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Minds and Machines

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24-02-2018

Neural Representations Observed

Authors: Eric Thomson, Gualtiero Piccinini

Published in: Minds and Machines | Issue 1/2018

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Abstract

The historical debate on representation in cognitive science and neuroscience construes representations as theoretical posits and discusses the degree to which we have reason to posit them. We reject the premise of that debate. We argue that experimental neuroscientists routinely observe and manipulate neural representations in their laboratory. Therefore, neural representations are as real as neurons, action potentials, or any other well-established entities in our ontology.

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This is not an exhaustive list. For instance, we won't discuss neural representation of space (Andersen et al. 1997; Moser et al. 2008).

Similar notions of representation are defended by Shepard and Chipman (1970), Swoyer (1991), Cummins (1996), Grush (2004), O'Brien and Opie (2004), Ryder (2004, forthcoming), Bartels (2006), Waskan (2006), Ramsey (2007), Bechtel (2008), Churchland (2012), Shagrir (2012), Isaac (2013), Hohwy (2013), Clark (2016), Morgan (2014) and Neander (2017, Chap. 8).

For a fuller treatment, see Hubel and Wiesel (2005), Rodieck (1998) and Wandell (1995).

Technically, the inequality states that if X → Y → Z is a Markov Chain, then I(X; Y) ≥ I(X; Z), where I() is mutual information. Using this to theorize about internal states of the animal assumes that the behavior of the animal in the working memory task depends on some internal state of the animal after the stimulus was presented. This is easy enough to demonstrate by removing the brain of the animal.

Note that humans beat dogs by a good order of magnitude. One study presented 10,000 pictures to passive observers in one sitting, and they were later able to recognize them with 90% accuracy (Standing 1973).

Note that ablating M1 does not always lead to permanent paresis, but more short-lived and subtle motor deficits (Schwartzman 1978). Sometimes such ablations show no notable motor deficits, but instead deficits in motor learning (Kawai et al. 2015, though see Castro 1972; Makino et al. 2017). Such results undermine simple stories in which M1 is the final common output driving all movement. Some of the most recalcitrant movement deficits such as Parkinson's disease result from damage to subcortical structures like the basal ganglia. As discussed briefly at the end of this section, motor control is distributed across multiple cortical and subcortical areas, and the focus on M1 here is a convenience meant to keep the discussion contained, not an endorsement of strict localizationist theories of M1 motor control.

Discussions of explicit/implicit coding have always taken place in the sensory system, so it is not actually clear if these are the correct standards to use for M1, which tends to send its outputs to muscles and central pattern generators (Kalaska 2009).

Note that ‘implicit’ is not the same as ‘distributed’ or ‘population’ code. ‘Implicit' implies ‘population' but not vice versa. Even in Georgopoulos' work, perhaps the locus classicus of explicit motor representations, to know the velocity of the animal's arm you must know the firing rate of the population of M1 neurons. That is, everyone in the game accepts that motor control involves a distributed code.

Sometimes these terms are used differently. For instance, ‘corollary discharge’ is sometimes taken to be the output of a forward model (see below). However, the two terms are typically used as synonyms in the literature. For instance, "A ubiquitous strategy is to route copies of movement commands to sensory structures. These signals, which are referred to as corollary discharge (CD), influence sensory processing in myriad ways" (Crapse and Sommer 2008). It would be a mistake to conclude, as (Clark 2016) does, that a paper doesn't support the existence of efference copy just because it uses the phrase 'corollary discharge'.

Why would such plasticity be useful in the electric fish? The local EM fields produced by the same EOD can change depending on changes in water resistivity, or if the animal is swimming, or spending considerable time next to a nonconducting surface such as a rock or air at the water's surface (Bell 1982), so the sensory consequences of the EOD are likely malleable enough that it is helpful to learn them (Bell 1981).

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Title: Neural Representations Observed
Authors: Eric Thomson
Gualtiero Piccinini
Publication date: 24-02-2018
Publisher: Springer Netherlands
Published in: Minds and Machines / Issue 1/2018
Print ISSN: 0924-6495
Electronic ISSN: 1572-8641
DOI: https://doi.org/10.1007/s11023-018-9459-4

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