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<div class="">Seeing that Deep Network skepticism is becoming a thing — at least among those of us who care about real brains and real behavior — I thought I’d offer my two cents’ worth of comments. (If you cannot get past the paywall, I’ll be happy to send
along a PDF.)</div>
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<div class="">1. Shimon Edelman, <b class=""><i class="">The minority report: some common assumptions to reconsider in the modeling of the brain and behavior</i></b>, Journal of Experimental and Theoretical AI (JETAI), DOI 10.1080/0952813X.2015.1042534 (2015).</div>
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<div class=""><a href="http://www.tandfonline.com/doi/full/10.1080/0952813X.2015.1042534" class="">http://www.tandfonline.com/doi/full/10.1080/0952813X.2015.1042534</a></div>
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<div class="">Reverse-engineering the brain involves adopting and testing a hierarchy of working hypotheses regarding the computational problems that it solves, the representations and algorithms that it employs and the manner in which these are implemented.
Because problem-level assumptions set the course for the entire research programme, it is particularly important to be open to the possibility that we have them wrong, but tacit algorithm- and implementation-level hypotheses can also benefit from occasional
scrutiny. This paper focuses on the extent to which our computational understanding of how the brain works is shaped by three such rarely discussed assumptions, which span the levels of Marr's hierarchy: (i) that animal behaviour amounts to a series of stimulus/response
bouts, (ii) that learning can be adequately modelled as being driven by the optimisation of a fixed objective function and (iii) that massively parallel, uniformly connected layered or recurrent network architectures suffice to support learning and behaviour.
In comparison, a more realistic approach acknowledges that animal behaviour in the wild is characterised by dynamically branching serial order and is often agentic rather than reactive. Arguably, such behaviour calls for open-ended learning of world structure
and may require a neural architecture that includes precisely wired circuits reflecting the serial and branching structure of behavioural tasks.</div>
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<div class="">2. Oren Kolodny and Shimon Edelman, <i class=""><b class="">The problem of multimodal concurrent serial order in behavior</b></i>, Neuroscience and Biobehavioral Reviews 56:252-265 (2015).</div>
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<div class=""><a href="http://www.sciencedirect.com/science/article/pii/S0149763415001943" class="">http://www.sciencedirect.com/science/article/pii/S0149763415001943</a></div>
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<div class="">The “problem of serial order in behavior,” as formulated and discussed by
<span id="bbib0545" class=""><a href="http://www.sciencedirect.com/science/article/pii/S0149763415001943#bib0545" id="ancbbib0545" class="intra_ref">Lashley (1951)</a></span>, is arguably more pervasive and more profound both than originally stated and than
currently appreciated. We spell out two complementary aspects of what we term the generalized problem of behavior: (i) multimodality, stemming from the disparate nature of the sensorimotor variables and processes that underlie behavior, and (ii) concurrency,
which reflects the parallel unfolding in time of these processes and of their asynchronous interactions. We illustrate these on a number of examples, with a special focus on language, briefly survey the computational approaches to multimodal concurrency, offer
some hypotheses regarding the manner in which brains address it, and discuss some of the broader implications of these as yet unresolved issues for cognitive science.</div>
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<div class="">Shimon Edelman<br class="">
Professor, Department of Psychology, 232 Uris Hall<br class="">
Cornell University, Ithaca, NY 14853-7601<br class="">
Home page: <a href="http://kybele.psych.cornell.edu/~edelman" class="">http://kybele.psych.cornell.edu/~edelman</a><br class="">
Latest book: <a href="http://kybele.psych.cornell.edu/~edelman/Beginnings" class="">
http://kybele.psych.cornell.edu/~edelman/Beginnings</a></div>
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