Mechanisms Gating the Flow of Information in the Cortex: What They Might Look Like and What Their Uses may be

The notion of gating as a mechanism capable of controlling the flow of information from one set of neurons to another, has been studied in many regions of the central nervous system. In the nucleus accumbens, where evidence is especially clear, gating seems to rely on the action of bistable neurons, i.e., of neurons that oscillate between a quiescent “down” state and a firing “up” state, and that act as AND-gates relative to their entries. Independently from these observations, a growing body of evidence now indicates that bistable neurons are also quite abundant in the cortex, although their exact functions in the dynamics of the brain remain to be determined. Here, we propose that at least some of these bistable cortical neurons are part of circuits devoted to gating information flow within the cortex. We also suggest that currently available structural, electrophysiological, and imaging data support the existence of at least three different types of gating architectures. The first architecture involves gating directly by the cortex itself. The second architecture features circuits spanning the cortex and the thalamus. The third architecture extends itself through the cortex, the basal ganglia, and the thalamus. These propositions highlight the variety of mechanisms that could regulate the passage of action potentials between cortical neurons sets. They also suggest that gating mechanisms require larger-scale neural circuitry to control the state of the gates themselves, in order to fit in the overall wiring of the brain and complement its dynamics

Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images

A new methodology for automatic feature extraction from biomedical images and subsequent classification is presented. The approach exploits the spatial orientation of high-frequency textural features of the processed image as determined by a two-step process. First, the two-dimensional discrete wavelet transform(DWT) is applied to obtain the HH high-frequency subband image. Then, a Gabor filter bank is applied to the latter at different frequencies and spatial orientations to obtain new Gabor-filtered image whose entropy and uniformity are computed. Finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier. The approach was validated on mammograms, retina, and brain magnetic resonance (MR) images.The obtained classification accuracies show better performance in comparison to common approaches that use only the DWT or Gabor filter banks for feature extraction

Perception SoC based on an ultrasonic array of sensors : efficient DSP core implementation and subsequent experimental results

We are concerned with the design, implementation, and validation of a perception SoC based on an ultrasonic array of sensors. The proposed SoC is dedicated to ultrasonic echography applications. A rapid prototyping platform is used to implement and validate the new architecture of the digital signal processing (DSP) core. The proposed DSP core efficiently integrates all of the necessary ultrasonic B-mode processing modules. It includes digital beamforming, quadrature demodulation of RF signals, digital filtering, and envelope detection of the received signals. This system handles 128 scan lines and 6400 samples per scan line with a angle of view span. The design uses a minimum size lookup memory to store the initial scan information. Rapid prototyping using an ARM/FPGA combination is used to validate the operation of the described system. This system offers significant advantages of portability and a rapid time to market

Intuitive wireless control of a robotic arm for people living with an upper body disability

Assistive Technologies (ATs) also called extrinsic enablers are useful tools for people living with various disabilities. The key points when designing such useful devices not only concern their intended goal, but also the most suitable human-machine interface (HMI) that should be provided to users. This paper describes the design of a highly intuitive wireless controller for people living with upper body disabilities with a residual or complete control of their neck and their shoulders. Tested with JACO, a six-degree-of-freedom (6-DOF) assistive robotic arm with 3 flexible fingers on its end-effector, the system described in this article is made of low-cost commercial off-the-shelf components and allows a full emulation of JACO's standard controller, a 3 axis joystick with 7 user buttons. To do so, three nine-degree-of-freedom (9-DOF) inertial measurement units (IMUs) are connected to a microcontroller and help measuring the user's head and shoulders position, using a complementary filter approach. The results are then transmitted to a base-station via a 2.4-GHz low-power wireless transceiver and interpreted by the control algorithm running on a PC host. A dedicated software interface allows the user to quickly calibrate the controller, and translates the information into suitable commands for JACO. The proposed controller is thoroughly described, from the electronic design to implemented algorithms and user interfaces. Its performance and future improvements are discussed as well

Encoding Static and Temporal Patterns with a Bidirectional Heteroassociative Memory

Brain-inspired, artificial neural network approach offers the ability to develop attractors for each pattern if feedback connections are allowed. It also exhibits great stability and adaptability with regards to noise and pattern degradation and can perform generalization tasks. In particular, the Bidirectional Associative Memory (BAM) model has shown great promise for pattern recognition for its capacity to be trained using a supervised or unsupervised scheme. This paper describes such a BAM, one that can encode patterns of real and binary values, perform multistep pattern recognition of variable-size time series and accomplish many-to-one associations. Moreover, it will be shown that the BAM can be generalized to multiple associative memories, and that it can be used to store associations from multiple sources as well. The various behaviors are the result of only topological rearrangements, and the same learning and transmission functions are kept constant throughout the models. Therefore, a consistent architecture is used for different tasks, thereby increasing its practical appeal and modeling importance. Simulations show the BAM's various capacities, by using several types of encoding and recall situations

Multi-Dimension Attention for Multi-Turn Dialog Generation (Student Abstract)

We present a generative neural model for open and multi-turn dialog response generation that relies on a multi-dimension attention process to account for the semantic interdependence between the generated words and the conversational history, so as to identify all the words and utterances that influence each generated response. The performance of the model is evaluated on the wide scope DailyDialog corpus and a comparison is made with two other generative neural architectures, using several machine metrics. The results show that the proposed model improves the state of the art for generation accuracy, and its multi-dimension attention allows for a more detailed tracking of the influential words and utterances in the dialog history for response explainability by the dialog history