Computer science. New life for neural networks.
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Biomedical subjects
Garrison W Cottrell
Publications and source records attributed to Garrison W Cottrell.
Early selection of diagnostic facial information in the human visual cortex.
There is behavioral evidence that different visual categorization tasks on various types of stimuli (e.g., faces) are sensitive to distinct visual characteristics of the same image, for example, spatial frequencies. However, it has been more difficult to address the question of how early in the processing stream this sensitivity to the information relevant to the categorization task emerges. The current study uses scalp event-related potentials recorded in humans to examine how and when information diagnostic to a particular task is processed during that task versus during a task for which it is not diagnostic. Subjects were shown diagnostic and anti-diagnostic face images for both expression and gender decisions (created using Gosselin and Schyns' Bubbles technique), and asked to perform both tasks on all stimuli. Behaviorally, there was a larger advantage of diagnostic over anti-diagnostic facial images when images designed to be diagnostic for a particular task were shown when performing that task, as compared to performing the other task. Most importantly, this interaction was seen in the amplitude of the occipito-temporal N170, a visual component reflecting a perceptual stage of processing associated with the categorization of faces. When participants performed the gender categorization task, the N170 amplitude was larger when they were presented with gender diagnostic images than with expression-diagnostic images, relative to their respective non-diagnostic stimuli. However, categorizing faces according to their facial expression was not significantly associated with a larger N170 when subjects categorized expression diagnostic cues relative to gender-diagnostic cues. These results show that the influence of higher-level task-oriented processing may take place at the level of visual categorization stages for faces, at least for processes relying on shared diagnostic features with facial identity judgments, such as gender cues.
Adult↗
Transmitting and decoding facial expressions.
This article examines the human face as a transmitter of expression signals and the brain as a decoder of these expression signals. If the face has evolved to optimize transmission of such signals, the basic facial expressions should have minimal overlap in their information. If the brain has evolved to optimize categorization of expressions, it should be efficient with the information available from the transmitter for the task. In this article, we characterize the information underlying the recognition of the six basic facial expression signals and evaluate how efficiently each expression is decoded by the underlying brain structures.
Adult↗
Imaging reveals synaptic targets of a swim-terminating neuron in the leech CNS.
In the leech, the command-like neuron called cell Tr2 is known to stop swimming, but the connections from cell Tr2 to the swim central pattern generator have not been identified. We used fluorescence resonance energy transfer voltage-sensitive dyes to identify three neurons that are synaptic targets of cell Tr2. We then used electrophysiological techniques to show that these connections are monosynaptic, chemical, and excitatory. Two of the novel targets, cell 256 and cell 54, terminate swimming when stimulated. These neurons are likely to mediate swim cessation caused by cell Tr2 activity, and thus play the role of intermediate control cells in the leech CNS.
Animals↗
Early lateralization and orientation tuning for face, word, and object processing in the visual cortex.
Event-related potential (ERP) studies of the human brain have shown that object categories can be reliably distinguished as early as 130-170 ms on the surface of occipito-temporal cortex, peaking at the level of the N170 component. Consistent with this finding, neuropsychological and neuroimaging studies suggest major functional distinctions within the human object recognition system, particularly in hemispheric advantage, between the processing of words (left), faces (right), and objects (bilateral). Given these observations, our aim was to (1) characterize the differential response properties of the N170 to pictures of faces, objects, and words across hemispheres; and (2) test whether an effect of inversion for highly familiar and monooriented nonface stimuli such as printed words can be observed at the level of the N170. Scalp EEG (53 channels) was recorded in 15 subjects performing an orientation decision task with pictures of faces, words, and cars presented upright or inverted. All three categories elicited at the same latency a robust N170 component associated with a positive counterpart at centro-frontal sites (vertex-positive potential, VPP). While there were minor amplitude differences at the level of the occipital medial P1 between linguistic and nonlinguistic categories, scalp topographies and source analyses indicated strong hemispheric and orientation effects starting at the level of the N170, which was right lateralized for faces, smaller and bilateral for cars, and as large for printed words in the left hemisphere as for faces. The entire N170/VPP complex was accounted for by two dipolar sources located in the lateral inferior occipital cortex/posterior fusiform gyrus. These two locations were roughly equivalent across conditions but differed in strength and lateralization. Inversion delayed the N170 (and VPP) response for all categories, with an increasing delay for cars, words, and faces, respectively, as suggested by source modeling analysis. Such results show that early processes in object recognition respond to category-specific visual information, and are associated with strong lateralization and orientation bias.
Adult↗
EMPATH: a neural network that categorizes facial expressions.
There are two competing theories of facial expression recognition. Some researchers have suggested that it is an example of "categorical perception." In this view, expression categories are considered to be discrete entities with sharp boundaries, and discrimination of nearby pairs of expressive faces is enhanced near those boundaries. Other researchers, however, suggest that facial expression perception is more graded and that facial expressions are best thought of as points in a continuous, low-dimensional space, where, for instance, "surprise" expressions lie between "happiness" and "fear" expressions due to their perceptual similarity. In this article, we show that a simple yet biologically plausible neural network model, trained to classify facial expressions into six basic emotions, predicts data used to support both of these theories. Without any parameter tuning, the model matches a variety of psychological data on categorization, similarity, reaction times, discrimination, and recognition difficulty, both qualitatively and quantitatively. We thus explain many of the seemingly complex psychological phenomena related to facial expression perception as natural consequences of the tasks' implementations in the brain.
Discrimination, Psychological↗
Analysis of oscillations in a reciprocally inhibitory network with synaptic depression.
We present and analyze a model of a two-cell reciprocally inhibitory network that oscillates. The principal mechanism of oscillation is short-term synaptic depression. Using a simple model of depression and analyzing the system in certain limits, we can derive analytical expressions for various features of the oscillation, including the parameter regime in which stable oscillations occur, as well as the period and amplitude of these oscillations. These expressions are functions of three parameters: the time constant of depression, the synaptic strengths, and the amount of tonic excitation the cells receive. We compare our analytical results with the output of numerical simulations and obtain good agreement between the two. Based on our analysis, we conclude that the oscillations in our network are qualitatively different from those in networks that oscillate due to postinhibitory rebound, spike-frequency adaptation, or other intrinsic (rather than synaptic) adaptational mechanisms. In particular, our network can oscillate only via the synaptic escape mode of Skinner, Kopell, and Marder (1994).
Animals↗