I want to take this opportunity to reinforce a point that is perhaps not stated clearly enough in my book. It is a point that Honavar (and another reviewer) have rightfully pursued.The NP-completeness is with respect to the size of the network and the amount of data in the training set to be loaded. Honavar points out that this measure of complexity of the input is somewhat suspect because "it does not reflect the.., inherent regularities of the data items being loaded; nor can it capture possible effects of choosing a particular network topology for loading a given set of data items." Very true. If we let the devil specify the network architecture (and the data to be loaded) then he can make our lives exceedingly difficult, but we should not take this result to indicate that our lives have to be difficult.In the domain of connectionist research, it is the researcher who gets to play the role of the devil, and he is certainly not trying to be malicious. If he knows what he is doing, he can presumably make the problem easy; but, no one yet has a theory of how best to accomplish this. The themes of my results are that it is easy to get into trouble, and that we need to elaborate the theory further. (Maybe the common all-to-all connectivity scheme is a solution for small nets, but that claim has to be proved and it may introduce concomitant generalization problems.)In the neurological domain, the network design is not under our control; it is nature that has played the role of devil and we must see what hand she has dealt us. For my explorations I picked a particular design family (shallow nets, grid-like structures) to model what seems to be the first-order aspect of her handiwork. Am I off base here? No one has yet suggested so. Even though the architectures are extremely regular, the intractability still shines through, and we are left to conclude that the only types of data that can be loaded in there have to have some kind of fortuitous peculiarity. Honovar's concern about "the interactions between the network architecture and intrinsic structure of the data set to be loaded" are natural and important. My theorems on simple regular architectures are an attempt to address this concern, although such is not well elaborated in the book.Blum and Rivest responded to my early theorems (which had large and complicated architectures as well as specially constructed data sets) by finding an apparently smaller, very simple, and very regular architecture that nevertheless also has intractable data sets. They too were worried that the intractability was somehow due to a devious mismatch between the data and the architecture, but their result also seems to mute this hypothesis. However, neither they nor myself have any crisp statements about this.

Cultured cardiomyocytes can be used to study cardiomyocyte biology using techniques that are complementary to in vivo systems. For example, the purity and accessibility of in vitro culture enables fine control over biochemical analyses, live imaging, and electrophysiology. Long-term culture of cardiomyocytes offers access to additional experimental approaches that cannot be completed in short term cultures. For example, the in vitro investigation of dedifferentiation, cell cycle re-entry, and cell division has thus far largely been restricted to rat cardiomyocytes, which appear to be more robust in long-term culture. However, the availability of a rich toolset of transgenic mouse lines and well-developed disease models make mouse systems attractive for cardiac research. Although several reports exist of adult mouse cardiomyocyte isolation, few studies demonstrate their long-term culture. Presented here, is a step-by-step method for the isolation and long-term culture of adult mouse cardiomyocytes. First, retrograde Langendorff perfusion is used to efficiently digest the heart with proteases, followed by gravity sedimentation purification. After a period of dedifferentiation following isolation, the cells gradually attach to the culture and can be cultured for weeks. Adenovirus cell lysate is used to efficiently transduce the isolated cardiomyocytes. These methods provide a simple, yet powerful model system to study cardiac biology. Video LinkThe video component of this article can be found at http://www.jove.com/video/54012/ IntroductionCultured cardiomyocytes are frequently used to monitor cell behavior in a well-controlled environment in vitro. For example, morphological, electrical, biochemical, or mechanical cell properties can be studied on engineered substrates, 1,2 in defined media, and in response to small molecule drugs, peptides, gene regulation, 3 or electrical stimulation. 4 The cellular content can also be controlled using defined co-cultures. 5These in vitro experiments are useful in large drug or genetic screens and complement in vivo methods for various types of investigations involving cardiomyocyte biology.Long-term culture enables experimental avenues that require extended periods of time to achieve phenotypic change. A timely example is that of adult mammalian cardiomyocyte proliferation, where dedifferentiation, cell cycle re-entry, and cell division is typically studied over several days to weeks. 6,7 Here, the extended culture time facilitates genetic manipulation, 7,8 functional dedifferentiation (e.g., sarcomeric disassembly) 9 and potentially transcriptional dedifferentiation.6 Subsequent cell cycle re-entry and cell division requires even longer culture periods to observe, especially if multiple rounds of division are the experimental goal. The importance of the cardiomyocyte cell-cycle is central to several recent key scientific works in heart regeneration, where the dedifferentiation and proliferation of pre-existing cardiomyocytes has been shown responsible for heart regeneration in ...

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