Abstract
Multiple realizability says that the same kind of mental states may be manifested by systems with very different physical constitutions. Putnam (1967) supposed it to be “overwhelmingly probable” that there exist psychological properties with different physical realizations in different creatures. But because function constrains possible physical realizers, this empirical bet is far less favorable than it might initially have seemed, especially when we take on board the richer picture of neural and brain function that neuroscience has been uncovering over the past forty years, in which all sorts of brain activities play crucial roles beyond all-or-nothing electrical signaling. Because of its evolutionary history, the brain’s metabolic and informational processes are inextricably intertwined. The resulting complex integrated functions impose more constraints on possible realizers than the clean, single-purpose functions usually cited as examples in discussions of multiple realizability—with ramifications for the functionalist and computationalist foundations of cognitive science, artificial intelligence, and the philosophy of mind.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
By contrast, if what is at issue is only the metaphysical question of how to analyze our mental terms, then perhaps nomological constraints can be left aside.
As such, it is part of a tradition starting from the pioneering neurophilosophy of the Churchlands to the naturalistic philosophy of mind and neuroscience of Bechtel, Bickle, Craver, Klein, Roskies, Sullivan and many others, especially the influential work of Polger and Shapiro—who, though clearly not all in agreement, remind us of the many reasons to think that the body and its brain do not just house the mind like a fleshly vat, but rather, constitute it, and that the many gory details are not just irrelevant biology, but profoundly relevant to the functioning of the system. Bickle (2010) provides a survey and critique of the earlier contributions to this debate.
Bechtel and Mundale contend that if we considered brain architectures at as coarse-grained a level as we casually speak of mental states like “pain”, we would find that the architectures had as much in common as the mental states, and therefore that they would constitute tokens of the same realization. At a finer grain of description, there are many differences between human versions of such components and those of the octopus—but Bechtel and Mundale’s claim is that at a finer grain of description there will also be differences between the functional profile that characterizes my pain and the one that characterizes that of the octopus.
See especially chapters 3 and 4 of Polger and Shapiro (2016) for careful developments of a number of related considerations that push them towards updated versions of a type-identity theory. They maintain that the burden of proof should be on defenders of multiple realizability, not its detractors. See Aizawa & Gillette (2009), and also Fang (2018) for counterpoint. There are also alternative characterizations of MR that allow for realizations that exhibit differences in functional organization to nevertheless count as alternative realizations of the same functional profile (e.g. Piccinini & Maley 2014). My argument is directed only at the canonical formulation from Putnam and Fodor inherited by mainstream philosophy of mind and much of cognitive science.
With the advent of neural network models and concomitant advances in artificial intelligence, it might seem that these considerations have already been found irrelevant. But in fact, the way in which those models succeed, when they do succeed, serves to underline the importance of the constraining relationship between functional demands and physical implementation.
Pylyshyn (1980) and others have introduced very similar thought experiments. Pylyshyn writes: “…if more and more of the cells in your brain were to be replaced by integrated circuit chips, programmed in such a way as to keep the input-output function of each unit identical to that of the unit being replaced, you would in all likelihood just keep right on speaking exactly as you are doing now …” I should also note that while Chalmers was concerned to make a point about conscious experience,I’m concerned here only with whether the functional organization of the brain ismultiply realizable, and not whether the organizational invariance thesis about conscious experience is true. (Everything in this section will be compatible with a thesis of organizational invariance, since invariance claims only that same organization entails same mental state, with no commitments about whether that functional organization can be realized by different physical systems).
It seems reasonable to generalize the idea of the “output” here to include internal changes, because when the unit changes its own state, it also changes its future dispositions to respond to input, and thus its input-output profile considered over a longer time interval.
To complicate things further, from the point of view of action potentials, the relevant functional unit may not be the whole neuron, but rather a single dendrite, or perhaps a group of them, which propagate local spikes resulting in local neurotransmitter release, but which rarely if ever have any effect on the cell body or axons. See London and Hausser (2005) and Mel (2007) for reviews.
Speed matters because behavior is partly individuated temporally, and the functional significance of many mental or psychological states relies on response speeds and the right kind of temporal coupling either to other processes or to the environment. For more on the role of speed, see Dennett’s Fast Thinking (1987).
When we consider the massive computational cost of simulating a single protein-protein interaction for less than a millisecond, it should give us pause about the plausibility of large-scale simulations of molecular dynamics over physiologically relevant periods of time. For a discussion of the constraints faced by such simulations, see Hollingsworth and Dror (2018). Computation may be universal in theory, but enough orders-of-magnitude differences between the simulation efficiency and the original can pile up to make some simulations nomologically impossible in the time and space available. “Quantum supremacy” might be thought of as an extreme example of the constraints imposed on the computational medium by a computational task.
“Why Philosophers Should Care About Computational Complexity”, Scott Aaronson (2011), Electronic Colloquium on Computational Complexity, Report No. 108.
Giving up that anchoring raises familiar triviality problems with functionalism—see e.g. the problem of inputs and outputs, Block (1978). Perhaps these troubles can be set aside, so long as the transduction mapping from the environment to input (and from output to physical behavior) is relatively straightforward.
The flow of ions across the cell membrane, via these channels, is what controls the propagation (or failure to propagate) of action potentials through neurons.
Marr (1982) mentions in passing that “some styles of algorithm will suit some physical substrates better than others. For example, in conventional digital computers, the number of connections is comparable to the number of gates, while in a brain, the number of connections is much larger (x 104) than the number of nerve cells. The underlying reason is that wires are rather cheap in biological architecture … [whereas] in conventional technology, wire laying is more or less restricted to two dimensions, which quite severely restricts the scope for using parallel techniques and algorithms; the same operations are often better carried out serially.” (p.24 Vision). As it turns out, wire is not that cheap even in biology – some of the brain’s larger-scale functional organization (e.g the layout of visual cortices) seem to result in part from minimizing wiring length.
Is this capacity for plasticity merely a second-order feature that doesn’t matter for the core case of say, perceiving or acting at an instant? Perhaps if we are only concerned with functional duplication at the scale of hundreds of milliseconds, we may ignore plasticity, which plays a more prominent role in learning and memory. However, even at shorter time scales, sculpting or self-modification processes may matter for phenomena such as adaptation, as well as for the stability and robustness of the system.
See Nedergaard et al (2006), Haydon and Carmignoto (2006), Halassa and Haydon (2010) for reviews of the functional roles of astrocytes in synaptic regulation, and Allen & Eroglu (2017) for a comprehensive review of astrocyte-neural interactions. See Angulo et al. (2004) and Perea & Araque (2007) for some suggestive results about the effects of astrocytic glutamate on neural activity in hippocampal slices. Astrocytes have more recently been shown to directly control behaviors in vivo including chemotaxis and startle responses in fruit flies. (Ma et al. 2016)
For an exploration of the implications of these influences, see Moore and Cao (2008).
See also Wimsatt (2006) on the shortcomings of interface determinism – assuming “that all that counts in analyzing the nature and behavior of a system is what comes or goes across the system-environment interface.”
See Sprevak (2018) for a thorough discussion of triviality arguments in this vein.
To say otherwise would be to argue for the existence of a kind of functional structure that could not in principle be denied, regardless of the actual physical organization of the components and how they interact. That would be to leave behind the crucial functionalist requirement that functional components are individuated by their causal roles within the organization of the system. I will come back to this in Sect. 4.
It is sometimes argued that paradigmatic mental processes such as problem solving must be multiply realizable because an answer is an answer—whether it is spoken or displayed on a screen or demonstrated in action. But this is to confuse the medium independence of the outputs of a process with the multiple realizability of the process itself. Furthermore, the outputs are only medium independent because we, the consumers of the outputs or answers, are able to easily translate them into the appropriate form to make sense of them in the context in which they are produced. So for example, we don’t care what form the outputs of the chess machine are (if they are a certain pattern of bright pixels in a display, or a voice saying “Knight to Q4”, or the physical movement of a chess piece), because we are capable of translating them into the form appropriate to the exercise (of playing chess) that is being undertaken.
This principle can be seen at work elsewhere in biology, for example, in the context of neutral processes in evolution: “Important proteins are more constrained and their amino acid changes are less likely to be neutral.” (Ohta 2001).
Introduced in Wimsatt (1986).
The brain’s grayish-pinkish hue is not one of its functional properties. But there is plausibly no collection of substrates, which together look green, which could also duplicate the exact functional properties of the brain, and so a green functional duplicate of the brain is not nomologically possible.
Dennett (1990). My contention is that the “thought experiment” proposed is one that must ignore nomological constraints such as chemistry-in-our-world. Although Dennett is often considered a behaviorist, his arguments lend a surprising amount of aid and comfort to this chauvinistic functionalist.
Of course, neurons also have guanosine-sensitive receptors, but those produce yet other modulatory effects, some but not all of which overlap with the effects of adenosine.
Krishnan and Nestler (2010) provide an accessible review (focusing on depression) of some of these factors.
Another term from Wimsatt (2007), the “causal thicket”, seems appropriate here. Causal thickets are where distinct levels and perspectives break down, so that no clear size scale or methodological foregrounding seems appropriate: “The neurophysiological, psychological, and social realms are mostly thickets.” (p.239).
As Lycan (1981) writes, “‘Neuron’ can be as much a functional description as ‘pain’. It is just empirically implausible that mental states are best identified with something so specific as the neuroanatomical level.” And so one consequence of a very fine-grained functionalism is that it pushes us towards a biologically chauvinistic theory of the mental.
Different investigations might prioritize different sub-questions, focusing on the human, or primate, or mammalian, or drosophila brain, for example. And for a few quite sophisticated capacities (as well as many simpler ones), we are now able to simulate species-typical responses within a plausible marginal of error (see e.g. Yamins and DiCarlo 2016).
The literature on neural population coding is huge, but for an accessible review, see Panzeri et al (2015). Wei et al (2019) explore various analysis techniques to deal with the variability in neural responses, and also provide a nice discussion of the virtues and drawbacks of these approaches. To be clear, my reservations about multiple realization at the level of a typical organism’s brain function apply primarily to the global collection of functional states and processes taken to constitute unfettered mental states in the wild. For extremely specific capacities to be demonstrated under relatively restrictive conditions, by contrast, I am agnostic, or even optimistic, since many fewer functional properties will matter, and there have been striking empirical successes on this front.
Experimentalists can run afoul of these conditions when they do experiments on neurons either in culture, or in acute slices, rather than in vivo. For example, neurons in dissociated cultures express unusual complements of receptors that are not seen in vivo, because of differences in their exposure to various growth factors. Neurons in slices are much more likely to exhibit oscillatory behaviors that are rarely seen in vivo (leading to some contentious debates about whether the oscillatory phenomena studied are genuinely relevant to brain function). And neurons can’t survive without glia, either in or out of the brain.
The importance of these constraints also provides a convenient functionalist argument for why Blockhead and Aunt Hilary don’t qualify as having mental states—because they are misdescribed as realizing the right functional organization, even though it would in fact be nomologically impossible for them to have the right functional organization due to the constraints that function places on constitution.
It might be argued that functionalism without multiple realizability isn’t functionalism at all, because it doesn’t give us the non-reductive goods—i.e. the autonomy of psychology. I think this is a mistake: to the extent that multiple realization is an empirical claim on which autonomy depends, any theory that presumes autonomy from the armchair is exhibiting a failing and not a virtue. I don’t think that functionalism must be committed to the autonomy of psychology—rather, a functionalist, behaviorist, and identity theorist might all reach the same verdicts as to which actual systems count as having minds and psychological states, but for different reasons. (And because the reasons that I rely on are ultimately functionalist, the view I am defending here is not a brute identity theory.)
This is a point raised in Dennett (1987). Maybe this explanatory difference is why functionalism and behaviorism can feel so different: behaviorism explains particular behavioral profiles by reference to the general (e.g. she yelped upon stubbing her toe because that is an instance of what people do under general circumstances of minor bodily insult), while functionalism at least purports to explain particular behavioral profiles by reference to their internal causes.
A series of related points are made beautifully in Sect. 4 of Chirimuuta’s (2018) review of Polger and Shapiro (2016). She emphasizes the importance of functional thinking in biology itself, and the interaction of this fact with the Heraclitean nature of living matter: “It can both be true that the material from which the nervous system is built (i.e. living, metabolizing cells) is crucial to their function and that those functions are multiply realized… Biological systems robustly maintain their functional profiles in spite of constant internally and externally generated perturbations. Therefore, the functionally relevant patterns—which stay the same across lower level changes, like the spiral shape of a raging tornado—will not be readily apparent at the finest grain descriptions of individual cells, or even small circuits. Hence the need for “meso-level” descriptions which make salient the shape of the storm against the swirling flux of background changes.” We may disagree about whether such multiply-realizable meso-scale descriptions are likely to exist, but not about their explanatory desirability.
References
Aaronson, S. (2011). Why Philosophers Should Care About Complexity, in Copeland, B., Posy, C., and Shagrir, O. (eds.), Computability: Turing, Gödel, Church, and Beyond, MIT Press, (2013)
Abbracchio, M. P., Burnstock, G., Verkhratsky, A., & Zimmermann, H. (2009). Purinergic signalling in the nervous system: an overview. Trends Neurosci. 2009 Jan;32(1):19–29.
Aizawa, K. (2013). Multiple realization by compensatory differences. Euro Jnl Phil Sci, 3, 69–86.
Aizawa, K., & Gillett, C. (2009). The (Multiple) Realization of Psychological and other Properties in the Sciences. Mind & Language, 24, 181–208.
Anderson, M. L. (2010). Neural reuse: A fundamental organizational principle of the brain. (Target article. Behavioral and Brain Sciences, 33(4), 245–266.
Angulo, M. C., Kozlov, A. S., Charpak, S., & Audinat, E. (2004). Glutamate Released from Glial Cells Synchronizes Neuronal Activity in the Hippocampus. J. Neurosci, 24(31 (August 4), 6920–6927.
Allen, Nicola J., Eroglu, C. (2017) Cell Biology of Astrocyte-Synapse Interactions. Neuron 96(3) 697-708 S089662731730925X. https://doi.org/10.1016/j.neuron.2017.09.056
Bechtel, W. and Mundale, J. (1999). “Multiple Realizability Revisited: Linking Cognitive and Neural States,” Philosophy of Science 66, no. 2: 175–207.
Bickle, J. (2010). Has the last decade of challenges to the multiple realization argument provided aid and comfort to psychoneural reductionists? Synthese, 177, 247–260.
Block, N. (1978). ‘Troubles with Functionalism’. In C. W. Savage (Ed.), Perception and Cognition: Issues in the Foundations of Psychology. Minneapolis: University of Minnesota Press.
Block, N. (1997). “Anti-Reductionism Slaps Back” Noûs, Vol. 31, Supplement: Philosophical Perspectives, 11, Mind, Causation, and World, pp. 107–132.
Bullock, T. H., Bennett, M. V., Johnston, D., Josephson, R., Marder, E., & Fields, R. D (2005). The neuron doctrine, redux.Science. Nov4;310(5749):791–3.
Cao, R. (2014). Signaling in the Brain: In Search of Functional Units, Philosophy of Science, 81 (December 2014) pp. 891–901.
Chalmers, D. J. (1995). Absent Qualia, Fading Qualia, Dancing Qualia. In Thomas Metzinger (ed.), Conscious Experience. Ferdinand Schoningh. pp. 309–328.
Chalmers, D. J. (1996). Does a rock implement every finite-state automaton? Synthese, 108, 309–333.
Chirimuuta, M. (2018). Marr, Mayr, and MR: What functionalism should now be about. Philosophical Psychology, 31(3), 403–418. https://doi.org/10.1080/09515089.2017.1381679
Clark, A. (2008). Supersizing the Mind: Embodiment, Action, and Cognitive Extension, Oxford University Press, 2008.
Dennett, D. C. (1986). “The Logical Geography of Computational Approaches: A View from the East Pole.“ In The Representation of Knowledge and Belief, edited by R. Harnish and M. Brand, 59–79. Tucson: University of Arizona Press.
Dennett, D. C. (1987). “Fast Thinking” Chapter 9 from The Intentional Stance. Cambridge MA: MIT Press.
Dennett, D. C. (1990). True Believers: The intentional strategy and why it works, W. Lycan, ed., Mind and Cognition: A Reader, MIT Press.
Dennett, D. (1996). Kinds of Minds. New York: Basic Books.
Del-Bel, E. & De-Miguel, F. (2018) Extrasynaptic Neurotransmission Mediated by Exocytosis and Diffusive Release of Transmitter Substances. Frontiers in Synaptic Neuroscience 1013. https://doi.org/10.3389/fnsyn.2018.00013
Fang, W. (2018). The Case for Multiple Realization in Biology, Biology and Philosophy 33 (1–2):3 (2018).
Fodor, J. (1974). “Special Sciences (Or: The Disunity of Science as a Working Hypothesis. Synthese, 28, 97–115.
Garthwaite, J. (2006). Signaling from blood vessels to CNS axons through nitric oxide. J Neurosci, 26, 7730–7740.
Greengard, P. (2001). “The Neurobiology of Slow Synaptic Transmission”, Science 2 November 2001: Vol. 294. no. 5544, pp. 1024–1030.
Godfrey-Smith, P. (2008). Reduction in Real Life, In Jakob Hohwy & Jesper Kallestrup (eds.), Being Reduced: New Essays on Reduction, Explanation, and Causation. Oxford University Press.
Godfrey-Smith, P. (2009). Triviality arguments against functionalism. Philosophical Studies 145, 273–295.
Halassa, M., & Haydon, P. G. (March 17, 2010). Integrated brain circuits: astrocytic networks modulate neuronal activity and behavior. Annual Review of Physiology, 72, 335–355.
Hamilton & Attwell Nicola. (2010). “Do astrocytes really exocytose neurotransmitters?”. Nature Reviews Neuroscience, 11, 227–238.
Han X, Chen M, Wang F, Windrem M, Wang S, Shanz S, Xu Q, Oberheim NA, Bekar L, Betstadt S, Silva AJ, Takano T, Goldman SA, Nedergaard M. (2013) Forebrain engraftment by human glial progenitor cells enhances synaptic plasticity and learning in adult mice. Cell Stem Cell. 2013 Mar 7;12(3):342–53. https://doi.org/10.1016/j.stem.2012.12.015 PMID: 23472873; PMCID: PMC3700554.
Hardingham, N., & Fox, K. (2006). The role of nitric oxide and GluR1 in presynaptic and postsynaptic components of neocortical potentiation. J Neurosci, 26, 7395–7404.
Haydon, P. G., & Carmignoto, G. ; Astrocyte Control of Synaptic Transmission and Neurovascular Coupling. Physiol. Rev. 86: 1009–1031, (2006).
Hollingsworth, S. A., & Dror, R. (2018). Molecular dynamics: Simulation for all; Neuron 99, pp. 1129–1143.
Kell, A. J. E., Yamins, D. L. K., Shook, E. N., Norman-Haignere, S. V., & McDermott, J. H. (2018). A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy. Neuron, 98(3), 630–644e16.
Krimer, L. S., Muly, E. C. 3rd, Williams, G. V., & Goldman-Rakic, P. S. (1998). Dopaminergic regulation of cerebral cortical microcirculation. Nat Neurosci, Aug;1(4), 286–289.
Krishnan, V., & Nestler, E. J. (2010). Linking Molecules to Mood: New Insight Into the Biology of Depression. American Journal of Psychiatry, 167(11), 1305–1320.
Lewis, D. (1972). Psychophysical and theoretical identifications. Australasian Journal of Philosophy, 50(3), 249–258.
London, M., & Hausser, M. (2005). Dendritic computation. Annu. Rev. Neurosci, 28, 503–532.
Lycan, W. G. (1981). Form, Function, Feel. Journal of Philosophy, 78(Jan), 24–50.
Ma, Z., Stork, T., Bergles, D., et al. (2016). Neuromodulators signal through astrocytes to alter neural circuit activity and behaviour. Nature, 539, 428–432.
Magistretti, P. J., & Allaman, I. (2018). Lactate in the brain: from metabolic end-product to signalling molecule. Nature Reviews Neuroscience volume, 19, 235–249.
Marr, D. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. New York: Henry Holt and Co.
McIntyre, C. K., & Roozendaal, B. (2007). Ch 13: "Adrenal Stress Hormones and Enhanced Memory for Emotionally Arousing Experiences" in Neural plasticity and memory: from genes to brain imaging, pp 265–284.
Mel, Bartlett W. (2007), 'Why have dendrites? A computational perspective’, in Greg Stuart, Nelson Spruston, and Michael Häusser (eds), Dendrites, 2nd edn (Oxford, 2007; online edn, Oxford Academic, 22 Mar. 2012).
Moore, C., & Cao, R. (2008). The hemo-neural hypothesis: on the role of blood flow in information processing. J Neurophysiol. 2008 May, 99(5), 2035–2047. Epub 2007 Oct 3.
Nedergaard, M., Ransom, B., & Goldman, A. (2003). New roles for astrocytes: Redefining the functional architecture of the brain. Trends in Neurosciences. Volume 26, Issue 10, Pages 523–530.
Ohta, T. (2001). Neutral Theory, in Encyclopedia of Genetics, commenting on King & Jukes (1969).
Ott, T., & Nieder, A. (2019). Dopamine and Cognitive Control in Prefrontal Cortex. Trends Cogn Sci. 2019 Mar;23(3):213–234. Epub 2019 Jan 31.
Panzeri, S., Macke, J.H., Gross, J., Kayser, C. (2015) Neural population coding: combining insights from microscopic and mass signals. Trends in Cognitive Sciences 19(3) 162–172 S1364661315000030. https://doi.org/10.1016/j.tics.2015.01.002
Perea G, Araque A. (2007) Astrocytes potentiate transmitter release at single hippocampal synapses. Science. 2007 Aug 24;317(5841):1083–6.
Piccinini, G., & Maley, C. (2014). “The Metaphysics of Mind and the Multiple Sources of Multiple Realizability.” In New Waves in the Philosophy of Mind, M. Sprevak, & J. Kallestrup (Eds.),125–52. London:Palgrave Macmillan (2014).
Pirritano, D., Plastino, M., Bosco, D., Gallelli, L., Siniscalchi, A., & De Sarro, G. (2014). Gambling disorder during dopamine replacement treatment in Parkinson’s disease: a comprehensive review. Biomed Res Int. 2014:728038.
Poldrack, R. A. (2006). Can cognitive processes be inferred from neuroimaging data? Trends in Cognitive Sciences, Volume 10, Issue 2, February 2006, Pages 59–63.
Polger, T. (2004). Natural Minds. Cambridge MA: MIT Press.
Polger, T. (2008). Two confusions concerning multiple realization. Philosophy of Science Vol. 75, No. 5, Proceedings of the 2006 Biennial Meeting of the Philosophy of Science Association Part II: Symposia Papers Edited by Cristina Bicchieri and Jason Alexander, pp. 537–547.
Polger, T., & Shapiro, L. (2016). The Multiple Realization Book. Oxford University Press UK.
Putnam, H. (1967). “The Nature of Mental States” (originally published as “Psychological Predicates”). In W. H. Captain, & D. D. Merrill (Eds.), Art, Mind and Religion (pp. 37–48). Pittsburgh: University of Pittsburgh Press.
Putnam, H. (1988). Representation and reality. Cambridge, MA: MIT Press.
Pylyshyn, Z. (1980). Reply to Searle in Searle, J., 1980, ‘Minds, Brains and Programs’, Behavioral and Brain Sciences, 3: 417–57.
Schrimpf, M., Blank, I., Tuckute, G., Kauf, C., Hosseini, E. A., Kanwisher, N., & Tenenbaum, J. (2021). Evelina Fedorenko. The neural architecture of language: Integrative modeling converges on predictive processing. Proceedings of the National Academy of Sciences Nov 118 (45) e2105646118.
Schultz W, Dayan P, Montague RR. (1997) A neural substrate of prediction and reward. Science.;275:1593–1599.
Searle, J. (1990). Is the Brain a Digital Computer? Proceedings and Addresses of the American Philosophical Association, Vol. 64, No. 3, pp. 21–37.
Shapiro, L. (2000). Multiple Realizations. Journal of Philosophy, 97(12), 635–654.
Shapiro, L. (2008). How to Test for Multiple Realization. Philosophy of Science, 75(5), 514–525.
Shapiro, L. A. (2004). The Mind Incarnate. MIT Press.
Sharma, J., Angelucci, A., & Sur, M. (2000). Induction of visual orientation modules in auditory cortex. Nature, 404, 841–847.
Sprevak, M. (2018). Triviality arguments about computational implementation, in Routledge Handbook of the Computational Mind (edited by Mark Sprevak & Matteo Colombo), Routledge: London, pp. 175–191.
Steinert, J. R., Robinson, S. W., Tong, H., Haustein, M. D., Kopp-Scheinpflug, C., & Forsythe, I. D. (2011). Nitric oxide is an activity-dependent regulator of target neuron intrinsic excitability. Neuron, 71(2), 291–305.
Steinert, J. R., Kopp-Scheinpflug, C., Baker, C., et al. (2008). Nitric oxide is a volume transmitter regulating postsynaptic excitability at a glutamatergic synapse. Neuron, 60(4), 642–656.
Stellwagen, D., & Malenka, R. C. (2006). Synaptic scaling mediated by glial TNF-alpha. Nature, 440, 1054–1059.
Wei, Z., Inagaki, H., Li, N., Svoboda, K., Druckmann, S. (2019) An orderly single-trial organization of population dynamics in premotor cortex predicts behavioral variability. Nature Communications 10(1) 216. https://doi.org/10.1038/s41467-018-08141-6
Turrigiano Gina. (2007). Homeostatic signaling: the positive side of negative feedback. Current Opinion Neurobiology, 17(3), 318–324.
Wimsatt, W. C. (1986) Developmental Constraints, Generative Entrenchment, and the Innate-Acquired Distinction. pp.185–208 in W. Bechtel (Ed.), Integrating Scientific Disciplines, Science and Philosophy, vol 2. Springer, Dordrecht.
Wimsatt, W. C. (2006). Reductionism and its heuristics: Making methodological reductionism honest. Synthese Volume, 151(3), 445–475.
Wimsatt, W. C. (2007). Re-Engineering Philosophy for Limited Beings. Cambridge MA: Harvard University Press.
Yamins Daniel, L. K., & DiCarlo James, J. (2016). Using goal-driven deep learning models to understand sensory cortex. Nat. Neurosci, 19(3), 356–365.
Zhang, J. M., Wang, H. K., Ye, C. Q., Ge, W. P., Chen, Y. R., Jiang, Z. L. & Duan, S. M. (2003). ATP Released by Astrocytes Mediates Glutamatergic Activity-Dependent Heterosynaptic Suppression, Neuron, Volume 40, Issue 5, Pages 971–982.
Acknowledgements
My heartfelt thanks to Peter Godfrey-Smith, Sean Kelly, Bill Wimsatt, Daniel Dennett, Nicholas Shea, Larry Shapiro, Tom Polger, Enoch Lambert, Justin Junge, Beckett Sterner, Brian Hemond, Chris Moore, Evelyn Keller, Sam Tobin-Hochstadt, Katie Edmonds, Lindy Blackburn, Nikhil Bhatla, Rob Goldstone, Colin Allen, Peter Todd, Kirk Ludwig, Gary Ebbs, Gualtiero Piccinini, Ned Block, Dan Waxman, Jeremy Goodman, David Chalmers, Michael Strevens, Charles Rathkopf, Helen Longino, Michael Anderson, Daniel Yamins, Paul Nuyujukian, Keith Winstein, and Jared Warren for extensive and helpful comments over many revisions. I am also grateful for feedback and suggestions from audiences at BU, Tufts, IU, Antwerp, Irvine, USC, and Stanford, as well as several anonymous reviewers.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
No funding or conflicts of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Cao, R. Multiple realizability and the spirit of functionalism. Synthese 200, 506 (2022). https://doi.org/10.1007/s11229-022-03524-1
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11229-022-03524-1