Abstract
While neuroscientists often characterize brain activity as representational, many philosophers have construed these accounts as just theorists’ glosses on the mechanism. Moreover, philosophical discussions commonly focus on finished accounts of explanation, not research in progress. I adopt a different perspective, considering how characterizations of neural activity as representational contributes to the development of mechanistic accounts, guiding the investigations neuroscientists pursue as they work from an initial proposal to a more detailed understanding of a mechanism. I develop one illustrative example involving research on the information-processing mechanisms mammals employ in navigating their environments. This research was galvanized by the discovery in the 1970s of place cells in the hippocampus. This discovery prompted research in what the activity of these cells represents and how place representations figure in navigation. It also led to the discovery of a host of other types of neurons—grid cells, head-direction cells, boundary cells—that carry other types of spatial information and interact with place cells in the mechanism underlying spatial navigation. As I will try to make clear, the research is explicitly devoted to identifying representations and determining how they are constructed and used in an information processing mechanism. Construals of neural activity as representations are not mere glosses but are characterizations to which neuroscientists are committed in the development of their explanatory accounts.
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Notes
An important part of Shagrir’s argument is that in determining what computation the oculomotor system is performing the researchers turned to the relation between the position and velocity of the eyes that the system was representing—it is because the relation between the represented properties is integration this the brain needs to perform integration over representations.
In making this case Morgan also draws attention to a condition in Dretske’s (1988) account of representations, that the representation be used in the “production of movements whose successful outcome depends on what is being indicated” (p. 70). Below I contend that representations arise in systems that have the task of controlling other systems. This makes their use in the control of behavior central to accounts of representations.
Morgan shows that some of the objections Ramsey has raised about representations in current neuroscience are more properly concerned with mental representations, where such representations figure in the mental models of agents. For neuroscience, the relevant notion of representation needs to be dissociated from the role representations play in mental models and treated as processes within a processing system that stands in for their contents without the organism being conscious of them.
The language I use in this paragraph to describe the project of identifying representations is very similar to that which McCauley and I (Bechtel and McCauley 1999; McCauley and Bechtel 2001) used in discussing identity claims in science—they are proposed at the outset of inquiry and by the time a research endeavor has developed around them, revising and elaborating on them, it would seem perverse to the scientists to propose that the relation in question was mere correlation, not identity. In fact claims about representations can be viewed as instances of identity claims since what researches are doing is identifying constituents of mechanisms as representations.
This is not to ignore neural phenomena that involve local transformations within the brain that are also of considerable interest to neuroscientists. In addition to ongoing metabolic activities and processes of gene expression, there are operations involving electrical potentials resulting from movements of ions across membranes which can sometimes give raise to action potentials in the absence of external stimuli. Some of this endogenous activity, such as synchronized oscillations in electrical potentials, may be extremely important to the brain’s capacity to execute cognitive operations when it receives sensory input (Abrahamsen and Bechtel 2011).
Processing information so as to coordinate responses to conditions inside or external to the organism is not unique to organisms with brains. Bacteria perform a vast array of information processing functions using chemical rather than neural signaling, and this has led a number of researchers to refer to bacterial cognition (see, for example, Shapiro 2007). Plants also use chemical signaling to execute a broad range of behaviors that has inspired a field some refer to as plant neurobiology (for discussion, see Garzon and Keijzer 2011).
See Craver (2003) for a discussion of the research on the hippocampus that led to the discovery of long-term potentiation as a laboratory technique before it, and the hippocampus more generally, became associated with learning and memory.
Miller and Best (1980) established the necessity of input from entorhinal cortex for hippocampal function by showing that lesions to the entorhinal cortex impaired the navigational abilities of rats and the responsiveness of hippocampal place cells.
Ranck provides a reflective discussion of the prospects and challenges of such a project noting several reasons the project might fail: the firing of a neuron “may signal something not directly related to overt behavior, such as drive state, or some idea the rat has, or the blood level of some substance. The firing of the neuron might be part of some internal timing mechanism, or a mechanism in memory retrieval. The firing of the neuron might be significant only in some neural net, and therefore, firing of a single neuron may not be interpretable.” With the hippocampus, however, he proposes that the strategy does work, but contends that correlation is not enough—one must determine how the information is transformed: “to be able to apply this approach to hippocampal formation we must know behavioral correlates of almost all inputs and outputs of the system and see what transformations occur.” He distinguishes two strategies, one in which researchers seek out conditions in which the neuron fires (during this stage “the behavior of the neuron shapes the behavior of the experimenter”), and a second in which a more systematic protocol is employed that also considers its firing frequency and patterns. Ranck’s discussion is particularly interesting in light of Ramsey’s claims, discussed above, that neuroscience only uses a receptor notion of representation. On Ranck’s view, understanding the behavior of neurons requires going beyond identifying it as a receptor and determining how what it represents is affected by the context within the organism.
In the wake of the initial research recording from hippocampal cells, O’Keefe collaborated with Nadel on a lesion experiment in which they lesioned the major input and output pathway from the hippocampus, the fornix. They found that these rats were unable to learn to locate water that was always at the same location, but showed somewhat improved performance in locating water that was always marked by the same cue (light). This indicated that the rats required the representation of its current location in allocentric space provided by CA1 cells in order to use place information in navigating their environment.
Leutgelb et al.’s findings are, on the face, inconsistent with those of Muller and Kubie noted above in which change from a circular to a rectangular enclosure resulted in global remapping. This may be explained by the fact, that in Muller and Kubie’s study the rats were first trained on both enclosures when they were next to each other in a common room and so had presumably developed place codes for each (Colgin et al. 2008). The substitution of one enclosure for another thus elicited the distinct encodings that had already been acquired for each.
O’Keefe and Burgess (1996) investigated how place fields might remap as a result of changes in the dimensions of the enclosure by comparing the responsiveness of the same neurons in enclosures that differed in the length of one or both walls—a small square, vertical rectangle (vertical wall extended), horizontal rectangle, and large square. This revealed that changing the length of the wall could cause place fields to expand, or sometimes split into two separate fields.
Both Wills et al. and Leutgeb et al. interpret their studies in light of the characterization of CA3 as constituting an attractor network. Wills et al. treat the abrupt transition in the place fields as indicative of an attractor network due to the recurrent connections in CA3 following the sparse coding imposed by DG. Although Leutgeb et al.’s results are in tension with a simple attractor network with a single global attractor, they construe the hysteresis effects as showing that more than a feedforward process is at work (perhaps recurrent networks with multiple attractors).
The labeling of the frequency ranges of oscillations detected with EEG (or as local field potentials with implanted electrodes) as alpha, beta, reflects the order in which oscillations in a frequency range were discovered. The range labeled theta in the hippocampus extended higher (into the traditional alpha band) than the 4–8 Hz band associated with cortical theta waves.
See Buzsáki (2005) for a review and references. He concludes (p. 828): “Despite seven decades of hard work on rabbits, rats, mice, gerbils, guinea pigs, sheep, cats, dogs, old world monkeys, chimpanzees, and humans by outstanding colleagues, to date, there is no widely agreed term that would unequivocally describe behavioral correlate(s) of this prominent brain rhythm. By exclusion, the only firm message that can be safely concluded from this brief summary is that in an immobile animal no theta is present, provided that no changes occur in the environment (and the animal is not ‘thinking’).”
For a discussion of this and other roles oscillation and their synchronization play in neural computing, see Buzsáki (2005). In this paper I have focused on research analyzing the responses of individual neurons. This focus was a consequence of the fact that until recently researchers could only record from individual neurons. Increasingly, though, researchers are recording from populations of neurons. This enables them to focus on the interactive behavior. Regions such as CA3, with many recurrent connections, exhibit especially rich dynamic behavior, but in fact most brain regions exhibit a fair amount of recurrent connectivity between neurons, resulting in what Hebb referred to as cell assemblies. As Buzsáki (2010) argues, assemblies of neurons generating action potentials form dynamically in the brain through mechanisms that temporarily alter pre- and post-synaptic mechanisms. These mechanisms give raise to specific constellations of synaptic weights he refers to as synapsembles.
More recent research from the same group, however, has shown that MEC responses gradually diminish when the hippocampus is inhibited by \(\hbox {GABA}_\mathrm{A}\) receptor agonist muscimol. I return to these studies below.
The researchers, though, did find a distinct pattern across the entire MEC: the spacing of vertices and the size of the fields in which neurons responded expanded monotonically the further from the dorsal tip on the border with the perirhinal cortex (where the fields extended approximately 30 cm) to the ventral tip (3 m) (Brun et al. 2008). This mirrors the expansion of place fields as one moves along the dorsoventral axis of the hippocampus.
As with place cells, there is not a topographical mapping of these cells onto directions. These cells only respond to direction in the horizontal plane and are largely unaffected by pitch or roll of the animal’s head as well as its ongoing behavior. The pattern of activity is stable in a given environment over several days.
Using a task in which the animal had to make a correct choice to acquire a reward, Dudchenko and Taube (1997) showed that typically head direction cells rotated their firing fields, and then, and only then, did the animal perform the correct behavior. Other studies from the same laboratory have found more problematic relations between activity of head-direction cells and behavior (Golob et al. 2001).
When visual cues were removed, head direction and place cells both often rotated in unpredictable but correlated ways (Knierim et al. 1995).
The discovery of yet other kinds of cells also supports such an account. Independently, Solstad et al. (2008) and Savelli et al. (2008) discovered a type of cell in the dorsocaudal quarter of the MEC and adjacent parasubiculum that fires only when the rat is along one or sometimes several borders of an enclosure. Accordingly, they named these cells border cells. These cells typically fire along most of the length of a border, and if the enclosure is extended on the side of the border, their firing fields are likewise expanded. If a new wall is inserted parallel to the border, a new firing field emerges on the same side of that wall as the field at the enclosure boundary. When the walls are removed and the platform simply drops off at the previous boundary, the boundary cells continue to respond. The behavior of these cells is also not affected when one enclosure is substituted for another. Still another type of cell carrying spatial information, boundary vector cells, were initially predicted on the basis of computational models designed to account for the remapping of place fields as a result of stretching the enclosure in a given direction (O’Keefe and Burgess 1996). Individual cells were postulated to have receptive fields at different distances from the rat and the cells were hypothesized to fire whenever an enclosure boundary intersects that field, regardless of the direction in which the rat was moving. Lever et al. (2009) identified a population of such cells in the subiculum that fit this description. The firing of these cells was unaffected by such factors as the color or material of the boundary, and whether it was a wall or a drop. These cells do not remap in the wake of changes to enclosures that yield remapping of place cells or head direction cells. Recent research indicates that grid cells are also found in the presubiculum and the parasubiculum, where they are collocated with head direction and border cells (Boccara et al. 2010).
There is not space here to discuss research on how path integration might be performed. Most of this research has taken the form of developing computational models. Fundamentally, two modeling strategies have been pursued—oscillatory interference models in which the interference between multiple oscillators serves to track locations and attractor network models in which networks of interacting components create dynamically transformable attractors (see Giocomo et al. 2011).
The fact that place cells typically carried information about more than spatial location was already clear in the remapping studies described above. For some researchers, especially those also influenced by research on human memory that viewed the hippocampus as fundamental to the encoding of episodic memories, this suggested a view in which the hippocampus was not an abstract map but served to relate spatial information to other content related to events the animal had experienced (Eichenbaum et al. 1999).
This point is nicely made by Moser and Moser “Finally, it is important to realize that the hippocampal–parahippocampal circuit only forms a representation. Representations can only influence navigation to the extent that information about the animal’s location is transferred to brain regions involved in planning and initiating movement” (2008, p. 1151).
Burke et al. (2005) addressed specifically the question of whether spatial differentiation was registered first in PPC and communicated to the hippocampal regions, or whether it was first developed in the hippocampal regions and then influenced PPC. Projections to the hippocampus originate in the deeper layers of PPC while the projections from the hippocampus are received in the superficial layers. The researchers showed, using a analysis of gene expression, that the differential representation of an enclosure in different rooms affected the superficial layers of PPC, not the deep layers, showing that the processing of location in the hippocampus regulate PPC behavior, not vice versa.
This study shared the limitation that the mazes used were far less complex than the routes rats navigate in the wild. Thus, despite its strong evidence for preplay, it does not conclusively answer the question of what role replay and preplay might subserve in real world navigation. One supportive indication came from Davidson, Kloosterman, and Wilson’s (2009) demonstration that when rats are required to run on a straight track, ripple events get chained together for longer tracks. They suggested that this chaining might be mediated by reentrant processing between the hippocampus and entorhinal cortex, supporting the idea that replay during the SPW state might be a general mechanism employed in learning a navigational route.
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Acknowledgments
I conducted the research for this paper while a Fellow at the Institute for Advanced Studies at Hebrew University and their support is gratefully acknowledged. I thank members of the Mind and Brain Research Group at the Institute for productive discussions. I also received valuable feedback from audiences at the Center for Theoretical Neuroscience at the University of Waterloo, the Southern Society for Philosophy and Psychology, the University of Amsterdam, and the Rotman Institute at the University of Western Ontario.
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Bechtel, W. Investigating neural representations: the tale of place cells. Synthese 193, 1287–1321 (2016). https://doi.org/10.1007/s11229-014-0480-8
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DOI: https://doi.org/10.1007/s11229-014-0480-8