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Neuron specialization in the Visual System

Neuron specialization in the Visual System


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Can someone point me to a good resource to explain how neurons in the visual system become sensitive to visual features? I understand that specific neurons fire for things like direction of motion, orientation, etc. How does this specialization get “programmed in” during development? I'm sure that training from experience is key but how does a specific neuron become specialized for a feature and its neighbor becomes specialized for another feature?

I'm a noob and curious how different neurons specialize to fire for simple features in the LGN but more complex features in higher visual areas. Just go straight to Huble & Wiesel?


Figure Locations

Figure 2 Parameter optimization and the log-posterior surface. (a) An illustration of how the log-posterior (LP) (vertical axis, also colored from blue to red) might depend on two parameters of a nonlinear model. An optimization path is shown, from the initial parameters Θ0 and following the gradient (black arrow) to an LP-maximum at Θbest. This LP-surface pictured is relatively well-behaved, although gradient ascent could get stuck in the lower maximum (right) with some initial conditions. (b) A demonstration of how the model predictions (green and red) compare with the observed data (black) as the LP of the model improves. (c) A typical convex LP-surface, such as that associated with generalized linear models (GLMs). Such a surface only has a single local maximum, which can be found with any initial conditions. (d) A projection of the initial model filters [100 random initializations (solid points)] and final filters [after model optimization (hollow points)] in the filter space for an ON-OFF cell simulation (from McFarland et al. 2013, supplemental material). The optimization always converged to two positions in parameter space with equal LP, corresponding to the true filters in either order. Thus, while there is more than one local maximum of the LP, they are both correct answers, and the LP-surface is well-behaved.


Multiple Target Broadcasting

Until now, it had remained unclear as to whether information transfer from primary visual cortex was largely &ldquoone neuron &ndash one target area,&rdquo or if individual neurons distributed their signals across multiple downstream areas.

While the research, conducted by neuroscientists from the Sainsbury Wellcome Centre for Neural Circuits and Behaviour, in conjunction with Cold Spring Harbor and Biozentrum, confirmed the existence of dedicated projections to certain cortical areas, the scientists found that these were the exception and that the majority of primary visual cortex neurons broadcast information to multiple targets.

&ldquoOur findings reveal that individual neurons in the visual cortex project to several targets in the neocortex. This means that their signals are distributed widely and that individual neurons contribute to multiple parallel computations across the neocortex,"

Justus M. Kebschull, one of the first authors on the paper at Cold Spring Harbor said.


Thomas Sprague

Thomas (Tommy) Sprague received his BA in Cognitive Sciences from Rice University in Houston, TX in 2010 and his PhD in Neurosciences with a Specialization in Computational Neurosciences from the University of California, San Diego in 2016. His graduate work with John Serences sought to develop and apply novel multivariate analysis methods to human neuroimaging techniques to understand how neural systems represent information in support of dynamic behavioral goals. Prior to joining the faculty at UCSB, Dr. Sprague worked as a postdoctoral fellowship with Clayton Curtis and Wei Ji Ma studying how neural systems represent both the contents of visual working memory, but also their ‘uncertainty’, by building new multivariate analysis methods.

Research

We often encounter the same scene (say, the inside of your refrigerator) with different behavioral goals (pouring a glass of water or finding a piece of cake). My lab is interested in how the actions we wish to perform impact neural representations of the world. When you’re looking for a piece of cake, how does that change the representation of the other items in the refrigerator? How well can you remember the color of the pitcher, or the location of the soda can, when you close the door? My lab combines computational neuroimaging (fMRI EEG), behavioral testing (psychophysics eyetracking), and model-based analysis techniques (voxel-wise modeling inverted encoding models) to shed light on how brain networks support and constrain our ability to represent information about our environment. Because of its ease of access to contemporary noninvasive neuroimaging tools, we use the visual system as a model system for evaluating neural representations and testing hypotheses about neural information processing.


Shared Flashcard Set

tendency to respond to a stimulus that resembles one involved in the original conditioning.

stimulus resembles CS elicts a CR

CC: responding differently to two+ stimuli.

stimulus similar to the CS fails to evoke a CR

OC: response occurs in the presence of one stimulus but not the other. similar stimuli that differ from it on some dimension

weakening and eventual disappearance of learned response

CS is no longer paired with the US

response more likely to occur.

response less likely to occur.

response becomes more or less likely to occur, depending on CONSEQUENCES

schedule of reinforcement

learning new responses by observing the behavior of another rather than throug direct experience.

gathers and processes info

interprets -- responds to stimuli

coordinateds cells to respond

collection of neurons and supportive tissue running down the center of the back

bridge between brain and body

can be controlled subconsciously and by brain

peripheral nervous system

portion of nervous system outside the brain and spinal cord, including sensory and motor nerves

handles CNS's input and output

nerves that are connected to sensory receptors

regulates internal organs and glands

sympathetic nervous system

parasympatheic nervous system

support, nurture, and insulate neurons

remove debris when neurons die

formation and maintenance of neural connections

modify neural functioning

receive info from other neurons and transmit toward cell body

neuron's extending fiber that conducts impulses away from the cell body

transmits to other neurons

can be insulated by myelin sheath

reduces confusion between nearby neurons

change in electrical potential between inside and outside of neuron.

action potential: brief change in electrical voltage when neuron is stimulated as an electrical impulse

end of an exon from which the axon releases its message

several synaptic vesicles containing a neutrotransmitter

(chemical that is released by a transmitting neuron at the synapse and that alters the activit of a receiving neuron)

site where transmission of nerve impulses from one nerve to another cell occurs


THE HUMAN VISUAL CORTEX

▪ Abstract The discovery and analysis of cortical visual areas is a major accomplishment of visual neuroscience. In the past decade the use of noninvasive functional imaging, particularly functional magnetic resonance imaging (fMRI), has dramatically increased our detailed knowledge of the functional organization of the human visual cortex and its relation to visual perception. The fMRI method offers a major advantage over other techniques applied in neuroscience by providing a large-scale neuroanatomical perspective that stems from its ability to image the entire brain essentially at once. This bird's eye view has the potential to reveal large-scale principles within the very complex plethora of visual areas. Thus, it could arrange the entire constellation of human visual areas in a unified functional organizational framework. Here we review recent findings and methods employed to uncover the functional properties of the human visual cortex focusing on two themes: functional specialization and hierarchical processing.


Andrey R. Nikolaev and Cees van Leeuwen were aided by an Odysseus grant from the Flemish Organization for Science (FWO). We thank Dr. Steeve Laquitaine, Dr. Justin L. Gardner and Dr. Anthony J. DeCostanzo for helpful discussions. We also thank the editor for her help and availability throughout the review process and the reviewers for their constructive criticism and helpful suggestions/comments on the manuscript.

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Keywords : color, form, segregation, integration, distributed processing, mixed selective cells, high dimensional code, complex selectivity

Citation: Rentzeperis I, Nikolaev AR, Kiper DC and van Leeuwen C (2014) Distributed processing of color and form in the visual cortex. Front. Psychol. 5:932. doi: 10.3389/fpsyg.2014.00932

Received: 14 June 2013 Paper pending published: 08 July 2013
Accepted: 05 August 2014 Published online: 27 October 2014.

Galina Paramei, Liverpool Hope University, UK

Ruth Rosenholtz, Massachusetts Institute of Technology, USA
Konstantinos Moutoussis, National and Kapodistrian University of Athens, Greece

Copyright © 2014 Rentzeperis, Nikolaev, Kiper and van Leeuwen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with theseterms.


GENERAL DISCUSSION

In this study, ERPs to visually presented letters and numbers were examined to investigate the time course of the dissociation between the two categories. As we hypothesized, a dissociation was observed between the ERP traces evoked by letters and by numbers at both the left and right occipital-temporal sites. Specifically, letters elicited significantly greater N1 amplitudes in the left hemisphere, whereas numbers elicited significantly greater N1 amplitudes in the right hemisphere, with otherwise very similar scalp topography elsewhere. Moreover, both letter/number strings and individual letters/numbers elicited similar patterns of dissociation at the N1 level, implying that the observed results are largely independent of the length of the character string. The finding of these electrophysiological effects at this very early latency suggests that adult human visual cortex is tuned to differentially process letters and numbers at one of the earliest encoding levels in the visual stream.

It should be noted that letters and numbers are both highly familiar stimuli. In addition, we minimized possible phonological or semantic processing by only presenting consonant letters, and we minimized top–down processing by using an orthogonal arrow detection task with rapid and randomized stimulus presentation. Thus, it is unlikely that the dissociations observed in the ERP responses to the letters and numbers were because of differential top–down attention or high-level cognitive strategies for these stimuli.

Greater N1 amplitude for letters than numbers at the left occipital temporal sites is consistent with the proposal that the left inferior temporal area in the occipital-temporal sulcus hosts a region specialized in visual word form processing (McCandliss et al., 2003). In fact, the characteristics of the left-lateralized orthography-evoked N1 closely match the activity in the visual word form area typically found in fMRI studies (Dien, 2009). Brem et al. (2006), using both fMRI and ERP techniques, found that fMRI activation in the visual word form area reliably correlated with N1 amplitude, suggesting that the N1 evoked by orthographic stimuli is closely related to the activation in the occipital-temporal cortex. Using a source localization analysis, Maurer et al. (2005) suggested that the inferior occipital N1 evoked by letters and words arises from the basal posterior temporal source cluster in the fusiform gyrus. Along the same line, a magnetoencephalographic study by Tarkiainen et al. (1999) reported greater activity to noise-free words compared with high-noise words or noiseless symbol strings in the left occipital-temporal cortex at around 150 msec. Thus, the greater N1 to letters compared with numbers over left occipital sites is consistent with the idea that these N1 effects reflect an early sensitivity in the visual cortex to visual letter forms over other also highly familiar visual stimuli.

Analogously, the greater N1 to numbers than to letters over the right hemisphere suggests that the right occipital temporal cortex hosts a region that preferentially processes visual number forms. Visual processing of number symbols (e.g., the Arabic numerals tested in this study) has received relatively little attention in the field, so only a handful of studies have investigated the neural correlates of visual number processing. Dehaene (1996) showed that, when participants are engaged in a numerical comparison task, processing numbers in Arabic notation elicited more bilateral N1 activity compared with verbal notation, which elicited strictly left-lateralized N1 activity. This study provided one of the first hints about dissociable processes between letters and numbers. However, it did not allow a systematic comparison between visual processing of letters and numbers because there were differences in the physical characteristics of letters and numbers (e.g., number of characters) and because an explicit task was used that required numerical processing. A few recent studies have shown that some occipital temporal regions preferentially process numbers compared with other physically similar stimuli (Shum et al., 2013 Park, Hebrank, et al., 2012 Roux et al., 2008) and suggest that these extrastriate regions are tuned to respond to visual shapes of Arabic numerals. Of interest, a recent electrocorticographic study of EEG oscillatory activity found a focally localized brain area that was highly selective for the processing of Arabic numerals, and this region was most reliably found in the right inferior temporal gyrus anterior to the occipital temporal incisures in the majority of participants (Shum et al., 2013).

Although the ERP effects observed for strings of letters and numbers were similar to those observed for single letters and numbers, at least at the level of the N1, there were noteworthy differences between the two sets of responses. In particular, an ERP difference in the P2 was only observed in the strings condition, and the scalp distributions between string processing and single-character processing at the N1 level were slightly different (compare Figure 3 and Figure 6). These results suggest that there may be different underlying mechanisms for string processing compared with single character processing, an idea also supported by James et al. (2005).

Previous studies have shown that the phonological and semantic aspects of stimuli modulate left posterior ERPs after the initial visual encoding level reflected by the N1. For example, Dehaene (1995) in a lexical decision task found that ERP waveforms diverge between words and consonant strings or even between different categories of words, starting from around 200 msec from stimulus onset, with the ERP waveforms for words being more positive than those for consonant strings. Hauk et al. (2006), also using a lexical decision task, showed a marked difference in the P2 evoked by words compared with pseudowords (i.e., pronounceable nonwords), with pseudowords eliciting larger amplitudes. From a yet different standpoint, McCandliss et al. (1997) showed that the P2 magnitude difference between English words and consonant strings was much greater (with words eliciting a larger amplitude) in a semantic task compared with a passive viewing task. Furthermore, when the authors trained participants to associate a set of artificial words to meaningful objects, properties, and events, they observed a significant amplitude change in the P2 to these learned stimuli, but not in the N1. In contrast, no training-induced changes in the P2 were observed in untrained artificial words that matched in orthographic regularity to the trained artificial words. These studies suggest that linguistic aspects of the stimuli modulate brain responses at the level of the P2. According to this idea, our results showing a P2 amplitude difference between strings of letters and numbers, but not between single letters and numbers, suggest that the visual cortex may be implicitly extracting phonological or semantic information in some conditions but not others.

On the other hand, it is difficult to imagine that there is asymmetry in phonological or semantic processing between letters and numbers only in strings and not in single characters. Therefore, P2 differences in the left hemisphere may not be because of implicit phonological or semantic influences on visual processing in the context of our study. Instead, the P2 in the context of visual word form processing may imply a later stage of a hierarchy of local combination detectors (Dehaene et al., 2005). This theoretical model, inspired by neurophysiological models of invariant object recognition, proposes that there is a hierarchical organization whereby neurons detect patterns of visual stimuli of increasing complexity along a hierarchy. Whereas pools of neurons in the lower levels may most effectively process single characters, pools of neurons in the upper levels may most effectively process combinations of characters. Our data fit well with this proposal, as only letter strings, but not single letters, showed a greater P2 compared with numbers. Moreover, according to this idea, our results imply that only letter-string processing (and not number-string processing) in the left visual cortex is subject to this hierarchical organization.

It is also of interest in this study that false fonts elicited greater ERP amplitudes than both letters and numbers across multiple phases of processing. Although these false-font ERP patterns do not explain the hemispheric double dissociations between letters and numbers, which was the more central research question of this study, they may potentially provide important insights for generating further hypotheses about how the visual cortex processes unfamiliar stimuli differently from familiar stimuli. It should first be noted that some other previous studies have shown greater ERP responses, at least at the N1 range, to letters and words compared with false fonts, a pattern opposite from our findings (Stevens et al., 2013 Wong et al., 2005). In these studies, however, an identity 1-back task was used with much longer stimulus presentations and longer intertrial intervals compared with our study. Accordingly, one possibility is that the active encoding of known stimuli (e.g., native letters and words) via top–down attentional mechanisms may elicit greater N1 amplitudes than the active encoding of unknown or incomprehensible stimuli. In contrast, when top–down attentional or encoding strategies are minimized, such as in our study, there may be no selective enhancement of N1 for known stimuli.

Enhanced neural activity to false fonts in our study parallels previous fMRI findings (Park, Hebrank, et al., 2012 Vinckier et al., 2007) but also shows that this enhancement occurs very early (as early as the P1 at 100 msec) in the processing stream. The larger ERP responses to false fonts may potentially be explained by inefficiency in the template-matching process for unfamiliar stimuli. For instance, highly experienced familiar stimuli such as letters and numbers may be detected by generic feature detectors early in the visual processing stream and then get fed rapidly into subsequent processing levels for more focal letter and number areas separately in the two hemispheres. In contrast, greater activity around the P1 for the false fonts may reflect the need for more extensive encoding of these unfamiliar stimuli by the generic feature detectors at early levels, resulting in greater neural activity that may propagate through later phases of the processing stream. Thus, the overall enhancement of the neural response to false fonts could result from the extended activity of generic feature detectors that have little influence from previous experience with false fonts. Consistent with this idea, Park, Park, et al. (2012) in an fMRI study of monozygotic twins showed smaller experiential influence (i.e., unique environmental effects) in the neural response to false fonts compared with letters, although the magnitude of the neural response to false fonts was greater than to letters.

In summary, the two electrophysiological experiments presented here show that the human visual cortex exhibits hemispherically differentiated processing for two categories of culturally defined, otherwise arbitrary, symbols, and it does so very early in the processing stream. These findings complement a previous fMRI study (Park, Hebrank, et al., 2012) by showing precisely when during letter and number processing the dissociation occurs, as well as when the processing of both of these culturally defined stimulus types differentiate from physically similar but unfamiliar visual stimulus forms (false fonts), thus providing important temporal information that was not afforded by the fMRI study. Our results further suggest that the processing of letter and number strings utilizes neural pathways that are partially differentiated from those processing single characters of letters and numbers. These findings imply a major neural specialization in the early visual cortex driven by extensive experience that is unique to humans. Future studies should explore when in the developmental time frame this specialization occurs and how and why it comes about.


Recognition Memory [ edit | edit source ]

When you see an object, you know what the object is because you've seen it on a past occasion this is recognition memory. Not only do abnormalities to the ventral (what) stream of the visual pathway effect our ability to recognize an object but also the way in which an object is presented to us.

Familiarity [ edit | edit source ]

A mechanism that is context free in the sense that what we recognize just feels familiar rather than spending time trying to find in what context we know this object. ⎧] The ventro-lateral region of the frontal lobe is involved in memory encoding during incidental learning and then later maintaining and retrieving semantic memories. ⎧] Familiarity can induce perceptual processes different to those of unfamiliar objects which means that our perception of a finite amount of familiar objects is unique. ⎨] Deviations from typical viewpoints and contexts can affect the efficiency for which an object is recognized most effectively. ⎨] It was found that not only are familiar objects recognized more efficiently when viewed from a familiar viewpoint opposed to an unfamiliar one, but also this principle applies to novel objects. This deduces to the thought that representations of objects in our brain are organized in more of a familiar fashion of the objects observed in the environment. ⎨] Recognition is not only largely driven by object shape and/or views but also by dynamic information. ⎩] Familiarity can benefit the perception of dynamic point-light displays, moving objects, the sex of faces, and face recognition. ⎨]

Recollection [ edit | edit source ]

Recollection shares many similarities with familiarity however it is context dependent, requiring specific information from the inquired incident. ⎧]


Brain-wide tracing of single neurons reveals breadth of information transfer from visual cortex

An international collaboration of neuroscientists has today published a paper in Nature demonstrating the breadth of neural communication in visual cortex using a combination of methods for tracing the projections of individual neurons across the brain.

In classical models of the visual system, information flows from 'primary' visual cortex (V1) to more specialized, downstream areas that focus for example on image movement or image form. However, the details of how individual cells carry this information are not understood.

Professor Tom Mrsic-Flogel, one of the senior authors of the paper and project leader at Biozentrum, University of Basel and Director of the Sainsbury Wellcome Centre commented:

"Understanding the fine-scale anatomy by which individual neurons distribute signals to their targets is a crucial step for forging the relationship between neuronal structure and function."

Up until now, it had remained unclear as to whether information transfer from primary visual cortex was largely "one neuron -- one target area," or if individual neurons distributed their signals across multiple downstream areas.

While the research, conducted by neuroscientists from the Sainsbury Wellcome Centre for Neural Circuits and Behaviour, in conjunction with Cold Spring Harbor and Biozentrum, confirmed the existence of dedicated projections to certain cortical areas the scientists found that these were the exception and that the majority of primary visual cortex neurons broadcast information to multiple targets.

Justus M. Kebschull, one of the first authors on the paper at Cold Spring Harbor commented:

"Our findings reveal that individual neurons in the visual cortex project to several targets in the neocortex. This means that their signals are distributed widely and that individual neurons contribute to multiple parallel computations across the neocortex."

In the Nature paper, the team outline the two complementary methods they used to map the projection patterns. Firstly, they used whole-brain fluorescence-based axonal tracing by labelling neurons in the right visual cortex of each mouse with GFP and then imaging axonal projections by whole-brain serial two-photon tomography.

The Allen Mouse Brain Reference Atlas was then used to identify the areas in which axonal terminations were observed. The mouse primary visual cortex (V1) neurons were found to have a high degree of projectional diversity and most of the individual layer 2/3 neurons were found to distribute information to multiple areas rather than projecting to a single target. Such neurons were termed 'broadcasting neurons'.

Secondly, the researchers used high-throughput DNA sequencing of genetically barcoded neurons (MAPseq) to determine whether the broadcasting neurons were targeting cortical areas at random, or whether they preferentially target, or avoid, specific subsets of areas thereby indicating a higher-order structure.

Thousands of individual V1 neurons were uniquely labelled with random RNA sequences, in essence barcodes. Each labelled neuron then transports the barcode into its own axonal processes where they can be read out by high throughout sequencing of a dissected target area to determine the projection targets of that specific neuron to higher visual areas.

Professor Anthony Zador, another senior author and project leader at Cold Spring Harbor Laboratory, explains the revolutionary technique:

"The RNA sequences, or 'barcodes', that we deliver to individual neurons are unmistakably unique and this enables us to determine if individual neurons, as opposed to entire regions, are tailored to specific targets."

This technique revealed that the majority of V1 neurons project to higher visual areas in a non-random manner. Six projection motifs were identified that reflect several sub-classes of projection neurons for divergent information transfer from V1 to higher visual areas.

The researchers state that the results of the study "suggest a functional specialization of subpopulations of projection cells beyond 'one neuron -- one target area' mapping.

"The next piece of the puzzle will be to understand what each of these projection motifs does for visual processing and perception and how these long-range connectivity patterns are established during development," Professor Mrsic-Flogel concluded.


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GENERAL DISCUSSION

In this study, ERPs to visually presented letters and numbers were examined to investigate the time course of the dissociation between the two categories. As we hypothesized, a dissociation was observed between the ERP traces evoked by letters and by numbers at both the left and right occipital-temporal sites. Specifically, letters elicited significantly greater N1 amplitudes in the left hemisphere, whereas numbers elicited significantly greater N1 amplitudes in the right hemisphere, with otherwise very similar scalp topography elsewhere. Moreover, both letter/number strings and individual letters/numbers elicited similar patterns of dissociation at the N1 level, implying that the observed results are largely independent of the length of the character string. The finding of these electrophysiological effects at this very early latency suggests that adult human visual cortex is tuned to differentially process letters and numbers at one of the earliest encoding levels in the visual stream.

It should be noted that letters and numbers are both highly familiar stimuli. In addition, we minimized possible phonological or semantic processing by only presenting consonant letters, and we minimized top–down processing by using an orthogonal arrow detection task with rapid and randomized stimulus presentation. Thus, it is unlikely that the dissociations observed in the ERP responses to the letters and numbers were because of differential top–down attention or high-level cognitive strategies for these stimuli.

Greater N1 amplitude for letters than numbers at the left occipital temporal sites is consistent with the proposal that the left inferior temporal area in the occipital-temporal sulcus hosts a region specialized in visual word form processing (McCandliss et al., 2003). In fact, the characteristics of the left-lateralized orthography-evoked N1 closely match the activity in the visual word form area typically found in fMRI studies (Dien, 2009). Brem et al. (2006), using both fMRI and ERP techniques, found that fMRI activation in the visual word form area reliably correlated with N1 amplitude, suggesting that the N1 evoked by orthographic stimuli is closely related to the activation in the occipital-temporal cortex. Using a source localization analysis, Maurer et al. (2005) suggested that the inferior occipital N1 evoked by letters and words arises from the basal posterior temporal source cluster in the fusiform gyrus. Along the same line, a magnetoencephalographic study by Tarkiainen et al. (1999) reported greater activity to noise-free words compared with high-noise words or noiseless symbol strings in the left occipital-temporal cortex at around 150 msec. Thus, the greater N1 to letters compared with numbers over left occipital sites is consistent with the idea that these N1 effects reflect an early sensitivity in the visual cortex to visual letter forms over other also highly familiar visual stimuli.

Analogously, the greater N1 to numbers than to letters over the right hemisphere suggests that the right occipital temporal cortex hosts a region that preferentially processes visual number forms. Visual processing of number symbols (e.g., the Arabic numerals tested in this study) has received relatively little attention in the field, so only a handful of studies have investigated the neural correlates of visual number processing. Dehaene (1996) showed that, when participants are engaged in a numerical comparison task, processing numbers in Arabic notation elicited more bilateral N1 activity compared with verbal notation, which elicited strictly left-lateralized N1 activity. This study provided one of the first hints about dissociable processes between letters and numbers. However, it did not allow a systematic comparison between visual processing of letters and numbers because there were differences in the physical characteristics of letters and numbers (e.g., number of characters) and because an explicit task was used that required numerical processing. A few recent studies have shown that some occipital temporal regions preferentially process numbers compared with other physically similar stimuli (Shum et al., 2013 Park, Hebrank, et al., 2012 Roux et al., 2008) and suggest that these extrastriate regions are tuned to respond to visual shapes of Arabic numerals. Of interest, a recent electrocorticographic study of EEG oscillatory activity found a focally localized brain area that was highly selective for the processing of Arabic numerals, and this region was most reliably found in the right inferior temporal gyrus anterior to the occipital temporal incisures in the majority of participants (Shum et al., 2013).

Although the ERP effects observed for strings of letters and numbers were similar to those observed for single letters and numbers, at least at the level of the N1, there were noteworthy differences between the two sets of responses. In particular, an ERP difference in the P2 was only observed in the strings condition, and the scalp distributions between string processing and single-character processing at the N1 level were slightly different (compare Figure 3 and Figure 6). These results suggest that there may be different underlying mechanisms for string processing compared with single character processing, an idea also supported by James et al. (2005).

Previous studies have shown that the phonological and semantic aspects of stimuli modulate left posterior ERPs after the initial visual encoding level reflected by the N1. For example, Dehaene (1995) in a lexical decision task found that ERP waveforms diverge between words and consonant strings or even between different categories of words, starting from around 200 msec from stimulus onset, with the ERP waveforms for words being more positive than those for consonant strings. Hauk et al. (2006), also using a lexical decision task, showed a marked difference in the P2 evoked by words compared with pseudowords (i.e., pronounceable nonwords), with pseudowords eliciting larger amplitudes. From a yet different standpoint, McCandliss et al. (1997) showed that the P2 magnitude difference between English words and consonant strings was much greater (with words eliciting a larger amplitude) in a semantic task compared with a passive viewing task. Furthermore, when the authors trained participants to associate a set of artificial words to meaningful objects, properties, and events, they observed a significant amplitude change in the P2 to these learned stimuli, but not in the N1. In contrast, no training-induced changes in the P2 were observed in untrained artificial words that matched in orthographic regularity to the trained artificial words. These studies suggest that linguistic aspects of the stimuli modulate brain responses at the level of the P2. According to this idea, our results showing a P2 amplitude difference between strings of letters and numbers, but not between single letters and numbers, suggest that the visual cortex may be implicitly extracting phonological or semantic information in some conditions but not others.

On the other hand, it is difficult to imagine that there is asymmetry in phonological or semantic processing between letters and numbers only in strings and not in single characters. Therefore, P2 differences in the left hemisphere may not be because of implicit phonological or semantic influences on visual processing in the context of our study. Instead, the P2 in the context of visual word form processing may imply a later stage of a hierarchy of local combination detectors (Dehaene et al., 2005). This theoretical model, inspired by neurophysiological models of invariant object recognition, proposes that there is a hierarchical organization whereby neurons detect patterns of visual stimuli of increasing complexity along a hierarchy. Whereas pools of neurons in the lower levels may most effectively process single characters, pools of neurons in the upper levels may most effectively process combinations of characters. Our data fit well with this proposal, as only letter strings, but not single letters, showed a greater P2 compared with numbers. Moreover, according to this idea, our results imply that only letter-string processing (and not number-string processing) in the left visual cortex is subject to this hierarchical organization.

It is also of interest in this study that false fonts elicited greater ERP amplitudes than both letters and numbers across multiple phases of processing. Although these false-font ERP patterns do not explain the hemispheric double dissociations between letters and numbers, which was the more central research question of this study, they may potentially provide important insights for generating further hypotheses about how the visual cortex processes unfamiliar stimuli differently from familiar stimuli. It should first be noted that some other previous studies have shown greater ERP responses, at least at the N1 range, to letters and words compared with false fonts, a pattern opposite from our findings (Stevens et al., 2013 Wong et al., 2005). In these studies, however, an identity 1-back task was used with much longer stimulus presentations and longer intertrial intervals compared with our study. Accordingly, one possibility is that the active encoding of known stimuli (e.g., native letters and words) via top–down attentional mechanisms may elicit greater N1 amplitudes than the active encoding of unknown or incomprehensible stimuli. In contrast, when top–down attentional or encoding strategies are minimized, such as in our study, there may be no selective enhancement of N1 for known stimuli.

Enhanced neural activity to false fonts in our study parallels previous fMRI findings (Park, Hebrank, et al., 2012 Vinckier et al., 2007) but also shows that this enhancement occurs very early (as early as the P1 at 100 msec) in the processing stream. The larger ERP responses to false fonts may potentially be explained by inefficiency in the template-matching process for unfamiliar stimuli. For instance, highly experienced familiar stimuli such as letters and numbers may be detected by generic feature detectors early in the visual processing stream and then get fed rapidly into subsequent processing levels for more focal letter and number areas separately in the two hemispheres. In contrast, greater activity around the P1 for the false fonts may reflect the need for more extensive encoding of these unfamiliar stimuli by the generic feature detectors at early levels, resulting in greater neural activity that may propagate through later phases of the processing stream. Thus, the overall enhancement of the neural response to false fonts could result from the extended activity of generic feature detectors that have little influence from previous experience with false fonts. Consistent with this idea, Park, Park, et al. (2012) in an fMRI study of monozygotic twins showed smaller experiential influence (i.e., unique environmental effects) in the neural response to false fonts compared with letters, although the magnitude of the neural response to false fonts was greater than to letters.

In summary, the two electrophysiological experiments presented here show that the human visual cortex exhibits hemispherically differentiated processing for two categories of culturally defined, otherwise arbitrary, symbols, and it does so very early in the processing stream. These findings complement a previous fMRI study (Park, Hebrank, et al., 2012) by showing precisely when during letter and number processing the dissociation occurs, as well as when the processing of both of these culturally defined stimulus types differentiate from physically similar but unfamiliar visual stimulus forms (false fonts), thus providing important temporal information that was not afforded by the fMRI study. Our results further suggest that the processing of letter and number strings utilizes neural pathways that are partially differentiated from those processing single characters of letters and numbers. These findings imply a major neural specialization in the early visual cortex driven by extensive experience that is unique to humans. Future studies should explore when in the developmental time frame this specialization occurs and how and why it comes about.


Recognition Memory [ edit | edit source ]

When you see an object, you know what the object is because you've seen it on a past occasion this is recognition memory. Not only do abnormalities to the ventral (what) stream of the visual pathway effect our ability to recognize an object but also the way in which an object is presented to us.

Familiarity [ edit | edit source ]

A mechanism that is context free in the sense that what we recognize just feels familiar rather than spending time trying to find in what context we know this object. ⎧] The ventro-lateral region of the frontal lobe is involved in memory encoding during incidental learning and then later maintaining and retrieving semantic memories. ⎧] Familiarity can induce perceptual processes different to those of unfamiliar objects which means that our perception of a finite amount of familiar objects is unique. ⎨] Deviations from typical viewpoints and contexts can affect the efficiency for which an object is recognized most effectively. ⎨] It was found that not only are familiar objects recognized more efficiently when viewed from a familiar viewpoint opposed to an unfamiliar one, but also this principle applies to novel objects. This deduces to the thought that representations of objects in our brain are organized in more of a familiar fashion of the objects observed in the environment. ⎨] Recognition is not only largely driven by object shape and/or views but also by dynamic information. ⎩] Familiarity can benefit the perception of dynamic point-light displays, moving objects, the sex of faces, and face recognition. ⎨]

Recollection [ edit | edit source ]

Recollection shares many similarities with familiarity however it is context dependent, requiring specific information from the inquired incident. ⎧]


Andrey R. Nikolaev and Cees van Leeuwen were aided by an Odysseus grant from the Flemish Organization for Science (FWO). We thank Dr. Steeve Laquitaine, Dr. Justin L. Gardner and Dr. Anthony J. DeCostanzo for helpful discussions. We also thank the editor for her help and availability throughout the review process and the reviewers for their constructive criticism and helpful suggestions/comments on the manuscript.

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Keywords : color, form, segregation, integration, distributed processing, mixed selective cells, high dimensional code, complex selectivity

Citation: Rentzeperis I, Nikolaev AR, Kiper DC and van Leeuwen C (2014) Distributed processing of color and form in the visual cortex. Front. Psychol. 5:932. doi: 10.3389/fpsyg.2014.00932

Received: 14 June 2013 Paper pending published: 08 July 2013
Accepted: 05 August 2014 Published online: 27 October 2014.

Galina Paramei, Liverpool Hope University, UK

Ruth Rosenholtz, Massachusetts Institute of Technology, USA
Konstantinos Moutoussis, National and Kapodistrian University of Athens, Greece

Copyright © 2014 Rentzeperis, Nikolaev, Kiper and van Leeuwen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with theseterms.


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THE HUMAN VISUAL CORTEX

▪ Abstract The discovery and analysis of cortical visual areas is a major accomplishment of visual neuroscience. In the past decade the use of noninvasive functional imaging, particularly functional magnetic resonance imaging (fMRI), has dramatically increased our detailed knowledge of the functional organization of the human visual cortex and its relation to visual perception. The fMRI method offers a major advantage over other techniques applied in neuroscience by providing a large-scale neuroanatomical perspective that stems from its ability to image the entire brain essentially at once. This bird's eye view has the potential to reveal large-scale principles within the very complex plethora of visual areas. Thus, it could arrange the entire constellation of human visual areas in a unified functional organizational framework. Here we review recent findings and methods employed to uncover the functional properties of the human visual cortex focusing on two themes: functional specialization and hierarchical processing.


Shared Flashcard Set

tendency to respond to a stimulus that resembles one involved in the original conditioning.

stimulus resembles CS elicts a CR

CC: responding differently to two+ stimuli.

stimulus similar to the CS fails to evoke a CR

OC: response occurs in the presence of one stimulus but not the other. similar stimuli that differ from it on some dimension

weakening and eventual disappearance of learned response

CS is no longer paired with the US

response more likely to occur.

response less likely to occur.

response becomes more or less likely to occur, depending on CONSEQUENCES

schedule of reinforcement

learning new responses by observing the behavior of another rather than throug direct experience.

gathers and processes info

interprets -- responds to stimuli

coordinateds cells to respond

collection of neurons and supportive tissue running down the center of the back

bridge between brain and body

can be controlled subconsciously and by brain

peripheral nervous system

portion of nervous system outside the brain and spinal cord, including sensory and motor nerves

handles CNS's input and output

nerves that are connected to sensory receptors

regulates internal organs and glands

sympathetic nervous system

parasympatheic nervous system

support, nurture, and insulate neurons

remove debris when neurons die

formation and maintenance of neural connections

modify neural functioning

receive info from other neurons and transmit toward cell body

neuron's extending fiber that conducts impulses away from the cell body

transmits to other neurons

can be insulated by myelin sheath

reduces confusion between nearby neurons

change in electrical potential between inside and outside of neuron.

action potential: brief change in electrical voltage when neuron is stimulated as an electrical impulse

end of an exon from which the axon releases its message

several synaptic vesicles containing a neutrotransmitter

(chemical that is released by a transmitting neuron at the synapse and that alters the activit of a receiving neuron)

site where transmission of nerve impulses from one nerve to another cell occurs


Thomas Sprague

Thomas (Tommy) Sprague received his BA in Cognitive Sciences from Rice University in Houston, TX in 2010 and his PhD in Neurosciences with a Specialization in Computational Neurosciences from the University of California, San Diego in 2016. His graduate work with John Serences sought to develop and apply novel multivariate analysis methods to human neuroimaging techniques to understand how neural systems represent information in support of dynamic behavioral goals. Prior to joining the faculty at UCSB, Dr. Sprague worked as a postdoctoral fellowship with Clayton Curtis and Wei Ji Ma studying how neural systems represent both the contents of visual working memory, but also their ‘uncertainty’, by building new multivariate analysis methods.

Research

We often encounter the same scene (say, the inside of your refrigerator) with different behavioral goals (pouring a glass of water or finding a piece of cake). My lab is interested in how the actions we wish to perform impact neural representations of the world. When you’re looking for a piece of cake, how does that change the representation of the other items in the refrigerator? How well can you remember the color of the pitcher, or the location of the soda can, when you close the door? My lab combines computational neuroimaging (fMRI EEG), behavioral testing (psychophysics eyetracking), and model-based analysis techniques (voxel-wise modeling inverted encoding models) to shed light on how brain networks support and constrain our ability to represent information about our environment. Because of its ease of access to contemporary noninvasive neuroimaging tools, we use the visual system as a model system for evaluating neural representations and testing hypotheses about neural information processing.


Brain-wide tracing of single neurons reveals breadth of information transfer from visual cortex

An international collaboration of neuroscientists has today published a paper in Nature demonstrating the breadth of neural communication in visual cortex using a combination of methods for tracing the projections of individual neurons across the brain.

In classical models of the visual system, information flows from 'primary' visual cortex (V1) to more specialized, downstream areas that focus for example on image movement or image form. However, the details of how individual cells carry this information are not understood.

Professor Tom Mrsic-Flogel, one of the senior authors of the paper and project leader at Biozentrum, University of Basel and Director of the Sainsbury Wellcome Centre commented:

"Understanding the fine-scale anatomy by which individual neurons distribute signals to their targets is a crucial step for forging the relationship between neuronal structure and function."

Up until now, it had remained unclear as to whether information transfer from primary visual cortex was largely "one neuron -- one target area," or if individual neurons distributed their signals across multiple downstream areas.

While the research, conducted by neuroscientists from the Sainsbury Wellcome Centre for Neural Circuits and Behaviour, in conjunction with Cold Spring Harbor and Biozentrum, confirmed the existence of dedicated projections to certain cortical areas the scientists found that these were the exception and that the majority of primary visual cortex neurons broadcast information to multiple targets.

Justus M. Kebschull, one of the first authors on the paper at Cold Spring Harbor commented:

"Our findings reveal that individual neurons in the visual cortex project to several targets in the neocortex. This means that their signals are distributed widely and that individual neurons contribute to multiple parallel computations across the neocortex."

In the Nature paper, the team outline the two complementary methods they used to map the projection patterns. Firstly, they used whole-brain fluorescence-based axonal tracing by labelling neurons in the right visual cortex of each mouse with GFP and then imaging axonal projections by whole-brain serial two-photon tomography.

The Allen Mouse Brain Reference Atlas was then used to identify the areas in which axonal terminations were observed. The mouse primary visual cortex (V1) neurons were found to have a high degree of projectional diversity and most of the individual layer 2/3 neurons were found to distribute information to multiple areas rather than projecting to a single target. Such neurons were termed 'broadcasting neurons'.

Secondly, the researchers used high-throughput DNA sequencing of genetically barcoded neurons (MAPseq) to determine whether the broadcasting neurons were targeting cortical areas at random, or whether they preferentially target, or avoid, specific subsets of areas thereby indicating a higher-order structure.

Thousands of individual V1 neurons were uniquely labelled with random RNA sequences, in essence barcodes. Each labelled neuron then transports the barcode into its own axonal processes where they can be read out by high throughout sequencing of a dissected target area to determine the projection targets of that specific neuron to higher visual areas.

Professor Anthony Zador, another senior author and project leader at Cold Spring Harbor Laboratory, explains the revolutionary technique:

"The RNA sequences, or 'barcodes', that we deliver to individual neurons are unmistakably unique and this enables us to determine if individual neurons, as opposed to entire regions, are tailored to specific targets."

This technique revealed that the majority of V1 neurons project to higher visual areas in a non-random manner. Six projection motifs were identified that reflect several sub-classes of projection neurons for divergent information transfer from V1 to higher visual areas.

The researchers state that the results of the study "suggest a functional specialization of subpopulations of projection cells beyond 'one neuron -- one target area' mapping.

"The next piece of the puzzle will be to understand what each of these projection motifs does for visual processing and perception and how these long-range connectivity patterns are established during development," Professor Mrsic-Flogel concluded.


Multiple Target Broadcasting

Until now, it had remained unclear as to whether information transfer from primary visual cortex was largely &ldquoone neuron &ndash one target area,&rdquo or if individual neurons distributed their signals across multiple downstream areas.

While the research, conducted by neuroscientists from the Sainsbury Wellcome Centre for Neural Circuits and Behaviour, in conjunction with Cold Spring Harbor and Biozentrum, confirmed the existence of dedicated projections to certain cortical areas, the scientists found that these were the exception and that the majority of primary visual cortex neurons broadcast information to multiple targets.

&ldquoOur findings reveal that individual neurons in the visual cortex project to several targets in the neocortex. This means that their signals are distributed widely and that individual neurons contribute to multiple parallel computations across the neocortex,"

Justus M. Kebschull, one of the first authors on the paper at Cold Spring Harbor said.


Figure Locations

Figure 2 Parameter optimization and the log-posterior surface. (a) An illustration of how the log-posterior (LP) (vertical axis, also colored from blue to red) might depend on two parameters of a nonlinear model. An optimization path is shown, from the initial parameters Θ0 and following the gradient (black arrow) to an LP-maximum at Θbest. This LP-surface pictured is relatively well-behaved, although gradient ascent could get stuck in the lower maximum (right) with some initial conditions. (b) A demonstration of how the model predictions (green and red) compare with the observed data (black) as the LP of the model improves. (c) A typical convex LP-surface, such as that associated with generalized linear models (GLMs). Such a surface only has a single local maximum, which can be found with any initial conditions. (d) A projection of the initial model filters [100 random initializations (solid points)] and final filters [after model optimization (hollow points)] in the filter space for an ON-OFF cell simulation (from McFarland et al. 2013, supplemental material). The optimization always converged to two positions in parameter space with equal LP, corresponding to the true filters in either order. Thus, while there is more than one local maximum of the LP, they are both correct answers, and the LP-surface is well-behaved.


Watch the video: BL1 VL6 4 das visuelle System (July 2022).


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