Journal of Integrative Neuroscience

The separation of input patterns received from the entorhinal cortex (EC) by the dentate gyrus (DG) is a well-known critical step of information processing in the hippocampus. Although the role of interneurons in separation pattern efficiency of the DG has been theoretically known, the balance of neurogenesis of excitatory neurons and interneurons as well as its potential role in information processing in the DG is not fully understood. In this work, we study separation efficiency of the DG for different rates of neurogenesis of interneurons and excitatory neurons using a novel computational model in which we assume an increase in the synaptic efficacy between excitatory neurons and interneurons and then its decay over time. Information processing in the EC and DG was simulated as information flow in a two layer feed-forward neural network. The neurogenesis rate was modeled as the percentage of new born neurons added to the neuronal population in each time bin. The results show an important role of an optimal neurogenesis rate of interneurons and excitatory neurons in the DG in efficient separation of inputs from the EC in pattern separation tasks. The model predicts that any deviation of the optimal values of neurogenesis rates leads to different decreased levels of the separation deficits of the DG which influences its function to encode memory.

The present study investigated psychophysiological differences between cognitive and motor tasks. Participants were 16 university students (male$=$10, female$=$6) aged between 24 to 31 years (mean$=$27.6 years, SD$=$ $2.3$ years). They were instructed to perform cognitive and motor tasks while their brain activity was simultaneously recorded using electroencephalography (EEG). A 3(task)$\times$8(area)$\times$4(bands) analysis of variance (ANOVA) was performed on theta, alpha and beta frequency bands. The results showed that the cognitive task resulted in a significantly higher spectral power in the theta band at frontal electrodes than did the motor task. This suggests that the frontal lobe might be engaged in problem solving, resulting in increased theta activity. However, there were no differences in alpha and beta activity between the two tasks. The present study provides psychophysiological evidence for classifying cognitive and motor tasks, which has been a controversial issue for task classifications in motor learning research.

The connectivity structure in cortical networks defines how information is transmitted and processed, and it is a source of the complex spatiotemporal patterns of network's development, and the process of creation and deletion of connections is continuous in the whole life of the organism. In this paper, we study how neural activity influences the growth process in neural networks. By using a two-dimensional activity-dependent growth model we demonstrated the neural network growth process from disconnected neurons to fully connected networks. For making quantitative investigation of the network's activity influence on its topological properties we compared it with the random growth network not depending on network's activity. By using the random graphs theory methods for the analysis of the network's connections structure it is shown that the growth in neural networks results in the formation of a well-known “small-world” network.

In this paper, a two-layer network is built to simulate the mechanism of visual selection and shifting based on the mapping dynamic model for instantaneous frequency. Unlike the differential equation model using limit cycle to simulate neuron oscillation, we build an instantaneous frequency mapping dynamic model to describe the change of the neuron frequency to avoid the difficulty of generating limit cycle. The activity of the neuron is rebuilt based on the instantaneous frequency and in this work, we use the first layer of neurons to implement image segmentation and the second layer of neurons to act as visual selector. The frequency of the second neuron (central neuron) is always changing, while central neuron resonates with the neurons corresponding to an object, the object is selected, then with the central neuron frequency changing, the selected object loses attention, the process goes on.

The dorsal column tracts (fasciculus gracilis and fasciculus cuneatus) are concerned with discriminative qualities of sensation. There are controversial descriptions related to the relations of dorsal column tracts with the dorsal horn laminae in text-books. The present study aims to define the laminae of the dorsal horn of the spinal cord that contribute fibers to the dorsal column tracts in the cervical, thoracic and lumbar spinal level. Series paraffin spinal cords sections of six formalin-embalmed adult human cadavers were evaluated. The present study shows that dorsal column tracts receive fiber contributions from laminae III and V and from Clarke’s dorsal nucleus at varying spinal levels. At upper cervical levels (C1–C4) fiber contributions were from lamina V and few from lamina III, and at lower cervical levels (C5–C8) there were, in addition to these laminae, also contributions from the Clarke’s dorsal nucleus. At upper thoracic levels (T1–T4) fiber contributions were from lamina V and few from Clarke’s dorsal nucleus. At lower thoracic (T5–T12) and lumbar levels (L1–L5), in contrast, fiber contributions were only from Clarke’s dorsal nucleus. The detailed knowledge of organization of the dorsal column tracts of the spinal cord may pave the way for future treatments of the spinal cord injuries.

Common spatial pattern (CSP) is a powerful algorithm for extracting discriminative brain patterns in motor imagery-based brain–computer interfaces (BCIs). However, its performance depends largely on the subject-specific frequency band and time segment. Accurate selection of most responsive frequency band and time segment remains a crucial problem. A novel evolutionary algorithm, the backtracking search optimization algorithm is used to find the optimal frequency band and the optimal combination of frequency band and time segment. The former is searched by a frequency window with changing width of which starting and ending points are selected by the backtracking optimization algorithm; the latter is searched by the same frequency window and an additional time window with fixed width. The three parameters, the starting and ending points of frequency window and the starting point of time window, are jointly optimized by the backtracking search optimization algorithm. Based on the chosen frequency band and fixed or chosen time segment, the same feature extraction is conducted by CSP and subsequent classification is carried out by Fisher discriminant analysis. The classification error rate is used as the objective function of the backtracking search optimization algorithm. The two methods, named BSA-F CSP and BSA-FT CSP, were evaluated on data set of BCI competition and compared with traditional wideband (8–30Hz) CSP. The classification results showed that backtracking search optimization algorithm can find much effective frequency band for EEG preprocessing compared to traditional broadband, substantially enhancing CSP performance in terms of classification accuracy. On the other hand, the backtracking search optimization algorithm for joint selection of frequency band and time segment can find their optimal combination, and thus can further improve classification rates.

Peripheral nerve injury requires a physical bridge across the lesion, which is limited by the insufficient supply of donor nerves. Here, we developed a new miniaturized bioreactor system for axon stretch growth. Dorsal root ganglia explants were first placed on two adjoining substrates and formed new synaptic connections. The axon bundles across the border between the top and bottom membranes were then stretched in a stepwise fashion by a microstepper motor system. After several days of stretch, the axon tracts could reach lengths that could develop into living nervous tissue constructs. In order to achieve appropriate neuronal culture to stimulate physiological conditions during axon stretch, we tested a variety of coating methods. Based on these results, the elongator substrates were coated with both poly-D-lysine and rat-tail collagen to maximize the number of axon bundles. Additionally, we found that increasing the axon stretch by 1$\mu$m at each step resulted in the highest stability. The bridging axons adapted to the stretch by increasing their length from 500$\mu$m to 5.94mm over 7 days of stretch growth. Immunocytochemical analysis confirmed that beta-III-tubulin, a major cytoskeletal constituent and neuronal marker, was present along axons. The findings demonstrate that bioreactor has the potential to generate transplant materials to address neural repair.

We propose a nonlinear dynamic model for an invasive electroencephalogram analysis that learns the optimal parameters of the neural population model via the Levenberg–Marquardt algorithm. We introduce the crucial windows where the estimated parameters present patterns before seizure onset. The optimal parameters minimizes the error between the observed signal and the generated signal by the model. The proposed approach effectively discriminates between healthy signals and epileptic seizure signals. We evaluate the proposed method using an electroencephalogram dataset with normal and epileptic seizure sequences. The empirical results show that the patterns of parameters as a seizure approach and the method is efficient in analyzing nonlinear epilepsy electroencephalogram data. The accuracy of estimating the optimal parameters is improved by using the nonlinear dynamic model.