This article discusses minimal model explanations, which we argue are distinct from various causal, mechanical, difference-making, and so on, strategies prominent in the philosophical literature. We contend that what accounts for the explanatory power of these models is not that they have certain features in common with real systems. Rather, the models are explanatory because of a story about why a class of systems will all display the same large-scale behavior because the details that distinguish them are irrelevant. This story (...) explains patterns across extremely diverse systems and shows how minimal models can be used to understand real systems. (shrink)

The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or predictions of phenomena. It (...) also serves to clarify the pattern of model refinement and elaboration undertaken by computational neuroscientists. (shrink)

I discuss the bearing on the mind-body problem of some general characteristics of living systems, including the physical basis of metabolism and the relation between living activity and cognitive capacities in simple organisms. I then attempt to describe stages in the history of animal life important to the evolution of subjective experience. Features of the biological basis of cognition are used to criticize arguments against materialism that draw on the conceivability of a separation between mental and physical. I also argue (...) against commonly held views about the "multiple realizability" of mental states of the kind seen in humans. The aim of the paper is to reconfigure and narrow the "explanatory gap" between mental and physical. (shrink)

Ernst Mayr''s distinction between ultimate and proximate causes is justly considered a major contribution to philosophy of biology. But how did Mayr come to this philosophical distinction, and what role did it play in his earlier scientific work? I address these issues by dividing Mayr''s work into three careers or phases: 1) Mayr the naturalist/researcher, 2) Mayr the representative of and spokesman for evolutionary biology and systematics, and more recently 3) Mayr the historian and philosopher of biology. If we want (...) to understand the role of the proximate/ultimate distinction in Mayr''s more recent career as a philosopher and historian, then it helps to consider hisearlier use of the distinction, in the course of his research, and in his promotion of the professions of evolutionary biology and systematics. I believe that this approach would also shed light on some other important philosophical positions that Mayr has defended, including the distinction between essentialism: and population thinking. (shrink)

What are the functional units of the brain? If the function of the brain is to process information-carrying signals, then the functional units will be the senders and receivers of those signals. Neurons have been the default candidate, with action potentials as the signals. But there are alternatives: synapses fit the action potential picture more cleanly, and glial activities (e.g., in astrocytes) might also be characterized as signaling. Are synapses or nonneuronal cells better candidates to play the role of functional (...) units? Will informational signaling still be the best model for brain function if we move beyond the neuron doctrine? (shrink)

In this article, I discuss the concept of robustness in neuroscience. Various mechanisms for making systems robust have been discussed across biology and neuroscience. Many of these notions originate from engineering. I argue that concepts borrowed from engineering aid neuroscientists in operationalizing robustness, formulating hypotheses about mechanisms for robustness, and quantifying robustness. Furthermore, I argue that the significant disanalogies between brains and engineered artifacts raise important questions about the applicability of the engineering framework. I argue that the use of such (...) concepts should be understood as a kind of simplifying idealization. (shrink)