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Charles Rathkopf [8]Charles A. Rathkopf [1]
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Charles Rathkopf
Jülich Research Center
  1. Network Representation and Complex Systems.Charles Rathkopf - 2018 - Synthese (1).
    In this article, network science is discussed from a methodological perspective, and two central theses are defended. The first is that network science exploits the very properties that make a system complex. Rather than using idealization techniques to strip those properties away, as is standard practice in other areas of science, network science brings them to the fore, and uses them to furnish new forms of explanation. The second thesis is that network representations are particularly helpful in explaining the properties (...)
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  2. Localization and Intrinsic Function.Charles A. Rathkopf - 2013 - Philosophy of Science 80 (1):1-21.
    This paper describes one style of functional analysis commonly used in the neurosciences called task-bound functional analysis. The concept of function invoked by this style of analysis is distinctive in virtue of the dependence relations it bears to transient environmental properties. It is argued that task-bound functional analysis cannot explain the presence of structural properties in nervous systems. An alternative concept of neural function is introduced that draws on the theoretical neuroscience literature, and an argument is given to show that (...)
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    Neural Information and the Problem of Objectivity.Charles Rathkopf - 2017 - Biology and Philosophy 32 (3):321-336.
    A fascinating research program in neurophysiology attempts to quantify the amount of information transmitted by single neurons. The claims that emerge from this research raise new philosophical questions about the nature of information. What kind of information is being quantified? Do the resulting quantities describe empirical magnitudes like those found elsewhere in the natural sciences? In this article, it is argued that neural information quantities have a relativisitic character that makes them distinct from the kinds of information typically discussed in (...)
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    What Kind of Information is Brain Information?Charles Rathkopf - 2020 - Topoi 39 (1):95-102.
    Neural systems process information. This platitude contains an interesting ambiguity between multiple senses of the term “information.” According to a popular thought, the ambiguity is best resolved by reserving semantic concepts of information for the explication of neural activity at a high level of organization, and quantitative concepts of information for the explication of neural activity at a low level of organization. This article articulates the justification behind this view, and concludes that it is an oversimplification. An analysis of the (...)
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    What Kind of Information is Brain Information?Charles Rathkopf - 2020 - Topoi 39 (1):95-102.
    Neural systems process information. This platitude contains an interesting ambiguity between multiple senses of the term “information.” According to a popular thought, the ambiguity is best resolved by reserving semantic concepts of information for the explication of neural activity at a high level of organization, and quantitative concepts of information for the explication of neural activity at a low level of organization. This article articulates the justification behind this view, and concludes that it is an oversimplification. An analysis of the (...)
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  6. Can We Read Minds by Imaging Brains?Charles Rathkopf - 2022 - Philosophical Psychology 10:1-25.
    Will brain imaging technology soon enable neuroscientists to read minds? We cannot answer this question without some understanding of the state of the art in neuroimaging. But neither can we answer this question without some understanding of the concept invoked by the term "mind reading." This article is an attempt to develop such understanding. Our analysis proceeds in two stages. In the first stage, we provide a categorical explication of mind reading. The categorical explication articulates empirical conditions that must be (...)
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    Mending Wall.Charles Rathkopf & Daniel C. Dennett - 2019 - Behavioral and Brain Sciences 42.
    Heyes suggests that selective social learning comes in two varieties. One is common, domain general, and associative. The other is rare, domain specific, and metacognitive. We argue that this binary distinction cannot quite do the work she assigns it and sketch a framework in which additional strategies for selective social learning might be accommodated.
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    Modest and Immodest Neural Codes: Can There Be Modest Codes?Rosa Cao & Charles Rathkopf - 2019 - Behavioral and Brain Sciences 42.
    We argue that Brette's arguments, or some variation on them, work only against the immodest codes imputed by neuroscientists to the signals they study; they do not tell against “modest” codes, which may be learned by neurons themselves. Still, caution is warranted: modest neural codes likely lead to only modest explanatory gains.
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  9. Mental Evolution: A Review of Daniel Dennett’s From Bacteria to Bach and Back. [REVIEW]Charles Rathkopf - 2017 - Biology and Philosophy 32 (6):1355-1368.
    From Bacteria To Bach and Back is an ambitious book that attempts to integrate a theory about the evolution of the human mind with another theory about the evolution of human culture. It is advertised as a defense of memes, but conceptualizes memes more liberally than has been done before. It is also advertised as a defense of the proposal that natural selection operates on culture, but conceptualizes natural selection as a process in which nearly all interesting parameters are free (...)
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