Journal of Integrative Neuroscience

The current study explores sentence comprehension impairments among adults following moderate closed head injury. It was hypothesized that if the factor of syntactic complexity significantly affects sentence comprehension in these patients, it would testify to the existence of syntactic processing deficit along with working-memory problems. Thirty-six adults (18 closed head injury patients and 18 healthy controls matched in age, gender, and IQ) participated in the study. A picture–sentence matching task together with various tests for memory, language, and reading abilities were used to explore whether sentence comprehension impairments exist as a result of a deficit in syntactic processing or of working-memory dysfunction. Results indicate significant impairment in sentence comprehension among adults with closed head injury compared with their non–head-injured peers. Results also reveal that closed head injury patients demonstrate considerable decline in working memory, short-term memory, and semantic knowledge. Analysis of the results shows that memory impairment and syntactic complexity contribute significantly to sentence comprehension difficulties in closed head injury patients. At the same time, the presentation mode (spoken or written language) was found to have no effect on comprehension among adults with closed head injury, and their reading abilities appear to be relatively intact.

Long-term potentiation (LTP) of synaptic transmission is a widely accepted model of learning and memory. In vitro brain slice techniques were used to investigate the effects of cortical-spreading depression and picrotoxin, an antagonist of the gamma-aminobutyric acid A (GABA_A) receptor, on the tetanus-induced long-term potentiation of field excitatory postsynaptic potentials. Cortical-spreading depression is involved in glutamate desensitization; on the other hand, GABA_A antagonists could increase postsynaptic excitability. This study shows that picrotoxin effectively induced long-term potentiation with 142.25 ± 4.18% of the baseline in the picrotoxin group (n = 8) versus 134.36 ± 2.35% of the baseline in the control group (n = 10). In group with picrotoxin applied to CSD, we obtained the smallest magnitude of LTP (120.15 ± 3.73% of the baseline, n = 8). These results suggest that picrotoxin could increase hippocampal activity and LTP; on the contrary, CSD reduced LTP magnitude. In addition, the results also suggest that the decay rate of post-tetanic potentiation has a direct relationship with LTP. Moreover, data were interpreted by nonlinear least squares quantifying, and LTP could also be quantified. The nonlinear attribute of LTP had an influence on the fitting, with respect to increasing the accuracy of the parameters and the compatibility of combination of stimuli that produce LTP.

Application of comparison theorem is used to examine the validitiy of the "lumped parameter assumption" in describing the behavior of solutions of the continuous cable equation U_t = DU_xx+f(U) with the discrete cable equation dV_n/dt = d*(V_n+1 - 2V_n + V_n-1) + f(V_n), where f is a nonlinear functional describing the internal diffusion of electrical potential in single neurons. While the discrete cable equation looks like a finite difference approximation of the continuous cable equation, solutions of the two reveal significantly different behavior which imply that the compartmental models (spiking neurons) are poor quantifiers of neurons, contrary to what is commonly accepted in computational neuroscience.

The P300 is an endogenously evoked potential with amplitude and latency depending on the amount of information carried by the stimulus rather than its physical characteristics. It has been suggested that P300 is a manifestation of the context updating mechanism in the human working memory. We present a neural network-based model that mimics the learning and forgetting mechanisms of external stimuli in the human working memory that are believed to be responsible for P300 generation. A modified version of the Hebbian learning rule has been devised to govern the weight dynamics of the network. The model was validated by comparing the characteristics of simulated P300 with actual experimental findings such as the relationship between P300 amplitude and stimulus probability, and task relevance. The results show that the proposed P300 model mimics many aspects of the nervous system responsible for P300 generation.

A simple recurrent network with a perceptual simulation layer was trained on a corpus of affirmative and negated sentences. Linguistic negation can be encoded by the network via the inclusion (or absence) of features and categories associated with the senses, in one step, without the need for an explicit logical operation or for treating the negating word any differently than any other words. Visualizing negation as a trajectory in perceptual simulation space is explored in detail, and the implications for artificial intelligence, embodied computational models, and more practical implications of everyday use of negations are discussed.

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide complementary noninvasive information of brain activity, and EEG/fMRI fusion can achieve higher spatiotemporal resolution than each modality separately. This focuses on independent component analysis (ICA)-based EEG/fMRI fusion. In order to appreciate the issues, we first describe the potential and limitations of the developed fusion approaches: fMRI-constrained EEG imaging, EEG-informed fMRI analysis, and symmetric fusion. We then outline some newly developed hybrid fusion techniques using ICA and the combination of data-/model-driven methods, with special mention of the spatiotemporal EEG/fMRI fusion (STEFF). Finally, we discuss the current trend in methodological development and the existing limitations for extrapolating neural dynamics.

The ability to dynamically track moving objects in the environment is crucial for efficient interaction with the local surrounds. Here, we examined this ability in the context of the multi-object tracking (MOT) task. Several theories have been proposed to explain how people track moving objects; however, only one of these previous theories is implemented in a real-time process model, and there has been no direct contact between theories of object tracking and the growing neural literature using ERPs and fMRI. Here, we present a neural process model of object tracking that builds from a Dynamic Field Theory of spatial cognition. Simulations reveal that our dynamic field model captures recent behavioral data examining the impact of speed and tracking duration on MOT performance. Moreover, we show that the same model with the same trajectories and parameters can shed light on recent ERP results probing how people distribute attentional resources to targets vs. distractors. We conclude by comparing this new theory of object tracking to other recent accounts, and discuss how the neural grounding of the theory might be effectively explored in future work.