FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2028154221> ?p ?o ?g. }

• W2028154221 endingPage "245" @default.
• W2028154221 startingPage "227" @default.
• W2028154221 abstract "Perception is influenced both by the immediate pattern of sensory inputs and by memories acquired through prior experiences with the world. Throughout much of its illustrious history, however, study of the cellular basis of perception has focused on neuronal structures and events that underlie the detection and discrimination of sensory stimuli. Relatively little attention has been paid to the means by which memories interact with incoming sensory signals. Building upon recent neurophysiological/behavioral studies of the cortical substrates of visual associative memory, I propose a specific functional process by which stored information about the world supplements sensory inputs to yield neuronal signals that can account for visual perceptual experience. This perspective represents a significant shift in the way we think about the cellular bases of perception. Perception is influenced both by the immediate pattern of sensory inputs and by memories acquired through prior experiences with the world. Throughout much of its illustrious history, however, study of the cellular basis of perception has focused on neuronal structures and events that underlie the detection and discrimination of sensory stimuli. Relatively little attention has been paid to the means by which memories interact with incoming sensory signals. Building upon recent neurophysiological/behavioral studies of the cortical substrates of visual associative memory, I propose a specific functional process by which stored information about the world supplements sensory inputs to yield neuronal signals that can account for visual perceptual experience. This perspective represents a significant shift in the way we think about the cellular bases of perception. You cannot count the number of bats in an inkblot because there are none. And yet a man—if he be “bat-minded”—may “see” several. (Gregory Bateson, 1972Bateson G. Steps to an Ecology of Mind: Collected Essays in Anthropology, Psychiatry, Evolution, and Epistemology. University of Chicago Press, Chicago1972Google Scholar) It should come as no surprise that what you see is not determined solely by the patterns of light that fall upon your retinae. Indeed, that visual perception is more than meets the eye has been understood for centuries, and there are several extraretinal factors known to interact with the incoming sensory data to yield perceptual experience. Perhaps foremost among these factors is information learned from our prior encounters with the visual world—our memories—which enables us to infer the cause, category, meaning, utility, and value of retinal images. By this process, the inherent ambiguity and incompleteness of information in the image—what is out there? Have I seen it before? What does it mean? How is it used?—is overcome, nearly instantaneously and generally without awareness, to yield unequivocal and behaviorally informative percepts. How does this transformation occur, and what are the underlying neuronal structures and events? Viewed in the context of a hierarchy of visual processing stages, prior knowledge of the world is believed to be manifested as “top-down” neuronal signals that influence the processing of “bottom-up” sensory information arising from the retina. Although the primate visual system has been a subject of intense study in neurobiological experiments for a half-century now, the primary focus of this research has been on the processing of visual signals as they ascend bottom-up through various levels of the hierarchy. Thus, with the notable exception of work on visual attention (for review, see Reynolds and Chelazzi, 2004Reynolds J.H. Chelazzi L. Attentional modulation of visual processing.Annu. Rev. Neurosci. 2004; 27: 611-647Crossref PubMed Scopus (505) Google Scholar), the neuronal substrates of top-down influences on visual processing have only recently come under investigation. Several of these recent experiments specifically address the interactions between top-down signals that reflect visual memories and bottom-up signals that convey retinal image content. The results of these experiments call for a significant shift in the way we think about the neuronal processing of visual information, and they are the subject of this review. The first part of this review explores neuronal changes that parallel the acquisition of long-term memories of associations between visual stimuli, such as between a knife and fork or a train and its track. The second part considers neuronal events that correspond to memories recalled via such learned associations and the relationship of this recall to the phenomenon of visual imagery. Finally, evidence is presented for a specific functional process by which—in the prescient words of 19th century perceptual psychologist James Sully, 1888Sully J. Outlines of Psychology, with Special References to the Theory of Education. Appleton, New York1888Google Scholar—the mind “supplements a sense impression by an accompaniment or escort of revived sensations, the whole aggregate of actual and revived sensations being solidified or ‘integrated’ into the form of a percept.” The concept of association is fundamental to learning and memory. Although this point was appreciated by the Ancient Greeks, it was by way of John Locke, 1690Locke J. An Essay Concerning Human Understanding. Basset, London1690Google Scholar and the emergent Associationist philosophy that the content of the human mind became viewed as progressively accumulating and diversifying throughout one's lifetime via the “associations of ideas.” Locke defined “ideas” broadly, but the simplest form of idea consists of sensation itself. Indeed, the learning of associations between sensory stimuli is a pervasive feature of human cognition. Formally speaking, learned associations between sensory stimuli constitute acquired information about statistical regularities in the observer's environment, which may be highly beneficial for predicting and interpreting future sensory inputs. Learned associations also help define the semantic properties of stimuli, as the meaning of a stimulus can be found, in large part, in the other stimuli with which it is associated. Associative learning can take place with or without an observer's awareness. It may be the product of simple temporal coincidence of stimuli—your grandmother (stimulus 1) is always seated in her favorite chair (stimulus 2)—or it may be facilitated by conditional reinforcement—emotional rewards may strengthen, for example, an association between the face of your lover (stimulus 1) and the song that the jukebox played on your first date (stimulus 2). The neuronal bases of associative learning have been the subject of speculations and detailed theoretical accounts for well over 100 years. Many of these proposals have at their core an idea first advanced concretely by William James, 1890James W. Principles of Psychology. Henry Holt, New York1890Crossref Google Scholar: the behavioral learning of an association between two stimuli is accomplished by the establishment or strengthening of a functional connection between the neuronal representations of the associated stimuli. At some level, James' hypothesis must be correct, and it is useful to consider the implications of this idea for the neuronal representation of visual information. This can be done using a simple example based on a nervous system composed of two parallel visual information processing channels (Figure 1A ). These channels extend from the retina up through visual cortex and beyond. One channel is dedicated to the processing of stimulus A and the other stimulus B. The flow of information through these channels is largely feed-forward, but there exist weak lateral connections that provide limited opportunities for crosstalk between the two channels. Recordings of activity from the A neuron in visual cortex should reveal a high degree of selectivity for stimulus A, relative to B, simply attributable to the different routes by which the signals reach the recorded neuron. Now, suppose the subject in whose brain these two channels exist is trained to associate stimuli A and B, by repeated temporal pairing of the stimuli in the presence of reinforcement (Figure 1B). By the end of training, stimuli A and B are highly predictive of one another—in some sense A means B, and vice versa. The Jamesian hypothesis predicts that the neuronal correlate of this associative learning is the strengthening of crosstalk between the two channels (Figure 1C). Now recordings from the A neuron should reveal similar responses to stimuli A and B, because both channels now have comparable access (albeit via different routes) to the recorded neuron. Thus, according to this simple model, the predicted neuronal signature of associative learning in visual cortex is a convergence of response magnitudes—as A and B become associated, neurons initially responding selectively to one or the other of these stimuli will generalize to the associated stimulus. An explicit test of the Jamesian hypothesis was first conducted by Miyashita and colleagues (Sakai and Miyashita, 1991Sakai K. Miyashita Y. Neural organization for the long-term memory of paired associates.Nature. 1991; 354: 152-155Crossref PubMed Scopus (324) Google Scholar). These investigators trained monkeys to associate a large number of pairs of visual stimuli: A with B, C with D, etc. Following behavioral acquisition of the associations, recordings were made from isolated neurons in the inferior temporal (IT) cortex (Figure 2), a region known to be critical for visual object recognition and memory (see below). Sakai and Miyashita, 1991Sakai K. Miyashita Y. Neural organization for the long-term memory of paired associates.Nature. 1991; 354: 152-155Crossref PubMed Scopus (324) Google Scholar found that paired stimuli (e.g., A&B) elicited responses of similar magnitude, whereas stimuli that were not paired (e.g., A&C) elicited uncorrelated responses. This finding of “pair-coding” neurons provided seminal support for the Jamesian view, as the similar responses to paired stimuli were taken to be a consequence of the learning-dependent connections formed between the neuronal representations of these stimuli. To directly explore the emergence of pair-coding responses, Messinger et al., 2001Messinger A. Squire L.R. Zola S.M. Albright T.D. Neuronal representations of stimulus associations develop in the temporal lobe during learning.Proc. Natl. Acad. Sci. USA. 2001; 98: 12239-12244Crossref PubMed Scopus (84) Google Scholar recorded from IT neurons while monkeys learned new stimulus pairings. For many neurons, the pattern of stimulus selectivity changed incrementally as pair learning progressed: responses to paired stimuli became more similar and responses to stimuli that had not been paired became less similar. The time course of this “associative neuronal plasticity” matched the time course of learning and the presence of neuronal changes depended upon whether learning actually occurred (i.e., if the monkey failed to learn new pairings, neuronal selectivity did not change). A snapshot of the Messinger et al., 2001Messinger A. Squire L.R. Zola S.M. Albright T.D. Neuronal representations of stimulus associations develop in the temporal lobe during learning.Proc. Natl. Acad. Sci. USA. 2001; 98: 12239-12244Crossref PubMed Scopus (84) Google Scholar results taken at the end of training reveals a pattern of neuronal selectivity that closely matches the findings of Sakai and Miyashita, 1991Sakai K. Miyashita Y. Neural organization for the long-term memory of paired associates.Nature. 1991; 354: 152-155Crossref PubMed Scopus (324) Google Scholar. The emergence of pair-coding responses in IT cortex supports the conclusion that learning strengthens connectivity between the relevant neuronal representations. That enhancement of connectivity may be regarded as the process of associative memory formation, the product of which is a neuronal state that captures the memory, i.e., the memory trace. This is precisely the interpretation that Miyashita and colleagues (e.g., Miyashita, 1993Miyashita Y. Inferior temporal cortex: where visual perception meets memory.Annu. Rev. Neurosci. 1993; 16: 245-263Crossref PubMed Google Scholar), and subsequently Messinger et al., 2001Messinger A. Squire L.R. Zola S.M. Albright T.D. Neuronal representations of stimulus associations develop in the temporal lobe during learning.Proc. Natl. Acad. Sci. USA. 2001; 98: 12239-12244Crossref PubMed Scopus (84) Google Scholar, have applied to the finding of pair-coding neurons in IT cortex, and it is consistent with neuropsychological data that identifies IT cortex as a long-term repository of visual memories (see below). Visual paired association learning is dependent upon the integrity of the hippocampus and cortical areas of the medial temporal lobe (MTL) (Murray et al., 1993Murray E.A. Gaffan D. Mishkin M. Neural substrates of visual stimulus-stimulus association in rhesus monkeys.J. Neurosci. 1993; 13: 4549-4561PubMed Google Scholar). These areas, which include the entorhinal, perirhinal and parahippocampal cortices, receive inputs from and are a source of feedback to IT cortex (see Figure 2; Webster et al., 1991Webster M.J. Ungerleider L.G. Bachevalier J. Connections of inferior temporal areas TE and TEO with medial temporal-lobe structures in infant and adult monkeys.J. Neurosci. 1991; 11: 1095-1116PubMed Google Scholar). The learning impairment following MTL lesions appears to be one of memory formation and the MTL areas are thus, under normal conditions, believed to exert their influence by enabling structural reorganization of local circuits in the presumed site of storage, i.e., IT cortex (Miyashita, 1993Miyashita Y. Inferior temporal cortex: where visual perception meets memory.Annu. Rev. Neurosci. 1993; 16: 245-263Crossref PubMed Google Scholar, Squire et al., 2004Squire L.R. Stark C.E. Clark R.E. The medial temporal lobe.Annu. Rev. Neurosci. 2004; 27: 279-306Crossref PubMed Scopus (1194) Google Scholar, Squire and Zola-Morgan, 1991Squire L.R. Zola-Morgan S. The medial temporal lobe memory system.Science. 1991; 253: 1380-1386Crossref PubMed Google Scholar). This hypothesis is supported by the finding that MTL lesions also eliminate the formation of pair-coding responses in IT cortex (Higuchi and Miyashita, 1996Higuchi S. Miyashita Y. Formation of mnemonic neuronal responses to visual paired associates in inferotemporal cortex is impaired by perirhinal and entorhinal lesions.Proc. Natl. Acad. Sci. USA. 1996; 93: 739-743Crossref PubMed Scopus (149) Google Scholar). Exactly how MTL regions contribute to the strengthening of connections between the neuronal representations of paired stimuli—with the attendant associative learning and neuronal response changes—is unknown. There are, nonetheless, good reasons to suspect the involvement of a Hebbian mechanism for enhancement of synaptic efficacy. Specifically, the temporal coincidence of stimuli during learning may cause coincident patterns of neuronal activity, which may lead, in turn, to a strengthening of synaptic connections between the neuronal representations of the paired stimuli (e.g., Yakovlev et al., 1998Yakovlev V. Fusi S. Berman E. Zohary E. Inter-trial neuronal activity in inferior temporal cortex: a putative vehicle to generate long-term visual associations.Nat. Neurosci. 1998; 1: 310-317Crossref PubMed Google Scholar). This conclusion is supported by the finding that associative plasticity in IT cortex is correlated with the appearance of molecular-genetic markers for synaptic plasticity: mRNAs encoding for brain-derived neurotrophic factor (BDNF) and for the transcription factor zif268 (Miyashita et al., 1998Miyashita Y. Kameyama M. Hasegawa I. Fukushima T. Consolidation of visual associative long-term memory in the temporal cortex of primates.Neurobiol. Learn. Mem. 1998; 70: 197-211Crossref PubMed Scopus (51) Google Scholar, Tokuyama et al., 2000Tokuyama W. Okuno H. Hashimoto T. Xin Li Y. Miyashita Y. BDNF upregulation during declarative memory formation in monkey inferior temporal cortex.Nat. Neurosci. 2000; 3: 1134-1142Crossref PubMed Scopus (94) Google Scholar). BDNF is known to play a role in activity-dependent synaptic plasticity (Lu, 2003Lu B. BDNF and activity-dependent synaptic modulation.Learn. Mem. 2003; 10: 86-98Crossref PubMed Scopus (449) Google Scholar). zif268 is a transcriptional regulator that leads to gene products necessary for structural changes that underlie plasticity (Knapska and Kaczmarek, 2004Knapska E. Kaczmarek L. A gene for neuronal plasticity in the mammalian brain: Zif268/Egr-1/NGFI-A/Krox-24/TIS8/ZENK?.Prog. Neurobiol. 2004; 74: 183-211Crossref PubMed Scopus (170) Google Scholar). The inferior temporal cortex was chosen as the initial target for study of associative neuronal plasticity for a number of reasons. This region of visual cortex was, for many years, termed “association cortex.” Although this designation originally reflected the belief that the temporal lobe represents a point at which information from different sensory modalities is associated (Flechsig, 1876Flechsig P. Die Leitungsbahnen im Gehirn and Ruckenmard des Menschen auf grund entwicklungsgeschichtlicher untersuchungen. Englemann, Leipzig, Germany1876Google Scholar), the term was later used to refer, more generally, to the presumed site of Locke's “association of ideas.” This view received early support from neuropsychological studies demonstrating that temporal lobe lesions in both humans and monkeys selectively impair the ability to recognize visual objects, while leaving basic visual sensitivities intact (Alexander and Albert, 1983Alexander M.P. Albert M.I. The anatomical basis of visual agnosia.in: Kertesz A. Localization in Neuropsychology. Academic Press, New York1983Google Scholar, Brown and Schafer, 1888Brown S. Schafer E.S. An investigation into the functions of the occipital and temporal lobes of the monkey's brain.Philos. Trans. R. Soc. Lond. B Biol. Sci. 1888; 179: 303-327Crossref Google Scholar, Kluver and Bucy, 1939Kluver H. Bucy P.C. Preliminary analysis of functions of the temporal lobes in monkeys.Arch. Neurol. Psychiatry. 1939; 42: 979-1000Crossref Google Scholar; Lissauer, 1988Lissauer H. A case of visual agnosia with a contribution to theory. M. Jackson, trans.Cogn. Neuropsychol. 1988; 5: 157-192Crossref Google Scholar). Along the same lines, the classic explorations of the neurosurgeon Wilder Penfield (Penfield and Perot, 1963Penfield W. Perot P. The brain's record of auditory and visual experience. a final summary and discussion.Brain. 1963; 86: 595-696Crossref PubMed Scopus (453) Google Scholar) revealed that electrical stimulation of the human temporal lobe commonly elicits reports of visual memories. The anatomical connections of IT cortex also support a role in object recognition and visual memory (Figure 2). IT cortex lies at the pinnacle of the ventral cortical visual processing stream and its neurons receive convergent projections from many visual areas at lower ranks, thus affording integration of information from a variety of visual submodalities (Desimone et al., 1980Desimone R. Fleming J. Gross C.G. Prestriate afferents to inferior temporal cortex: an HRP study.Brain Res. 1980; 184: 41-55Crossref PubMed Scopus (74) Google Scholar, Ungerleider, 1984Ungerleider L.G. Constrasts between the cortiocortical pathways for pattern and spatial vision.in: Chagas C. Study Group on Pattern Recognition Mechanisms. Pontifical Academy of Sciences, Vatican City1984Google Scholar). As noted above, IT cortex is also reciprocally connected with MTL structures that are critical for acquisition of declarative memories (Milner, 1972Milner B. Disorders of learning and memory after temporal lobe lesions in man.Clin. Neurosurg. 1972; 19: 421-446PubMed Google Scholar, Mishkin, 1982Mishkin M. A memory system in the monkey.Philos. Trans. R. Soc. Lond. B Biol. Sci. 1982; 298: 83-95Crossref PubMed Google Scholar, Murray et al., 1993Murray E.A. Gaffan D. Mishkin M. Neural substrates of visual stimulus-stimulus association in rhesus monkeys.J. Neurosci. 1993; 13: 4549-4561PubMed Google Scholar, Squire and Zola-Morgan, 1991Squire L.R. Zola-Morgan S. The medial temporal lobe memory system.Science. 1991; 253: 1380-1386Crossref PubMed Google Scholar). Finally, the visual response properties of IT neurons, which have been explored in much detail over the past 40 years, also exhibit features that suggest a role in object recognition and visual memory (for review see Gross et al., 1985Gross C.G. Desimone R. Albright T.D. Schwartz E.L. Inferior temporal cortex and pattern recognition.in: Chagas C. Study Group on Pattern Recognition Mechanisms. Pontifica Academia Scientiarum, Vatican City1985: 179-200Crossref Google Scholar, Miyashita, 1993Miyashita Y. Inferior temporal cortex: where visual perception meets memory.Annu. Rev. Neurosci. 1993; 16: 245-263Crossref PubMed Google Scholar). Most importantly, IT neurons are known to respond selectively to complex objects—often those with some behavioral significance to the observer, such as faces (Desimone et al., 1984Desimone R. Albright T.D. Gross C.G. Bruce C.J. Stimulus-selective properties of inferior temporal neurons in the macaque.J. Neurosci. 1984; 4: 2051-2062PubMed Google Scholar, Gross et al., 1969Gross C.G. Bender D.B. Rocha-Miranda C.E. Visual receptive fields of neurons in inferotemporal cortex of the monkey.Science. 1969; 166: 1303-1306Crossref PubMed Google Scholar). Based on this collective body of evidence, it would seem that IT cortex is unique among visual areas and strongly implicated as a storage site for long-term associative memories. Yet, there are reasons to suspect that associative neuronal plasticity may be a general property of sensory cortices. Evidence for this comes in part from functional brain imaging studies that have found learning-dependent activity changes in early cortical visual areas (e.g., Shulman et al., 1999Shulman G.L. Ollinger J.M. Akbudak E. Conturo T.E. Snyder A.Z. Petersen S.E. Corbetta M. Areas involved in encoding and applying directional expectations to moving objects.J. Neurosci. 1999; 19: 9480-9496PubMed Google Scholar, Wheeler et al., 2000Wheeler M.E. Petersen S.E. Buckner R.L. Memory's echo: vivid remembering reactivates sensory-specific cortex.Proc. Natl. Acad. Sci. USA. 2000; 97: 11125-11129Crossref PubMed Google Scholar). Motivated by these findings, Schlack and Albright, 2007Schlack A. Albright T.D. Remembering visual motion: neural correlates of associative plasticity and motion recall in cortical area MT.Neuron. 2007; 53: 881-890Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar explored the possibility that associative learning might influence response properties in the middle temporal visual area (area MT), which occupies a relatively early position in the cortical visual processing hierarchy (Ungerleider and Mishkin, 1979Ungerleider L.G. Mishkin M. The striate projection zone in the superior temporal sulcus of Macaca mulatta: location and topographic organization.J. Comp. Neurol. 1979; 188: 347-366Crossref PubMed Google Scholar). In an experiment that represents a simple analog to the paired-association learning studies of Sakai and Miyashita, 1991Sakai K. Miyashita Y. Neural organization for the long-term memory of paired associates.Nature. 1991; 354: 152-155Crossref PubMed Scopus (324) Google Scholar and Messinger et al., 2001Messinger A. Squire L.R. Zola S.M. Albright T.D. Neuronal representations of stimulus associations develop in the temporal lobe during learning.Proc. Natl. Acad. Sci. USA. 2001; 98: 12239-12244Crossref PubMed Scopus (84) Google Scholar, Schlack and Albright, 2007Schlack A. Albright T.D. Remembering visual motion: neural correlates of associative plasticity and motion recall in cortical area MT.Neuron. 2007; 53: 881-890Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar trained monkeys to associate directions of stimulus motion with stationary arrows. Thus, for example, monkeys learned that an upward-pointing arrow was associated with a pattern of dots moving in an upward direction, a downward arrow was associated with downward motion, etc. (Figures 3A and 3B ). Moving stimuli were used for this training because it is well known that such stimuli elicit robust responses from the vast majority of neurons in cortical visual area MT (Albright, 1984Albright T.D. Direction and orientation selectivity of neurons in visual area MT of the macaque.J. Neurophysiol. 1984; 52: 1106-1130PubMed Google Scholar). In macaque monkeys, where it has been most intensively studied, area MT is a small cortical region (Figure 2) that lies posteriorly along the lower bank of the superior temporal sulcus (Gattass and Gross, 1981Gattass R. Gross C.G. Visual topography of striate projection zone (MT) in posterior superior temporal sulcus of the macaque.J. Neurophysiol. 1981; 46: 621-638PubMed Google Scholar) and which receives direct input from primary visual cortex (Ungerleider and Mishkin, 1979Ungerleider L.G. Mishkin M. The striate projection zone in the superior temporal sulcus of Macaca mulatta: location and topographic organization.J. Comp. Neurol. 1979; 188: 347-366Crossref PubMed Google Scholar). MT neurons are highly selective for the direction of stimulus motion, and the area is believed to be a key component of the neural substrates of visual motion perception (for review, see Albright, 1993Albright T.D. Cortical processing of visual motion.in: Wallman J. Miles F.A. Visual Motion and its Use in the Stabilization of Gaze. Elsevier, Amsterdam1993: 177-201Google Scholar). If MT neurons have potential for associative plasticity similar to that seen in IT cortex, the behavioral pairing of motion directions with arrow directions should lead to a convergence of responses to the paired stimuli, overtly detectable in MT as emergent responses to the arrows. Moreover, those responses should be tuned for arrow direction, and the form of that tuning should depend on the specific associations learned. Schlack and Albright, 2007Schlack A. Albright T.D. Remembering visual motion: neural correlates of associative plasticity and motion recall in cortical area MT.Neuron. 2007; 53: 881-890Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar tested these hypotheses by recording from MT neurons after the motion-arrow associations were learned. Many MT neurons exhibited selectivity for the direction of the static arrow—a property not seen prior to learning, and seemingly heretical to the accepted view that MT neurons are primarily selective for visual motion. Moreover, for individual neurons, the arrow-direction tuning curve was a close match to the motion-direction tuning curve (Figures 3C and 3D). To confirm that the emergent responses to arrows reflected the learned association with motions rather than specific physical attributes of the arrow stimulus, Schlack and Albright, 2007Schlack A. Albright T.D. Remembering visual motion: neural correlates of associative plasticity and motion recall in cortical area MT.Neuron. 2007; 53: 881-890Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar trained a second monkey on the opposite associations (e.g., upward motion associated with downward arrow). As expected from the learning hypothesis, the emergent tuning again reflected the association (e.g., if the preferred direction for motion was upward, the preferred direction for the arrow was downward) rather than the specific properties of the associated stimulus. On the surface of things, the plasticity seen in area MT appears identical to that previously observed in IT cortex: the neuronal response change is learning-dependent and can be characterized as a convergence of responses to the paired stimuli. One might suppose, therefore, that the phenomenon in MT also reflects mechanisms for long-term memory storage. There are, however, several reasons to believe that the plasticity observed in MT reflects rather different functions and mechanisms. To begin with, IT and MT cortices are distinguished from one another by the availability of substrates for long-term memory storage. In the IT experiments described above the paired stimuli (arbitrary complex objects) are in all cases plausibly represented by separate groups of IT neurons, which means that connections between those representations could be forged locally within IT cortex. The same is not true for area MT, as there exists no native selectivity for stationary arrows (or for most other nonmoving stimuli). IT and MT are also distinguished from one another by the presence versus absence of feedback from cortical areas of the medial temporal lobe (see Figure 2). As noted above, these MTL areas are essential for learning of visual paired-associates (presumably also including those between arrows and motions), and they are believed to enable memory formation via selective modification of local circuits at the targets of their feedback projections. IT cortex is one of those targets, but area MT is not (Suzuki and Amaral, 1994Suzuki W.A. Amaral D.G. Perirhinal and parahippocampal cortices of the macaque monkey: cortical afferents.J. Comp. Neurol. 1994; 350: 497-533Crossref PubMed Scopus (643) Google Scholar). Although it remains to be seen whether MTL lesions block the emergence of pair-coding responses in area MT, as they do in IT cortex, the evident connectional dissimilarities between MT and IT suggest that the associative neuronal plasticity in MT is not the basis of memory storage. If not memory storage, what then is represented by the observed learning-dependent responses in MT? One possibility is that they simply represent the properties of the retinal stimulus, i.e." @default.
• W2028154221 created "2016-06-24" @default.
• W2028154221 creator A5083326500 @default.
• W2028154221 date "2012-04-01" @default.
• W2028154221 modified "2023-10-17" @default.
• W2028154221 title "On the Perception of Probable Things: Neural Substrates of Associative Memory, Imagery, and Perception" @default.
• W2028154221 cites W1278754391 @default.
• W2028154221 cites W1501173573 @default.
• W2028154221 cites W1526492552 @default.
• W2028154221 cites W1580700358 @default.
• W2028154221 cites W1691513537 @default.
• W2028154221 cites W1837967720 @default.
• W2028154221 cites W1842265643 @default.
• W2028154221 cites W1910834158 @default.
• W2028154221 cites W191413725 @default.
• W2028154221 cites W1937144007 @default.
• W2028154221 cites W1964109094 @default.
• W2028154221 cites W1969166506 @default.
• W2028154221 cites W1971162173 @default.
• W2028154221 cites W1974165805 @default.
• W2028154221 cites W1975140269 @default.
• W2028154221 cites W1981692923 @default.
• W2028154221 cites W1986708110 @default.
• W2028154221 cites W1986856036 @default.
• W2028154221 cites W1987615735 @default.
• W2028154221 cites W1992699631 @default.
• W2028154221 cites W1996821770 @default.
• W2028154221 cites W2001501438 @default.
• W2028154221 cites W2012252610 @default.
• W2028154221 cites W2014555772 @default.
• W2028154221 cites W2014621385 @default.
• W2028154221 cites W2015394094 @default.
• W2028154221 cites W2016253504 @default.
• W2028154221 cites W2017960973 @default.
• W2028154221 cites W2019665647 @default.
• W2028154221 cites W2019816574 @default.
• W2028154221 cites W2020778578 @default.
• W2028154221 cites W2022613290 @default.
• W2028154221 cites W2024602467 @default.
• W2028154221 cites W2028935233 @default.
• W2028154221 cites W2029264248 @default.
• W2028154221 cites W2033321575 @default.
• W2028154221 cites W2033916656 @default.
• W2028154221 cites W2035611197 @default.
• W2028154221 cites W2036543993 @default.
• W2028154221 cites W2037118457 @default.
• W2028154221 cites W2037781104 @default.
• W2028154221 cites W2040124307 @default.
• W2028154221 cites W2040996971 @default.
• W2028154221 cites W2042899905 @default.
• W2028154221 cites W2044463604 @default.
• W2028154221 cites W2045133472 @default.
• W2028154221 cites W2045307802 @default.
• W2028154221 cites W2045310765 @default.
• W2028154221 cites W2047495330 @default.
• W2028154221 cites W2050600696 @default.
• W2028154221 cites W2052301699 @default.
• W2028154221 cites W2053518358 @default.
• W2028154221 cites W2055443477 @default.
• W2028154221 cites W2058947925 @default.
• W2028154221 cites W2059049804 @default.
• W2028154221 cites W2059450483 @default.
• W2028154221 cites W2062103770 @default.
• W2028154221 cites W2062752228 @default.
• W2028154221 cites W2064889904 @default.
• W2028154221 cites W2065235698 @default.
• W2028154221 cites W2068718477 @default.
• W2028154221 cites W2069094890 @default.
• W2028154221 cites W2070191061 @default.
• W2028154221 cites W2070246276 @default.
• W2028154221 cites W2071490573 @default.
• W2028154221 cites W2075243918 @default.
• W2028154221 cites W2075412993 @default.
• W2028154221 cites W2076078009 @default.
• W2028154221 cites W2076217198 @default.
• W2028154221 cites W2076425653 @default.
• W2028154221 cites W2079951912 @default.
• W2028154221 cites W2082008231 @default.
• W2028154221 cites W2086556872 @default.
• W2028154221 cites W2087113014 @default.
• W2028154221 cites W2090399446 @default.
• W2028154221 cites W2093040540 @default.
• W2028154221 cites W2094576805 @default.
• W2028154221 cites W2095047274 @default.
• W2028154221 cites W2097865389 @default.
• W2028154221 cites W2099107381 @default.
• W2028154221 cites W2103591963 @default.
• W2028154221 cites W2106235350 @default.
• W2028154221 cites W2108992228 @default.
• W2028154221 cites W2112886416 @default.
• W2028154221 cites W2113044517 @default.
• W2028154221 cites W2115471261 @default.
• W2028154221 cites W2117213592 @default.
• W2028154221 cites W2117215414 @default.
• W2028154221 cites W2118895150 @default.
• W2028154221 cites W2120852200 @default.
• W2028154221 cites W2122249653 @default.
• W2028154221 cites W2127094700 @default.

• next