Because of the “all-or-none” character of nervous activity, neural events and the relations among them can be treated by means of propositional logic. It is found that the behavior of every net can be described in these terms, with the addition of more complicated logical means for nets containing circles; and that for any logical expression satisfying certain conditions, one can find a net behaving in the fashion it describes. It is shown that many particular choices among possible neurophysiological assumptions (...) are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under the other and gives the same results, although perhaps not in the same time. Various applications of the calculus are discussed. (shrink)

Recent work by Brian Skyrms offers a very general way to think about how information flows and evolves in biological networks — from the way monkeys in a troop communicate, to the way cells in a body coordinate their actions. A central feature of his account is a way to formally measure the quantity of information contained in the signals in these networks. In this paper, we argue there is a tension between how Skyrms talks of signalling networks and his (...) formal measure of information. Although Skyrms refers to both how information flows through networks and that signals carry information, we show that his formal measure only captures the latter. We then suggest that to capture the notion of flow in signalling networks, we need to treat them as causal networks. This provides the formal tools to define a measure that does capture flow, and we do so by drawing on recent work defining causal specificity. Finally, we suggest that this new measure is crucial if we wish to explain how evolution creates information. For signals to play a role in explaining their own origins and stability, they can’t just carry information about acts: they must be difference-makers for acts. (shrink)

The concept of information tempts us as a theoretical primitive, partly because of the respectability lent to it by highly successful applications of Shannon’s information theory, partly because of its broad range of applicability in various domains, partly because of its neutrality with respect to what basic sorts of things there are. This versatility, however, is the very reason why information cannot be the theoretical primitive we might like it to be. “Information,” as it is variously used, is systematically ambiguous (...) between whether it involves continuous or discrete quantities, causal or noncausal relationships, and intrinsic or relational properties. Many uses can be firmly grounded in existing theory, however. Continuous quantities of information involving probabilities can be related to information theory proper. Information defined relative to systems of rules or conventions can be understood relative to the theory of meaning . A number of causal notions may possibly be located relative to standard notions in physics. Precise specification of these distinct properties involved in the common notion of information can allow us to map the relationships between them. Consequently, while information is not in itself the kind of single thing that can play a significant unifying role, analyzing its ambiguities may facilitate headway toward that goal. (shrink)

The book "Philosophical Foundations of Neuroscience" is an engaging criticism of cognitive neuroscience from the perspective of a Wittgensteinian philosophy of ordinary language. The authors' main claim is that assertions like "the brain sees" and "the left hemisphere thinks" are integral to cognitive neuroscience but that they are meaningless because they commit the mereological fallacy—ascribing to parts of humans, properties that make sense to predicate only of whole humans. The authors claim that this fallacy is at the heart of Cartesian (...) dualism, implying that current cognitive neuroscientists are Cartesian dualists. Against this claim, we argue that the fallacy cannot be committed within Cartesian dualism either, for this doctrine does not allow for an intelligible way of asserting that a soul is part of a human being. Also, the authors' Aristotelian essentialistic outlook is at odds with their Wittgensteinian stance, and we were unconvinced by their case against explanatory reductionism. Finally, although their Wittgensteinian stance is congenial with radical behaviorism, their separation between philosophy and science seems less so because it is based on a view of philosophy as a priori. The authors' emphasis on the a priori, however, does not necessarily commit them to rationalism if it is restricted to conceptual or analytical truths. (shrink)

The brain is often taken to be a paradigmatic example of a signaling system with semantic and representational properties, in which neurons are senders and receivers of information carried in action potentials. A closer look at this picture shows that it is not as appealing as it might initially seem in explaining the function of the brain. Working from several sender-receiver models within the teleosemantic framework, I will first argue that two requirements must be met for a system to support (...) genuine semantic information: 1. The receiver must be competent —that is, it must be able to extract rewards from its environment on the basis of the signals that it receives. 2. The receiver must have some flexibility of response relative to the signal received. In the second part of the paper, this initial framework will be applied to neural processes, pointing to the surprising conclusion that signaling at the single-neuron level is only weakly semantic at best. Contrary to received views, neurons will have little or no access to semantic information (though their patterns of activity may carry plenty of quantitative, correlational information) about the world outside the organism. Genuine representation of the world requires an organism - level receiver of semantic information, to which any particular set of neurons makes only a small contribution. (shrink)

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 (...) the philosophical literature. It is also argued that despite this relativistic character, there are cases in which neural information quantities can be viewed as robustly objective empirical properties. (shrink)

Recent work by Brian Skyrms offers a very general way to think about how information flows and evolves in biological networks—from the way monkeys in a troop communicate to the way cells in a body coordinate their actions. A central feature of his account is a way to formally measure the quantity of information contained in the signals in these networks. In this article, we argue there is a tension between how Skyrms talks of signalling networks and his formal measure (...) of information. Although Skyrms refers to both how information flows through networks and that signals carry information, we show that his formal measure only captures the latter. We then suggest that to capture the notion of flow in signalling networks, we need to treat them as causal networks. This provides the formal tools to define a measure that does capture flow, and we do so by drawing on recent work defining causal specificity. Finally, we suggest that this new measure is crucial if we wish to explain how evolution creates information. For signals to play a role in explaining their own origins and stability, they can’t just carry information about acts; they must be difference-makers for acts. _1_ Signalling, Evolution, and Information _2_ Skyrms’s Measure of Information _3_ Carrying Information versus Information Flow _3.1_ Example 1 _3.2_ Example 2 _3.3_ Example 3 _4_ Signalling Networks Are Causal Networks _4.1_ Causal specificity _4.2_ Formalizing causal specificity _5_ Information Flow as Causal Control _5.1_ Example 1 _5.2_ Examples 2 and 3 _5.3_ Average control implicitly ‘holds fixed’ other pathways _6_ How Does Evolution Create Information? _7_ Conclusion Appendix >. (shrink)