one of the most complex problems the visual system has to solve is recognizing objects across a wide range of encountered variations . retinal information about one and the same object can dramatically vary when position , viewpoint , lighting , or distance change , or when the object is partly occluded by other objects . in computer vision , there are a variety of models using alignment , invariant properties , or part - decomposition methods ( roberts , 1965 ; fukushima , 1982 ; marr , 1982 ; ullman et al . , 2001 ; viola and jones , 2001 ; lowe , 2004 ; torralba et al . , 2008 ) , which are able to identify objects across a range of viewing conditions .
some computational models are clearly biologically inspired and take for example the architecture of the visual system into account ( e.g. , wersing and krner , 2003 ) , or cleverly adapt the concept of a powerful computer vision algorithm ( e.g. , the fourier - mellin transform ) to a neurobiologically plausible alternative ( sountsov et al .
such models can successfully detect objects in sets of widely varying natural images ( torralba et al . , 2008 ) and achieve impressive invariance ( sountsov et al . , 2011 ) .
in general however , computer vision models are developed for practical image analysis applications ( handwriting recognition , face detection , etc . )
for which fast and accurate object recognition and not neurobiological validity is pivotal . therefore , these models are generally less powerful in explaining how object constancy arises in the human brain . indeed ,  models are common ; good theories are scarce  as suggested by stevens ( 2000 , p. 1177 ) .
humans are highly skilled in object recognition , and they outperform machines in object recognition tasks with great ease ( fleuret et al . , 2011 ) .
this is partly because they are able to strategically use semantics and information from context or memory .
in addition , they can direct attention to informative features in the image , while ignoring distracting information .
such higher cognitive processes are difficult to implement , but improve object recognition performance when taken into account ( lowe , 2000 ) .
computer vision models might become more accurate in recognizing objects across a wide range of variations in image input , when implementing algorithms derived from neurobiological observations .
reciprocally , our interpretation of such neurobiological findings might be greatly improved by insights in the underlying computational mechanisms .
humans can identify objects with great speed and accuracy , even when the object percept is degraded , occluded or presented in a highly cluttered visual scene ( e.g. , thorpe et al .
, 1996 ) . however , which computational mechanisms enable such remarkable performance is not yet fully understood . to create a comprehensive theory of human object recognition and how it achieves invariant object recognition , computational mechanisms derived from modeling efforts
, we highlight recent developments in object recognition research and put forward a  common brain space 
framework ( cbs ; goebel and de weerd , 2009 ; peters et al . , 2010 ) in which empirical data and computational results can be directly integrated and quantitatively compared .
object recognition , discrimination , and identification are complex tasks . different encounters with an object are unlikely to take place under identical viewing conditions , requiring the visual system to generalize across changes .
information that is important to retrieve object identity should be effectively processed , while unimportant view - point variations should be ignored .
that is , the recognition system should be stable yet sensitive ( marr and nishihara , 1978 ) , leading to inherent tradeoffs .
how the visual system is able to accomplish this task with such apparent ease is not yet understood .
the first suggests that objects can be recognized by cardinal (  non - accidental  ) properties that are relatively invariant to the objects ' appearance ( marr , 1982 ; biederman , 1987 ) .
thus , these invariant properties and their spatial relations should provide sufficient information to recognize objects regardless of their viewpoint .
however , how such cardinal properties are defined and recognized in an invariant manner is a complex issue ( tarr and blthoff , 1995 ) .
the second type of theory suggests that there are no such invariants but that objects are stored in the view as originally encountered ( which , in natural settings encompasses multiple views being sampled in a short time interval ) , thereby maintaining view - dependent shape and surface information ( edelman and blthoff , 1992 ) .
recognition of an object under different viewing conditions is achieved by either computing quality matches between the input and stored presentations ( perrett et al . , 1998 ; riesenhuber and poggio , 1999 ) or by transforming input to match the view specifications of the stored representation ( blthoff and edelman , 1992 ) .
the latter normalization can be accomplished by interpolation ( poggio and edelman , 1990 ) , mental transformation ( tarr and pinker , 1989 ) , or alignment ( ullman , 1989 ) .
view - invariant theories suggest that the visual system recognizes objects using a limited library of non - accidental properties , and neural representations are invariant .
evidence for such invariant object representations have been found at final stages of the visual pathway ( quiroga et al . , 2005 ; freiwald and tsao , 2010 ) .
in contrast , the second class of theories assumes that neural object representations are view - dependent , with neurons being sensitive to object transformations .
clearly , the early visual system is sensitive to object appearance : the same object can elicit completely different , non - overlapping neural activation patterns when presented at different locations in the visual field .
so , object representations are input specific at initial stages of processing , whereas invariant representations emerge in final stages .
however , how objects are represented by intermediate stages of this processing chain is not yet well understood .
likely , multiple different transforms are ( perhaps in parallel ) performed at theses stages .
this creates multiple object representations , in line with the various types of information ( such as position and orientation ) that have to be preserved for interaction with objects .
moreover , position information aids invariant object learning ( einhuser et al . , 2005 ; li and dicarlo , 2008 , 2010 ) and representations can reflect view - dependent and view - invariant information simultaneously ( franzius et al . , 2011 ) .
the following section reviews evidence from monkey neurophysiology and human neuroimaging on how object perception and recognition are implemented in the primate brain . as already alluded to above
, the visual system is hierarchically organized in more than 25 areas ( felleman and van essen , 1991 ) with initial processing of low - level visual information by neurons in the thalamus , striate cortex ( v1 ) and v2 ; and of more complex features in v3 and v4 ( carlson et al . , 2011 ) .
further processing of object information in the human ventral pathway ( ungerleider and haxby , 1994 ) , involves higher - order visual areas such as the lateral occipital cortex ( loc ; malach , 1995 ) and object selective areas for faces (  ffa  ; kanwisher et al . , 1997 ) , bodies (  eba  ; downing et al . , 2001 ) ,
words (  vwfa  ; mccandliss et al . , 2003 ) , and scenes (  ppa  ; epstein et al . , 1999 ) .
the first studies on the neural mechanisms of object recognition were neurophysiological recordings in monkeys . in macaque anterior inferotemporal ( it ) cortex ,
most of the object - selective neurons are tuned to viewing - position ( logothetis et al .
, 1995 ; booth and rolls , 1998 ) , in line with viewpoint - dependent theories . on the other hand
, it neurons also turned out to be more sensitive to changes in  non - accidental  than to equally large pixel - wise changes in other shape features (  metric properties  ; kayaert et al . , 2003 ) , providing support for structural description theories ( biederman , 1987 ) .
taken together , these studies provide neural evidence for both theories ( see also rust and dicarlo , 2010 ) . however , to which degree object representations are stored in an invariant or view - dependent manner across visual areas , and how these representations arise and are matched to incoming information , remains elusive .
the bold signal reflects neural activity at the population rather than single - cell level .
the highest functional resolution provided by standard 3 tesla mri scanners is around 2  2  2 mm , which is too coarse to zoom into the functional architecture within visual areas .
however , more subtle information - patterns can be extracted using multi - voxel pattern analysis ( mvpa ; haynes et al .
, 2007 ) or fmri - adaptation ( fmri - a ; grill - spector and malach , 2001 ) .
mvpa can reveal subtle differences in distributed fmri patterns across voxels resulting from small biases in the distributions of differentially tuned neurons that are sampled by each voxel . by using classification techniques developed in machine learning , distributed spatial patterns of different classes ( e.g. , different objects ) can be successfully discriminated ( see fuentemilla et al .
for example , changing the position of an object significantly changes patterns in loc , even more than replacing an object ( at the same position ) by an object of a different category ( sayres and grill - spector , 2008 ) . rotating the object ( up to 60 ) did not change loc responses however ( eger et al . ,
2008 ) suggesting that loc representations might be view - dependent in only some aspects .
fmri - a exploits the fact that the neuronal ( and the corresponding hemodynamic ) response is weaker for repeated compared to novel stimuli ( miller and desimone , 1994 ) .
thus , areas are sensitive to view - dependent changes when their bold response returns to its initial level for objects that are presented a second time , but now from a different view - point
for example , a recent study observed view - point and size dependent coding at intermediate processing stages ( v4 , v3a , mt , and v7 ) , whereas responses in higher visual areas were view - invariant ( konen and kastner , 2008 ) .
remarkably , these view - invariant representations were not only found in the ventral ( e.g. , loc ) , but also in the dorsal pathway ( e.g. , ips ) . the dorsal  where / how  or  perception - for - action  pathway is involved in visually guided actions toward objects rather than in identifying objects  which is mainly performed by the ventral or 
what  pathway ( goodale and milner , 1992 ; ungerleider and haxby , 1994 ) . for this role ,
maintaining view - point dependent information in higher dorsal areas seems important , which however was thus not confirmed by the view - invariant results in ips ( but see james et al . , 2002 ) .
likewise , another recent study ( dilks et al . , 2011 ) revealed an unexpected tolerance for mirror - reversals in visual scenes in a parahippocampal area thought to play a key role in navigation ( e.g. , janzen and van turennout , 2004 ) and reorientation ( e.g. , epstein and kanwisher , 1998 ) , functions for which view - dependent information is essential .
for example , different findings on size , position , and viewpoint - invariant representations in different subparts of the loc have been found ( grill - spector et al . , 1999 ;
these divergent findings might be partly related to intricacies inherent to the fmri - a approach ( e.g. , krekelberg et al . , 2006 ) , and its sensitivity to the design used ( grill - spector et al . , 2006 ) and varying attention ( vuilleumier et al . , 2005 ) and task demands ( e.g. , ewbank et al . , 2011 ) .
the latter should not be regarded as obscuring confounds however , since they appear to strongly contribute to our skilled performance .
object perception is accompanied by cognitive processes supporting fast ( e.g. , extracting the  gist  of a scene , attentional selection of relevant objects ) and accurate ( e.g. , object - verification , semantic interpretation ) object identification for subsequent goal - directed use of the object ( e.g. , grasping ; tool - use ) .
these processes engage widespread memory- and frontoparietal attention - related areas interacting with object processing in the visual system ( corbetta and shulman , 2002 ; bar , 2004 ; ganis et al . , 2007 ) . as the involvement of such top - down processes
might be particularly pronounced in humans  and weaker or even absent in monkeys and machines respectively  efforts to integrate computational modeling with human neuroimaging remain essential ( see tagamets and horwitz , 1998 ; corchs and deco , 2002 for earlier work ) . with the advent of ultra - high field fmri ( 7 tesla scanners ) , both the sensitivity ( due to increases in signal - to - noise ratio linearly dependent on field strength ) and the specificity ( due to a stronger contribution of gray - matter microvasculature compared to large draining veins and less partial volume effects ) of the acquired signal improves significantly , providing data at a level of detail which previously was only available via invasive optical imaging in non - human species .
the functional visual system can be spatially sampled in the range of hundreds of microns , which is sufficient to resolve activation at the cortical column ( yacoub et al . , 2008 ; zimmermann et al . , 2011 ) and layer ( polimeni et al . , 2010 )
given that cortical columns are thought to provide the organizational structure forming computational units involved in visual feature processing ( hubel and wiesel , 1962 ; tanaka , 1996 ; mountcastle , 1997 ) , the achievable resolution at ultra - high fields will therefore not only produce more detailed maps , but really has the potential to yield new vistas on within - area operations .
the approach we propose is to project the predicted activity in a modeled area onto corresponding cortical regions where empirical data are collected ( figure 1 ) . by interfacing empirical and simulated data in one anatomical 
direct and quantitative mutual hypothesis testing based on predicted and observed spatiotemporal activation patterns can be achieved .
more specifically , modeled units ( e.g. , cortical columns ) are 1-to-1 mapped to corresponding neuroimaging units ( e.g. , voxels , vertices ) in the empirically acquired brain model ( e.g. , cortical gray matter surface ) . as a result ,
a running network simulation creates spatiotemporal data directly on a linked brain model , enabling highly specific and accurate comparisons between neuroimaging and neurocomputational data in the temporal as well as spatial domain .
note that in cbs ( as implemented in neurolator 3d ; goebel , 1993 ) , computational and neuroimaging units can flexibly represent various neural signals ( e.g. , fmri , eeg , meg , fnirs , or intracranial recordings ) .
furthermore , both hidden and output layers of the neural network can be projected to the brain model , providing additional flexibility to the framework as predicted and observed activations can be compared at multiple selected processing stages simultaneously ( see figure 2 for an example ) . the common brain space framework : measured neuroimaging data ( left panel ) and simulated data ( right panel ) are projected to the same anatomical brain space via network - brain links ( see figure 2 ) .
input : ( a ) visualization of common brain space ( cbs ) in neurolator : each computational unit of a neural network layer is separately connected to a topographically corresponding location on the cortical sheet via a networkbrain link ( nbl ) . in this example , model layers v1 , loc , and ffa are connected to the corresponding brain regions v1 , loc , and ffa on a mesh reconstruction of an individual 's gray - white matter boundary . for this participant ,
v1 , loc , and ffa were localized using standard retinotopy and related fmri region - of - interest mapping techniques . by connecting a running neural network , activity in the connected layers
( b ) in neurolator , functional mri data can be projected on the cortical mesh , similar to the standard functional - anatomical data co - registration applied in fmri analyses .
output : ( c ) depending on display mode , cortical patches ( i.e. , vertices ) either represent the empirical or the simulated fmri data . since the observed and simulated datasets are in the same anatomical space , identical fmri analyses tools can be used to analyze observed and simulated timeseries . to model the human object recognition system
, we developed large - scale networks of cortical column units , which dynamics can either reflect the spike activity , integrated synaptic activity , or oscillating activity ( when modeled as burst oscillators ) , resulting from excitatory and inhibitory synaptic input . to create simulated spatiotemporal patterns , each unit of a network layer ( output and/or hidden )
is linked to a topographically corresponding patch on a cortical representation via a so - called network - to - brain link ( nbl ) . via this link ,
activity of modeling units in the running network is transformed into timecourses of neuroimaging units , spatially organized in an anatomical coordinate system .
importantly , when simulated and measured data co - exist in the same representational space , the same analysis tools ( e.g. , mvpa , effective connectivity analysis ) can be applied to both data sets allowing for quantitative comparisons ( figure 2 ) . see peters et al .
we propose that such a tight integration of neuroimaging and modeling data allows reciprocal fine - tuning and facilitates hypothesis testing at a mechanistic level as it leads to falsifiable predictions that can subsequently be empirically tested .
importantly , there is a direct topographical correspondence between computational ( cortical columnar ) units at the model and brain level .
moreover , comparisons between simulated and empirical data are not limited to activity patterns in output stages ( i.e. , object - selective areas in anterior it such as ffa or even more anterior in putative  face exemplar  regions ; kriegeskorte et al . , 2007 ) , but also at intermediate stages ( such as v4 and loc ) . interpreting the role of feature representations at intermediate stages may be essential for a comprehensive brain model of object recognition ( ullman et al .
studying several stages of the visual hierarchy simultaneously , by quantitatively comparing ongoing visual processes across stages both within and between the simulated and empirically acquired dataset , may help to clarify how higher - order invariant representations are created from lower - level features in several ways .
incoming percepts might be differently transformed and matched to stored object representations at several stages , with view - dependent matching at intermediate stages and matching of only informative properties ( biederman , 1987 ; ullman et al .
monitoring activity patterns at multiple processing stages simultaneously is desirable , given that early stages are influenced by processing in later stages .
to facilitate object recognition , invariant information is for example fed back from higher to early visual areas ( williams et al . , 2008 ) , suggesting that object perception results from a dynamic interplay between visual areas .
finally , it is important to realize that such top - down influences are not limited to areas within the classical visual hierarchy , but also engage brain - wide networks involved in  initial guessing 
2006 ) , object selection ( serences et al . , 2004 ) , context integration ( graboi and lisman , 2003 ; bar , 2004 ) , and object verification ( ganis et al . , 2007 ) .
such functions should be incorporated in computational brain models to fully comprehend what makes human object recognition so flexible , fast , and accurate .
modeling higher cognitive functions is in general challenging , but may be aided by considering empirical observations in object perception studies where the level of top - down processing varies ( e.g. , ganis et al . , 2007 ) .
the interactions between the visual pathway and frontoparietal system revealed by such fmri studies can be compared at multiple processing stages to simulations , allowing a more subtle , process - specific fine - tuning of the modeled areas .
a number of recent fmri studies applied en- and decoding techniques developed in the field of machine learning and computer vision , to interpret their data ( kriegeskorte et al .
, 2011 ; naselaris et al . , 2011 ; see laconte , 2011 for an extention to brain - computer - interfaces ) , showing that both fields are starting to approach each other .
for example , by summarizing the complex statistical properties of natural images using a computer vision technique , a visual scene percept could be successfully reconstructed from fmri activity ( naselaris et al . , 2009 ) .
the trend to investigate natural vision is noteworthy , given that processing cluttered and dynamic natural visual input rather than artificially created isolated objects poses additional challenges to the visual system ( einhuser and knig , 2010 ) .
we believe that now columnar - level imaging is in reach with the advent of high - resolution fmri ( in combination with the recently developed en- and decoding fmri methods ) the time has come to more directly integrate computational and experimental neuroscience , and test which predictions of the various object recognition models are supported by this new type of empirical evidence .
the authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest .
the authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest .
 recent advances in computer vision and experimental neuroscience provided insights into mechanisms underlying invariant object recognition . however , due to the different research aims in both fields models tended to evolve independently . 
 a tighter integration between computational and empirical work may contribute to cross - fertilized development of ( neurobiologically plausible ) computational models and computationally defined empirical theories , which can be incrementally merged into a comprehensive brain model . 
 after reviewing theoretical and empirical work on invariant object perception , this article proposes a novel framework in which neural network activity and measured neuroimaging data are interfaced in a common representational space . 
 this enables direct quantitative comparisons between predicted and observed activity patterns within and across multiple stages of object processing , which may help to clarify how high - order invariant representations are created from low - level features . 
 given the advent of columnar - level imaging with high - resolution fmri , it is time to capitalize on this new window into the brain and test which predictions of the various object recognition models are supported by this novel empirical evidence . 
Introduction
Exploring invariant object recognition in the human visual system
Integration of computational and experimental findings in CBS
Conflict of interest statement