Sentence examples for neural face from inspiring English sources

Neural face processing in the fear network separated SAD patients from controls.

Neural face processing in the fear network alone correctly discriminated between SAD and HC, while the structural analyses yielded significant classification accuracy only when utilizing information from the whole brain.

These findings again support the integrative network character of face processing in the brain and emphasize the importance of future work on not only the spatial but also the temporal, characteristics of neural face processing both in the mature and the developing brain.

This triangular scan pattern strategy is fully consistent with previous eye movements findings [1], [4] [7], [22], [23] and also in line with recent neuroimaging results on Western Caucasian observers showing a tuning in the neural face-sensitive regions for visual stimuli containing more elements in the upper part [24].

Specifically, the scrambled images of the emotional faces were presented for 400ms in the unmasked condition, while the scrambled images of the emotional faces were presented for 40ms followed by presentation of the scrambled images of the neural faces for 360 ms in the masked condition.

In Section 2, the FPGA implementation of the neural network face detector using the bit-width reduced FPUs is described.

In general, its performance is superior to traditional statistical methods (West 2000); however, the neural network faces issues regarding training efficiency and convergence.

At first sight, given the temporal properties of the fMRI signal (imprecise and sluggish), it is quite astonishing that fixation-related analysis of fMRI data does not only work for categories that have well-established vastly distinct neural signatures (faces and houses), but also for categories that differ more subtly (visual character strings).

In particular, the N170 component received and receives much interest, as it was originally thought to exclusively reflect neural processing of faces or face parts [30].

In the paper entitled "Performance Analysis of Bit-Width Reduced Floating-Point Arithmetic Units in FPGAs: Case Study of Neural Network-based Face Detector," the authors describe the implementation of an FPGA-based face detector using a neural network and bit-width reduced floating-point arithmetic units (FPUs).

In Haxby et al.'s (2000) neural model of face processing, the core network involved in face perception is characterized as a hierarchical system, with face information directly entering OFA only (see also Fairhall and Ishai 2007).