Value, variable, and coarse coding by posterior parietal neurons

Neural Stimulation Has a Long-Term Effect on Foreign Vocabulary Acquisition

Acquisition of a foreign language is a challenging task that is becoming increasingly more important in the world nowadays. There is evidence suggesting that the frontal and temporal cortices are involved in language processing and comprehension, but it is still unknown whether foreign language acquisition recruits additional cortical areas in a causal manner. For the first time, we used transcranial random noise stimulation on the frontal and parietal brain areas, in order to compare its effect on the acquisition of unknown foreign words and a sham, or placebo, condition was also included. This type of noninvasive neural stimulation enhances cortical activity by boosting the spontaneous activity of neurons. Foreign vocabulary acquisition was tested both immediately and seven days after the stimulation. We found that stimulation on the posterior parietal, but not the dorsolateral prefrontal cortex or sham stimulation, significantly improved the memory performance in the long term. These results suggest that the posterior parietal cortex is directly involved in acquisition of foreign vocabulary, thus extending the "linguistic network" to this area.

What connectionist models learn: Learning and representation in connectionist networks

Connectionist models provide a promising alternative to the traditional computational approach that has for several decades dominated cognitive science and artificial intelligence, although the nature of connectionist models and their relation to symbol processing remains controversial. Connectionist models can be characterized by three general computational features: distinct layers of interconnected units, recursive rules for updating the strengths of the connections during learning, and “simple” homogeneous computing elements. Using just these three features one can construct surprisingly elegant and powerful models of memory, perception, motor control, categorization, and reasoning. What makes the connectionist approach unique is not its variety of representational possibilities (including “distributed representations”) or its departure from explicit rule-based models, or even its preoccupation with the brain metaphor. Rather, it is that connectionist models can be used to explore systematically the complex interaction between learning and representation, as we try to demonstrate through the analysis of several large networks.

Receptive Field Characteristics That Allow Parietal Lobe Neurons to Encode Spatial Properties of Visual Input: A Computational Analysis

A subset of visually sensitive neurons in the parietal lobe apparently can encode the locations of stimuli, whereas visually sensitive neurons in the inferotemporal cortex (area IT) cannot. This finding is puzzling because both sorts of neurons have large receptive fields, and yet location can be encoded in one case, but not in the other. The experiments reported here investigated the hypothesis that a crucial difference between the IT and parietal neurons is the spatial distribution of their response profiles. In particular, IT neurons typically respond maximally when stimuli are presented at the fovea, whereas parietal neurons do not. We found that a parallel-distributed-processing network could map a point in an array to a coordinate representation more easily when a greater proportion of its input units had response peaks off the center of the input array. Furthermore, this result did not depend on potentially implausible assumptions about the regularity of the overlap in receptive fields or the homogeneity of the response profiles of different units. Finally, the internal representations formed within the network had receptive fields resembling those found in area 7a of the parietal lobe.