime mould physarum polycephalum. J Mol Biol. 2015;427(23):3734–3743.
   PubMed Web of Science ®Google Scholar
• Lyon P, Keijzer F, Arendt D, et al. Reframing cognition: getting down to biological basics. Philos Trans R Soc Lond B Biol Sci. 2021;376(1820):20190750.
   PubMed Web of Science ®Google Scholar
• Levin M, Keijzer F, Lyon P, et al. Uncovering cognitive similarities and differences, conservation and innovation. Philos Trans R Soc Lond B Biol Sci. 2021;376(1821):20200458.
   PubMed Web of Science ®Google Scholar
• Lyon P. The cognitive cell: bacterial behavior reconsidered. Front Microbiol. 2015;6:264.
   PubMedGoogle Scholar
• Keijzer F, van Duijn M, Lyon P. What nervous systems do: early evolution, input-output, and the skin brain thesis. Adapt Behav. 2013;21(2):67–85.
   Web of Science ®Google Scholar
• Lyon P. The biogenic approach to cognition. Cogn Process. 2006;7(1):11–29.
   PubMedGoogle Scholar
• Rahwan I, Cebrian M, Obradovich N, et al. Machine behaviour. Nature. 2019;568(7753):477–486.
   PubMed Web of Science ®Google Scholar
• Kamm RD, Bashir R, Arora N, et al. Perspective: the promise of multi-cellular engineered living systems. Apl Bioeng. 2018;2(4):040901.
   PubMed Web of Science ®Google Scholar
• Kamm RD, Bashir R. Creating living cellular machines. Ann Biomed Eng. 2014;42(2):445–459.
   PubMed Web of Science ®Google Scholar
• Ebrahimkhani MR, Levin M. Synthetic living machines: a new window on life. iScience. 2021;24(5):102505.
   PubMed Web of Science ®Google Scholar
• Newman JP, Zeller-Townson R, Fong MF, et al. Closed-Loop, multichannel experimentation using the open-source neurorighter electrophysiology platform. Front Neural Circuits. 2012;6:98.
   PubMed Web of Science ®Google Scholar
• Bakkum DJ, Gamblen PM, Ben-Ary G, et al. MEART: the semi-living artist. Front Neurorobot. 2007;1:5.
   PubMedGoogle Scholar
• Bakkum DJ, Chao ZC, and Gamblen P, et al. Embodying cultured networks with a robotic drawing arm. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Conference, Lyon, France. 2007; 2007:2996–2999.
   Google Scholar
• Pishvar M, Harne RL. Foundations for soft, smart matter by active mechanical metamaterials. Adv Sci (Weinh). 2020;7(18):2001384.
   PubMedGoogle Scholar
• Shah D, Yang BL, and Kriegman S, et al. Shape changing robots: bioinspiration, simulation, and physical realization. Adv Mater. 2020; 2002882.
   Web of Science ®Google Scholar
• McGivern P, Kirchhoff MD, Friston KJ. Active materials: minimal models of cognition? Adapt Behav. 2019;28(4):225–239.
   PubMedGoogle Scholar
• Bernheim-Groswasser A, Gov NS, Safran SA, et al. Living matter: mesoscopic active materials. Adv Mater. 2018;30(41):e1707028.
   PubMed Web of Science ®Google Scholar
• Adamatzky A, Chiolerio A, Szacilowski K. Liquid metal droplet solves maze. Soft Matter. 2020;16(6):1455–1462.
   PubMed Web of Science ®Google Scholar
• Kriegman S, Nasab AM, and Shah D, et al. Scalable sim-to-real transfer of soft robot designs. 2020 3rd Ieee International Conference on Soft Robotics (Robosoft), New Haven, CT, USA. 2020:359–366.
   Google Scholar
• Sole R. The major synthetic evolutionary transitions. Philos Trans R Soc Lond B Biol Sci. 2016; 371:20160175.
   PubMed Web of Science ®Google Scholar
• Sole R. Synthetic transitions: towards a new synthesis. Philos Trans R Soc Lond B Biol Sci. 2016; 371:20150438.
   PubMed Web of Science ®Google Scholar
• Cheney N, Bongard JC, and Lipson H. Evolving soft robots in tight spaces. Gecco’15: Proceedings of the 2015 Genetic and Evolutionary Computation Conference, Madrid, Spain. 2015:935–942.
   Google Scholar
• Rieffel J, Knox D, Smith S, et al. Growing and evolving soft robots. Artif Life. 2014;20(1):143–162.
   PubMed Web of Science ®Google Scholar
• Doursat R, Sayama H, Michel O. A review of morphogenetic engineering. Nat Comput. 2013;12(4):517–535.
   Web of Science ®Google Scholar
• Blackiston D, Lederer E, Kriegman S, et al. A cellular platform for the development of synthetic living machines. Sci Rob. 2021;6(52):eab f1571.
   Web of Science ®Google Scholar
• Kriegman S, Blackiston D, Levin M, et al. A scalable pipeline for designing reconfigurable organisms. Proc Natl Acad Sci U S A. 2020;117(4):1853–1859.
   PubMed Web of Science ®Google Scholar
• Rosenblueth A, Wiener N, Bigelow J. Behavior, Purpose and Teleology. Philos Sci. 1943;10(1):18–24.
   Google Scholar
• Calvo P, Sahi VP, Trewavas A. Are plants sentient? Plant Cell Environ. 2017;40(11):2858–2869.
   PubMed Web of Science ®Google Scholar
• Nilsonne G, Appelgren A, Axelsson J, et al. Learning in a simple biological system: a pilot study of classical conditioning of human macrophages in vitro. Behav Brain Funct. 2011;7(1):47.
   PubMedGoogle Scholar
• Teuscher C. Cellular computing. In: Meyers RA, editor. Encyclopedia of complexity and systems science. New York: Springer New York; 2009. p. 922–936.
   Google Scholar
• Whissell C, Abramson CI, Barber KR. The search for cognitive terminology: an analysis of comparative psychology journal titles. Behav Sci (Basel). 2013;3(1):133–142.
   PubMedGoogle Scholar
• Abramson CI. Problems of teaching the behaviorist perspective in the cognitive revolution. Behav Sci (Basel). 2013;3(1):55–71.
   PubMedGoogle Scholar
• Zentall TR. A cognitive behaviorist approach to the study of animal behavior. J Gen Psychol. 2002;129(4):328–363.
   PubMed Web of Science ®Google Scholar
• Leigland S. On cognitivism and behaviorism. Am Psychol. 2000;55(2):273–274.
   PubMed Web of Science ®Google Scholar
• Bargh JA, Ferguson MJ. Beyond behaviorism: on the automaticity of higher mental processes. Psychol Bull. 2000;126(6):925–945.
   PubMed Web of Science ®Google Scholar
• Rachlin H. Teleological behaviorism. Am Psychol. 1992;47(11):1371–1382.
   PubMed Web of Science ®Google Scholar
• Adams F. Why we still need a mark of the cognitive. Cogn Syst Res. 2010;11(4):324–331.
   Web of Science ®Google Scholar
• Adams F, Aizawa K. The bounds of cognition. Oxford (U.K.): Wiley-Blackwell; 2008.
   Google Scholar
• Cromwell HC, Panksepp J. Rethinking the cognitive revolution from a neural perspective: how overuse/misuse of the term ‘cognition’ and the neglect of affective controls in behavioral neuroscience could be delaying progress in understanding the BrainMind. Neurosci Biobehav R. 2011;35(9):2026–2035.
   PubMed Web of Science ®Google Scholar
• Overskeid G. They should have thought about the consequences: the crisis of cognitivism and a second chance for behavior analysis. Psychol Rec. 2008;58(1):131–151.
   Web of Science ®Google Scholar
• Abramson CI. A crisis in comparative psychology: where have all the undergraduates gone? Front Psychol. 2015;6:1500.
   PubMed Web of Science ®Google Scholar
• Abramson CI. A primer of invertebrate learning: the behavioral perspective. Washington (DC): American Psychological Association; 1994.
   Google Scholar
• Abramson CI. Where have I heard it all before: some neglected issues of invertebrate learning. In: Greenberg G, Tobach E, editors. Comparative psychology of invertebrates: the field and laboratory study of insect behavior. New York (NY): Garland Publishing; 1997. p. 55–78.
   Google Scholar
• Abramson CI, Chicas-Mosier AM. Learning in plants: lessons from mimosa pudica. Front Psychol. 2016;7:417.
   PubMed Web of Science ®Google Scholar
• Abramson CI, Calvo P. General issues in the cognitive analysis of plant learning and intelligence. In: Baluska F, Gagliano M, Witzany G, editors. Memory and learning in plants. New York (NY): Springer; 2018. p. 35–49.
   Google Scholar
• Abramson CI, Wells H. An inconvenient truth: some neglected issues in invertebrate learning. Perspect Behav Sci. 2018;41(2):395–416.
   PubMed Web of Science ®Google Scholar
• Logan FA. Incentive: how the conditions of reinforcement affect the performance of rats. New Haven (CT): Yale University Press; 1960.
   Google Scholar
• Perrotta MV, Asgeirsdottir T, Eagleman DM. Deciphering sounds through patterns of vibration on the skin. Neuroscience. 2021;458:77–86.
   PubMed Web of Science ®Google Scholar
• Bach-y-rita P. Sensory plasticity. Applications to a vision substitution system. Acta Neurol Scand. 1967;43(4):417–426.
   PubMed Web of Science ®Google Scholar
• Danilov Y, Tyler M. Brainport: an alternative input to the brain. J Integr Neurosci. 2005;4(4):537–550.
   PubMedGoogle Scholar
• Froese T, McGann M, Bigge W, et al. The enactive torch: a new tool for the science of perception. Ieee T Haptics. 2012;5(4):365–375.
   PubMed Web of Science ®Google Scholar
• Bitterman ME. Learning in animals. In: Helson H, Bevan W, editors. Contemporary approaches to psychology. Princeton (NJ): D. Van Nostrand and Co; 1967. p. 139–179.
   Google Scholar
• Abramson CI. Let us bring comparative psychology back. Int J Comp Psychol. 2018;31. https://escholarship.org/uc/item/81j662cd.
   Google Scholar
• Abramson CI, Feinman RD. Lever-press conditioning in the crab. Physiol Behav. 1990;48(2):267–272.
   PubMed Web of Science ®Google Scholar
• Amaya-Marquez M, Tusso S, Hernandez J, et al. Learning in the Stingless Bee Melipona eburnea Friese (Apidae: meliponini). Insects. 2019;10(11):412.
   Web of Science ®Google Scholar
• Rankin CH, Abrams T, Barry RJ, et al. Habituation revisited: an updated and revised description of the behavioral characteristics of habituation. Neurobiol Learn Mem. 2009;92(2):135–138.
   PubMed Web of Science ®Google Scholar
• Lyon P, Kuchling F. Valuing what happens: a biogenic approach to valence and (potentially) affect. Philos Trans R Soc Lond B Biol Sci. 2021;376(1820):20190752.
   PubMed Web of Science ®Google Scholar
• Oudeyer P-Y, Kaplan F. How can we define intrinsic motivation? 2013.
   Google Scholar
• Oudeyer PY, Kaplan F. What is intrinsic motivation? A Typology of computational approaches. Front Neurorobot. 2007;1:6.
   PubMedGoogle Scholar
• Abramson CI, Sokolowski MBC, Wells H. Issues in the study of proboscis conditioning. In: Columbus F, editor. Social insects: structure, function, and behavior. Hauppaug (NY): Nova Science Publishers; 2011. p. 25–49.
   Google Scholar
• Varnon CA, Abramson CI. The propeller experiment controller: low-cost automation for classroom experiments in learning and behavior. Comprehensive Psychol. 2013;2:2.
   Google Scholar
• Varnon CA, Abramson CI. The propeller experiment controller: automation for the comparative analysis of behavior in research and teaching. J Mind Behav. 2018;39:1–153.
   Google Scholar
• Vora HD, and Abramson CI. The use of 3D printing in comparative research and teaching. Int J Comp Psychol. 2020;33. https://escholarship.org/uc/item/0qm0k0h1.
   Google Scholar
• Grice JW. Observation oriented modeling: analysis of cause in the behavioral sciences. San Diego (CA): Academic Press; 2011.
   Google Scholar
• Grice JW, Barrett PT, Schlimgen LA, et al. Toward a brighter future for psychology as an observation oriented science. Behav Sci (Basel). 2012;2(1):1–22.
   PubMedGoogle Scholar
• Gormezano I, Kehoe EJ. Classical conditioning: some methodological-conceptual issues. In: Estes WK, editor. Handbook of learning and cognitive processes: vol 2 conditioning and behavior theory. Hillsdale (NJ): LEA; 1975. p. 143–179.
   Google Scholar
• Bitterman ME. Techniques for the study of learning in animals: analysis and classification. Psychol Bull. 1962;59(2):81–93.
   PubMed Web of Science ®Google Scholar
• Dyal JA, Corning WC. Invertebrate learning and behavior taxonomies. In: Corning WC, Dyal JA, Willows AOD, editors. Invertebrate learning, Vol 1 Protozoans through annelids. New York (NY): Plenum; 1973. p. 1–48.
   Google Scholar
• Woods PJ. A taxonomy of instrumental conditioning. Am Psychologist. 1974;29(8):584–597.
   Web of Science ®Google Scholar
• Ferster CB, Skinner BF. Schedules of reinforcement. Englewood Cliffs (NJ): Prentice-Hall; 1957.
   Google Scholar
• Smith BH, Abramson CI, Tobin TR. Conditional withholding of proboscis extension in honeybees (Apis mellifera) during discriminative punishment. J Comp Psychol. 1991;105(4):345–356.
   PubMed Web of Science ®Google Scholar
• Sigurdson JE. Measurement of consummatory behavior in honey bees. Behav Res Meth Instrum. 1981;13(3):308–310.
   Google Scholar
• Crain BJ, Giray T, Abramson CI. A tool for every job: assessing the need for a universal definition of tool use. Int J Comp Psychol. 2013;26(4):281–303.
   Google Scholar
• Ginsburg S, Jablonka E, Zeligowski A. The evolution of the sensitive soul: learning and the origins of consciousness. Cambridge (Mass): The MIT Press; 2019.
   Google Scholar
• Di Primio F, Muller BS, and Lengeler JW. Minimal cognition in unicellular organisms. In: Meyer J-A, Berthoz A, and Dario Floreano HL, et al., eds. SAB 2000 Sixth International Conference on Simulation of Adaptive Behavior: FROM ANIMALS TO ANIMATS, Paris, France, 2000.
   Google Scholar
• Turner CH, Robling AG, Duncan RL, et al. Do bone cells behave like a neuronal network? Calcif Tissue Int. 2002;70(6):435–442.
   PubMed Web of Science ®Google Scholar
• Chakravarthy SV, Ghosh J. On Hebbian-like adaptation in heart muscle: a proposal for ‘cardiac memory’. Biol Cybern. 1997;76(3):207–215.
   PubMed Web of Science ®Google Scholar
• Zoghi M. Cardiac memory: do the heart and the brain remember the same? J Interv Card Electrophysiol. 2004;11(3):177–182.
   PubMed Web of Science ®Google Scholar
• Dexter JP, Prabakaran S, Gunawardena J, et al. Hierarchy of Avoidance Behaviors in a Single-Cell Eukaryote. Curr Biol. 2019;29(24):4323–9 e2.
   PubMed Web of Science ®Google Scholar
• Katz Y. Embodying probabilistic inference in biochemical circuits. ArXiv 2018.
   Google Scholar
• Biswas S, Manicka S, Hoel E, et al. Gene regulatory networks exhibit several kinds of memory: quantification of memory in biological and random transcriptional networks. iScience. 2021;24(3):102131.
   PubMed Web of Science ®Google Scholar
• Tareen A, Kinney JB. Biophysical models of cis-regulation as interpretable neural networks. bioRxiv. 2019;835942.
   Google Scholar
• Biswas S, Acharyya S. Neural model of gene regulatory network: a survey on supportive meta-heuristics. Theor Biosci. 2016;135(1–2):1–19.
   PubMed Web of Science ®Google Scholar
• Santra N, Biswas S, and Acharyya S. Neural modeling of gene regulatory network using firefly algorithm. 2015 Ieee up Section Conference on Electrical Computer and Electronics (Upcon), Allahabad, India, 2015.
   Google Scholar
• Szabó Á, Vattay G, Kondor D. A cell signaling model as a trainable neural nanonetwork. Nano Commun Netw. 2012;3(1):57–64.
   Google Scholar
• Ajo-Franklin CM, Drubin DA, Eskin JA, et al. Rational design of memory in eukaryotic cells. Genes Dev. 2007;21(18):2271–2276.
   PubMed Web of Science ®Google Scholar
• Macia J, Vidiella B, and Sole RV. Synthetic associative learning in engineered multicellular consortia. J R Soc Interface. 2017; 14:20170158.
   PubMed Web of Science ®Google Scholar
• Urrios A, Macia J, Manzoni R, et al. A synthetic multicellular memory device. Acs Synth Biol. 2016;5(8):862–873.
   PubMed Web of Science ®Google Scholar
• Sole R, Amor DR, Duran-Nebreda S, et al. Synthetic collective intelligence. Biosystems. 2016;148:47–61.
   PubMed Web of Science ®Google Scholar
• Langille JJ, Gallistel CR. Locating the engram: should we look for plastic synapses or information-storing molecules? Neurobiol Learn Mem. 2020;169:107164.
   PubMed Web of Science ®Google Scholar
• Gallistel CR. The physical basis of memory. Cognition. 2020;213:104533.
   PubMed Web of Science ®Google Scholar
• Queenan BN, Ryan TJ, Gazzaniga MS, et al. On the research of time past: the hunt for the substrate of memory. Ann N Y Acad Sci. 2017;1396(1):108–125.
   PubMed Web of Science ®Google Scholar
• Pezzulo G, Levin M. Re-membering the body: applications of computational neuroscience to the top-down control of regeneration of limbs and other complex organs. Integr Biol (Camb). 2015;7(12):1487–1517.
   PubMed Web of Science ®Google Scholar
• Brofiga M, Pisano M, and Raiteri R, et al. On the road to the brain-on-a-chip: a review on strategies, methods, and applications. J Neural Eng. 2021; 181:041005.
   Google Scholar
• DeMarse TB, and Dockendorf KP. Adaptive flight control with living neuronal networks on microelectrode trays 2005 IEEE International Joint Conference on Neural Networks, Montreal, QC, Canada:1548–1551.
   Google Scholar
• Pandarinath C, Bensmaia SJ. The science and engineering behind sensitized brain-controlled bionic hands. Physiol Rev. 2021. DOI:https://doi.org/10.1152/physrev.00034.2020
   Google Scholar
• Potter SM, DeMarse TB, Bakkum DJ, et al. HYBROTS: HYBRIDS OF LIVING NEURONS AND ROBOTS FOR STUDYING NEURAL COMPUTATION. Stirling Scotland: Brain-inspired cognitive systems; 2004.
   Google Scholar
• Ay N, Lohr W. The Umwelt of an embodied agent–a measure-theoretic definition. Theory Biosci. 2015;134(3–4):105–116.
   PubMed Web of Science ®Google Scholar
• Salthe SN. Creating the Umwelt: from chance to choice. Biosemiotics-Neth. 2014;7(3):351–359.
   Web of Science ®Google Scholar
• Uexküll J, Uexküll J. A foray into the worlds of animals and humans with A theory of meaning. Posthumanities 12. Minneapolis: University of Minnesota Press; 2010. 1 online resource (272 p., 4 p.).
   Google Scholar