ASIC Implementation of a Nonlinear Dynamical Model for Hippocampal Prosthesis.

A hippocampal prosthesis is a very large scale integration (VLSI) biochip that needs to be implanted in the biological brain to solve a cognitive dysfunction. In this letter, we propose a novel low-complexity, small-area, and low-power programmable hippocampal neural network application-specific integrated circuit (ASIC) for a hippocampal prosthesis. It is based on the nonlinear dynamical model of the hippocampus: namely multi-input, multi-output (MIMO)-generalized Laguerre-Volterra model (GLVM).

Evaluating functional connectivity of executive control network and frontoparietal network in Alzheimer's disease.

Investigating the early Alzheimer's disease (AD) more emphasizes sensitive and specific biomarkers, which can help the clinicians to monitor the progression and treatments of AD. Among these biomarkers, default mode network (DMN) functional connectivity is gaining more attention as a potential noninvasive biomarker to diagnose incipient Alzheimer's disease. However, besides changed functional connectivity of DMN, other functional networks haven't yet been examined systematically.

Functional brain networks reconstruction using group sparsity-regularized learning.

Investigating functional brain networks and patterns using sparse representation of fMRI data has received significant interests in the neuroimaging community. It has been reported that sparse representation is effective in reconstructing concurrent and interactive functional brain networks. To date, most of data-driven network reconstruction approaches rarely take consideration of anatomical structures, which are the substrate of brain function.

A memory efficient implementation scheme of Gauss error function in a Laguerre-Volterra network for neuroprosthetic devices.

Cognitive neural prosthesis is a manmade device which can be used to restore or compensate for lost human cognitive modalities. The generalized Laguerre-Volterra (GLV) network serves as a robust mathematical underpinning for the development of such prosthetic instrument. In this paper, a hardware implementation scheme of Gauss error function for the GLV network targeting reconfigurable platforms is reported.

An Application Specific Instruction Set Processor (ASIP) for Adaptive Filters in Neural Prosthetics.

Neural coding is an essential process for neuroprosthetic design, in which adaptive filters have been widely utilized. In a practical application, it is needed to switch between different filters, which could be based on continuous observations or point process, when the neuron models, conditions, or system requirements have changed. As candidates of coding chip for neural prostheses, low-power general purpose processors are not computationally efficient especially for large scale neural population coding.

Laguerre-Volterra model and architecture for MIMO system identification and output prediction.

A generalized mathematical model is proposed for behaviors prediction of biological causal systems with multiple inputs and multiple outputs (MIMO). The system properties are represented by a set of model parameters, which can be derived with random input stimuli probing it. The system calculates predicted outputs based on the estimated parameters and its novel inputs.

Personality, category, and cross-linguistic speech sound processing: a connectivistic view.

Category formation of human perception is a vital part of cognitive ability. The disciplines of neuroscience and linguistics, however, seldom mention it in the marrying of the two. The present study reviews the neurological view of language acquisition as normalization of incoming speech signal, and attempts to suggest how speech sound category formation may connect personality with second language speech perception.

A customizable stochastic state point process filter (SSPPF) for neural spiking activity.

Stochastic State Point Process Filter (SSPPF) is effective for adaptive signal processing. In particular, it has been successfully applied to neural signal coding/decoding in recent years. Recent work has proven its efficiency in non-parametric coefficients tracking in modeling of mammal nervous system.

Real-time prediction of neuronal population spiking activity using FPGA.

A field-programmable gate array (FPGA)-based hardware architecture is proposed and utilized for prediction of neuronal population firing activity. The hardware system adopts the multi-input multi-output (MIMO) generalized Laguerre-Volterra model (GLVM) structure to describe the nonlinear dynamic neural process of mammalian brain and can switch between the two important functions: estimation of GLVM coefficients and prediction of neuronal population spiking activity (model outputs). The model coefficients are first estimated using the in-sample training data; then the output is predicted using the out-of-sample testing data and the field estimated coefficients.

A dual mode FPGA design for the hippocampal prosthesis.

One important step towards the cognitive neural prosthesis design is to achieve real-time prediction of neuronal firing pattern. An FPGA-based hardware computational platform is designed to guarantee this hard real-time signal processing requirement. The proposed platform can work in dual modes: generalized Laguerre-Volterra model parameters estimation and output prediction, and can switch between these two important system functions.

A hardware-based computational platform for Generalized Laguerre-Volterra MIMO model for neural activities.

A parallelized and pipelined architecture based on FPGA and a higher-level Self Reconfiguration Platform are proposed in this paper to model Generalized Laguerre-Volterra MIMO system essential in identifying the time-varying neural dynamics underlying spike activities. Our proposed design is based on the Xilinx Virtex-6 FPGA platform and the processing core can produce data samples at a speed of 1.33 × 10(6)/s, which is 3.