Neural networks need real-world behavior.

Bowers et al. propose to use controlled behavioral experiments when evaluating deep neural networks as models of biological vision. We agree with the sentiment and draw parallels to the notion that "neuroscience needs behavior.

Deep Neural Networks and Visuo-Semantic Models Explain Complementary Components of Human Ventral-Stream Representational Dynamics.

Deep neural networks (DNNs) are promising models of the cortical computations supporting human object recognition. However, despite their ability to explain a significant portion of variance in neural data, the agreement between models and brain representational dynamics is far from perfect. We address this issue by asking which representational features are currently unaccounted for in neural time series data, estimated for multiple areas of the ventral stream via source-reconstructed magnetoencephalography data acquired in human participants (nine females, six males) during object viewing.

Analysing linear multivariate pattern transformations in neuroimaging data.

Most connectivity metrics in neuroimaging research reduce multivariate activity patterns in regions-of-interests (ROIs) to one dimension, which leads to a loss of information. Importantly, it prevents us from investigating the transformations between patterns in different ROIs. Here, we applied linear estimation theory in order to robustly estimate the linear transformations between multivariate fMRI patterns with a cross-validated ridge regression approach.

Rapid Invariant Encoding of Scene Layout in Human OPA.

Successful visual navigation requires a sense of the geometry of the local environment. How do our brains extract this information from retinal images? Here we visually presented scenes with all possible combinations of five scene-bounding elements (left, right, and back walls; ceiling; floor) to human subjects during functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). The fMRI response patterns in the scene-responsive occipital place area (OPA) reflected scene layout with invariance to changes in surface texture.

Longitudinal Alzheimer's Degeneration Reflects the Spatial Topography of Cholinergic Basal Forebrain Projections.

The cholinergic neurons of the basal forebrain (BF) provide virtually all of the brain's cortical and amygdalar cholinergic input. They are particularly vulnerable to neuropathology in early Alzheimer's disease (AD) and may trigger the emergence of neuropathology in their cortico-amygdalar projection system through cholinergic denervation and trans-synaptic spreading of misfolded proteins. We examined whether longitudinal degeneration within the BF can explain longitudinal cortico-amygdalar degeneration in older human adults with abnormal cerebrospinal fluid biomarkers of AD neuropathology.

A supramodal role of the basal ganglia in memory and motor inhibition: Meta-analytic evidence.

The ability to stop actions and thoughts is essential for goal-directed behaviour. Neuroimaging research has revealed that stopping actions and thoughts engage similar cortical mechanisms, including the ventro- and dorso-lateral prefrontal cortex. However, whether and how these abilities require similar subcortical mechanisms remains unexplored.

Deep Convolutional Neural Networks Outperform Feature-Based But Not Categorical Models in Explaining Object Similarity Judgments.

Recent advances in Deep convolutional Neural Networks (DNNs) have enabled unprecedentedly accurate computational models of brain representations, and present an exciting opportunity to model diverse cognitive functions. State-of-the-art DNNs achieve human-level performance on object categorisation, but it is unclear how well they capture human behavior on complex cognitive tasks. Recent reports suggest that DNNs can explain significant variance in one such task, judging object similarity.

Visual features as stepping stones toward semantics: Explaining object similarity in IT and perception with non-negative least squares.

Object similarity, in brain representations and conscious perception, must reflect a combination of the visual appearance of the objects on the one hand and the categories the objects belong to on the other. Indeed, visual object features and category membership have each been shown to contribute to the object representation in human inferior temporal (IT) cortex, as well as to object-similarity judgments. However, the explanatory power of features and categories has not been directly compared.

Faciotopy-A face-feature map with face-like topology in the human occipital face area.

The occipital face area (OFA) and fusiform face area (FFA) are brain regions thought to be specialized for face perception. However, their intrinsic functional organization and status as cortical areas with well-defined boundaries remains unclear. Here we test these regions for "faciotopy", a particular hypothesis about their intrinsic functional organisation.

Intrinsic structure of visual exemplar and category representations in macaque brain.

One of the most remarkable properties of the visual system is the ability to identify and categorize a wide variety of objects effortlessly. However, the underlying neural mechanisms remain elusive. Specifically, the question of how individual object information is represented and intrinsically organized is still poorly understood.

Human Object-Similarity Judgments Reflect and Transcend the Primate-IT Object Representation.

Primate inferior temporal (IT) cortex is thought to contain a high-level representation of objects at the interface between vision and semantics. This suggests that the perceived similarity of real-world objects might be predicted from the IT representation. Here we show that objects that elicit similar activity patterns in human IT (hIT) tend to be judged as similar by humans.

Local discriminability determines the strength of holistic processing for faces in the fusiform face area.

Recent evidence suggests that the Fusiform Face Area (FFA) is not exclusively dedicated to the interactive processing of face features, but also contains neurons sensitive to local features. This suggests the existence of both interactive and local processing modes, consistent with recent behavioral findings that the strength of interactive feature processing (IFP) engages most strongly when similar features need to be disambiguated. Here we address whether the engagement of the FFA into interactive versus featural representational modes is governed by local feature discriminability.

Inverse MDS: Inferring Dissimilarity Structure from Multiple Item Arrangements.

The pairwise dissimilarities of a set of items can be intuitively visualized by a 2D arrangement of the items, in which the distances reflect the dissimilarities. Such an arrangement can be obtained by multidimensional scaling (MDS). We propose a method for the inverse process: inferring the pairwise dissimilarities from multiple 2D arrangements of items.

Categorical, yet graded--single-image activation profiles of human category-selective cortical regions.

Human inferior temporal cortex contains category-selective visual regions, including the fusiform face area (FFA) and the parahippocampal place area (PPA). These regions are defined by their greater category-average activation to the preferred category (faces and places, respectively) relative to nonpreferred categories. The approach of investigating category-average activation has left unclear to what extent category selectivity holds for individual object images.

Face-identity change activation outside the face system: "release from adaptation" may not always indicate neuronal selectivity.

Face recognition is a complex cognitive process that requires distinguishable neuronal representations of individual faces. Previous functional magnetic resonance imaging (fMRI) studies using the "fMRI-adaptation" technique have suggested the existence of face-identity representations in face-selective regions, including the fusiform face area (FFA). Here, we present face-identity adaptation findings that are not well explained in terms of face-identity representations.

Revealing representational content with pattern-information fMRI--an introductory guide.

Conventional statistical analysis methods for functional magnetic resonance imaging (fMRI) data are very successful at detecting brain regions that are activated as a whole during specific mental activities. The overall activation of a region is usually taken to indicate involvement of the region in the task. However, such activation analysis does not consider the multivoxel patterns of activity within a brain region.

Matching categorical object representations in inferior temporal cortex of man and monkey.

Inferior temporal (IT) object representations have been intensively studied in monkeys and humans, but representations of the same particular objects have never been compared between the species. Moreover, IT's role in categorization is not well understood. Here, we presented monkeys and humans with the same images of real-world objects and measured the IT response pattern elicited by each image.

Representational similarity analysis - connecting the branches of systems neuroscience.

A FUNDAMENTAL CHALLENGE FOR SYSTEMS NEUROSCIENCE IS TO QUANTITATIVELY RELATE ITS THREE MAJOR BRANCHES OF RESEARCH: brain-activity measurement, behavioral measurement, and computational modeling. Using measured brain-activity patterns to evaluate computational network models is complicated by the need to define the correspondency between the units of the model and the channels of the brain-activity data, e.g.