View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation

The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and relatively robust against identity-preserving transformations like depth-rotations. Current computational models of object recognition, including recent deep learning networks, generate these properties through a hierarchy of alternating selectivity-increasing filtering and tolerance-increasing pooling operations, similar to simple-complex cells operations. While simulations of these models recapitulate the ventral stream's progression from early view-specific to late view-tolerant representations, they fail to generate the most salient property of the intermediate representation for faces found in the brain: mirror-symmetric tuning of the neural population to head orientation. Here we prove that a class of hierarchical architectures and a broad set of biologically plausible learning rules can provide approximate invariance at the top level of the network. While most of the learning rules do not yield mirror-symmetry in the mid-level representations, we characterize a specific biologically-plausible Hebb-type learning rule that is guaranteed to generate mirror-symmetric tuning to faces tuning at intermediate levels of the architecture

Does finding a face cell tell us anything much at all?

There are two approaches to doing science. One is the “tractor” approach. You take a big, powerful piece of machinery and apply it in systematic fashion to a problem. Here, “you” is often a large group of people who share the same goal. A recent example is the International Brain Lab (Abbott et al., 2017), a consortium of labs across the world all performing the same experiment to understand visually-guided decision making in the rodent. The plan is for each lab to train mice on a common behavioral paradigm and then insert high channel count electrodes into different parts of the brain, like a fleet of tractors mowing a field. A very different, older approach is that of the lone hunter pursuing a question no one else cares about, guided by a vision in his or her own head. Auden captures the essence of this approach in his wonderful poem “History of Science.” The poem tells the tale of the Fourth Brother, who has been excised from the official fairy tale..

Does finding a face cell tell us anything much at all?

There are two approaches to doing science. One is the “tractor” approach. You take a big, powerful piece of machinery and apply it in systematic fashion to a problem. Here, “you” is often a large group of people who share the same goal. A recent example is the International Brain Lab (Abbott et al., 2017), a consortium of labs across the world all performing the same experiment to understand visually-guided decision making in the rodent. The plan is for each lab to train mice on a common behavioral paradigm and then insert high channel count electrodes into different parts of the brain, like a fleet of tractors mowing a field. A very different, older approach is that of the lone hunter pursuing a question no one else cares about, guided by a vision in his or her own head. Auden captures the essence of this approach in his wonderful poem “History of Science.” The poem tells the tale of the Fourth Brother, who has been excised from the official fairy tale..

Fast, invariant representation for human action in the visual system

Humans can effortlessly recognize others' actions in the presence of complex transformations, such as changes in viewpoint. Several studies have located the regions in the brain involved in invariant action recognition, however, the underlying neural computations remain poorly understood. We use magnetoencephalography (MEG) decoding and a dataset of well-controlled, naturalistic videos of five actions (run, walk, jump, eat, drink) performed by different actors at different viewpoints to study the computational steps used to recognize actions across complex transformations. In particular, we ask when the brain discounts changes in 3D viewpoint relative to when it initially discriminates between actions. We measure the latency difference between invariant and non-invariant action decoding when subjects view full videos as well as form-depleted and motion-depleted stimuli. Our results show no difference in decoding latency or temporal profile between invariant and non-invariant action recognition in full videos. However, when either form or motion information is removed from the stimulus set, we observe a decrease and delay in invariant action decoding. Our results suggest that the brain recognizes actions and builds invariance to complex transformations at the same time, and that both form and motion information are crucial for fast, invariant action recognition

Representation of conscious percept without report in the macaque face patch network

A powerful paradigm to identify the neural correlates of consciousness is binocular rivalry, wherein a constant visual stimulus evokes a varying conscious percept. It has recently been suggested that activity modulations observed during rivalry could represent the act of report rather than the conscious percept itself. Here, we performed single-unit recordings from face patches in macaque inferotemporal (IT) cortex using a no-report paradigm in which the animal's conscious percept was inferred from eye movements. We found high proportions of IT neurons represented the conscious percept even without active report. Population activity in single trials, measured using a new 128-site Neuropixels-like electrode, was more weakly modulated by rivalry than by physical stimulus transitions, but nevertheless allowed decoding of the changing conscious percept. These findings suggest that macaque face patches encode both the physical stimulus and the animal's conscious visual percept, and the latter encoding does not require active report

View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation

© 2017 Elsevier Ltd The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and robust against identity-preserving transformations, like depth rotations [1, 2]. Current computational models of object recognition, including recent deep-learning networks, generate these properties through a hierarchy of alternating selectivity-increasing filtering and tolerance-increasing pooling operations, similar to simple-complex cells operations [3–6]. Here, we prove that a class of hierarchical architectures and a broad set of biologically plausible learning rules generate approximate invariance to identity-preserving transformations at the top level of the processing hierarchy. However, all past models tested failed to reproduce the most salient property of an intermediate representation of a three-level face-processing hierarchy in the brain: mirror-symmetric tuning to head orientation [7]. Here, we demonstrate that one specific biologically plausible Hebb-type learning rule generates mirror-symmetric tuning to bilaterally symmetric stimuli, like faces, at intermediate levels of the architecture and show why it does so. Thus, the tuning properties of individual cells inside the visual stream appear to result from group properties of the stimuli they encode and to reflect the learning rules that sculpted the information-processing system within which they reside