Christoph Börgers

Mr. Smith has two children. Given that at least one of them is a boy, how likely is it that Mr. Smith has two boys? It’s a very standard puzzle in elementary books on probability theory. Whoever asks you this question hopes that you will answer “1/2”, in which case they can say triumphantly “Oh no, the answer is 1/3 ”. This is called the two-child puzzle. Some authors have discussed a striking variation, which we’ll call the Adam puzzle. Again, Mr. Smith has two children. Given that one of them is a boy named Adam, how likely is it that Mr. Smith has two boys? Astonishingly, now the answer is 1/2 , at least approximately. (The exact answer depends a bit on precise assumptions.) We give pictorial explanations of both puzzles. We then point out that the answers usually given rely on a tacit assumption about how the information that one of Mr. Smith’s two children is a boy, or one of them is a boy named Adam, is obtained. We give examples showing that the answers may be different with different assumptions. We conclude with a discussion of why the Adam puzzle is so confusing to most people.

It has been known for more than 300 years that the shape of an inelastic hanging cable, chain, or rope of uniform linear mass density is the graph of the hyperbolic cosine, up to scaling and shifting coordinates. But given two points at which the ends of the cable are attached, how exactly should we scale and shift the coordinates? Many otherwise excellent expositions of the problem are a little vague about that. They might for instance give the answer in terms of the tension at the lowest point, but without explaining how to compute that tension. Here we discuss how to obtain all necessary parameters. To obtain the tension at the lowest point, one has to solve a nonlinear equation numerically. When the two ends of the cable are attached at different heights, a second nonlinear equation must be solved to determine the location of the lowest point. When the cable is elastic, think of a thread in a spider's web for instance, the two equations can no longer be decoupled, but they can be solved using two-dimensional Newton iteration.

We investigate rhythms in networks of neurons with recurrent excitation, that is, with excitatory cells exciting each other. Recurrent excitation can sustain activity even when the cells in the network are driven below threshold, too weak to fire on their own. This sort of “reverberating” activity is often thought to be the basis of working memory. Recurrent excitation can also lead to “runaway” transitions, sudden transitions to high-frequency firing; this may be related to epileptic seizures. Not all fundamental questions about these phenomena have been answered with clarity in the literature. We focus on three questions here: (1) How much recurrent excitation is needed to sustain reverberating activity? How does the answer depend on parameters? (2) Is there a positive minimum frequency of reverberating activity, a positive “onset frequency”? How does it depend on parameters? (3) When do runaway transitions occur? For reduced models, we give mathematical answers to these questions. We also examine computationally to which extent our findings are reflected in the behavior of biophysically more realistic model networks. Our main results can be summarized as follows. (1) Reverberating activity can be fueled by extremely weak slow recurrent excitation, but only by sufficiently strong fast recurrent excitation. (2) The onset of reverberating activity, as recurrent excitation is strengthened or external drive is raised, occurs at a positive frequency. It is faster when the external drive is weaker (and the recurrent excitation stronger). It is slower when the recurrent excitation has a longer decay time constant. (3) Runaway transitions occur only with fast, not with slow, recurrent excitation. We also demonstrate that the relation between reverberating activity fueled by recurrent excitation and runaway transitions can be visualized in an instructive way by a (generalized) cusp catastrophe surface. We investigate rhythms in networks of neurons with recurrent excitation, that is, with excitatory cells exciting each other. Recurrent excitation can sustain activity even when the cells in the network are driven below threshold, too weak to fire on their own. This sort of “reverberating” activity is often thought to be the basis of working memory. Recurrent excitation can also lead to “runaway” transitions, sudden transitions to high-frequency firing; this may be related to epileptic seizures. Not all fundamental questions about these phenomena have been answered with clarity in the literature. We focus on three questions here: (1) How much recurrent excitation is needed to sustain reverberating activity? How does the answer depend on parameters? (2) Is there a positive minimum frequency of reverberating activity, a positive “onset frequency”? How does it depend on parameters? (3) When do runaway transitions occur? For reduced models, we give mathematical answers to these questions. We also examine computationally to which extent our findings are reflected in the behavior of biophysically more realistic model networks. Our main results can be summarized as follows. (1) Reverberating activity can be fueled by extremely weak slow recurrent excitation, but only by sufficiently strong fast recurrent excitation. (2) The onset of reverberating activity, as recurrent excitation is strengthened or external drive is raised, occurs at a positive frequency. It is faster when the external drive is weaker (and the recurrent excitation stronger). It is slower when the recurrent excitation has a longer decay time constant. (3) Runaway transitions occur only with fast, not with slow, recurrent excitation. We also demonstrate that the relation between reverberating activity fueled by recurrent excitation and runaway transitions can be visualized in an instructive way by a (generalized) cusp catastrophe surface.