Neural networks-on-chip for hybrid bio-electronic systems

PhD ThesisBy modelling the brains computation we can further our understanding of its function and develop novel treatments for neurological disorders. The brain is incredibly powerful and energy e cient, but its computation does not t well with the traditional computer architecture developed over the previous 70 years. Therefore, there is growing research focus in developing alternative computing technologies to enhance our neural modelling capability, with the expectation that the technology in itself will also bene t from increased awareness of neural computational paradigms. This thesis focuses upon developing a methodology to study the design of neural computing systems, with an emphasis on studying systems suitable for biomedical experiments. The methodology allows for the design to be optimized according to the application. For example, di erent case studies highlight how to reduce energy consumption, reduce silicon area, or to increase network throughput. High performance processing cores are presented for both Hodgkin-Huxley and Izhikevich neurons incorporating novel design features. Further, a complete energy/area model for a neural-network-on-chip is derived, which is used in two exemplar case-studies: a cortical neural circuit to benchmark typical system performance, illustrating how a 65,000 neuron network could be processed in real-time within a 100mW power budget; and a scalable highperformance processing platform for a cerebellar neural prosthesis. From these case-studies, the contribution of network granularity towards optimal neural-network-on-chip performance is explored

A Review of Control Strategies in Closed-Loop Neuroprosthetic Systems

It has been widely recognized that closed-loop neuroprosthetic systems achieve more favourable outcomes for users then equivalent open-loop devices. Improved performance of tasks, better usability and greater embodiment have all been reported in systems utilizing some form of feedback. However the interdisciplinary work on neuroprosthetic systems can lead to miscommunication due to similarities in well established nomenclature in different fields. Here we present a review of control strategies in existing experimental, investigational and clinical neuroprosthetic systems in order to establish a baseline and promote a common understanding of different feedback modes and closed loop controllers. The first section provides a brief discussion of feedback control and control theory. The second section reviews the control strategies of recent Brain Machine Interfaces, neuromodulatory implants, neuroprosthetic systems and assistive neurorobotic devices. The final section examines the different approaches to feedback in current neuroprosthetic and neurorobotic systems

Digital neural circuits : from ions to networks

PhD ThesisThe biological neural computational mechanism is always fascinating to human beings since it shows several state-of-the-art characteristics: strong fault tolerance, high power efficiency and self-learning capability. These behaviours lead the developing trend of designing the next-generation digital computation platform. Thus investigating and understanding how the neurons talk with each other is the key to replicating these calculation features. In this work I emphasize using tailor-designed digital circuits for exactly implementing bio-realistic neural network behaviours, which can be considered a novel approach to cognitive neural computation. The first advance is that biological real-time computing performances allow the presented circuits to be readily adapted for real-time closed-loop in vitro or in vivo experiments, and the second one is a transistor-based circuit that can be directly translated into an impalpable chip for high-level neurologic disorder rehabilitations. In terms of the methodology, first I focus on designing a heterogeneous or multiple-layer-based architecture for reproducing the finest neuron activities both in voltage-and calcium-dependent ion channels. In particular, a digital optoelectronic neuron is developed as a case study. Second, I focus on designing a network-on-chip architecture for implementing a very large-scale neural network (e.g. more than 100,000) with human cognitive functions (e.g. timing control mechanism). Finally, I present a reliable hybrid bio-silicon closed-loop system for central pattern generator prosthetics, which can be considered as a framework for digital neural circuit-based neuro-prosthesis implications. At the end, I present the general digital neural circuit design principles and the long-term social impacts of the presented work

Computational models of object motion detectors accelerated using FPGA technology

The detection of moving objects is a trivial task when performed by vertebrate retinas, yet a complex computer vision task. This PhD research programme has made three key contributions, namely: 1) a multi-hierarchical spiking neural network (MHSNN) architecture for detecting horizontal and vertical movements, 2) a Hybrid Sensitive Motion Detector (HSMD) algorithm for detecting object motion and 3) the Neuromorphic Hybrid Sensitive Motion Detector (NeuroHSMD) , a real-time neuromorphic implementation of the HSMD algorithm. The MHSNN is a customised 4 layers Spiking Neural Network (SNN) architecture designed to reflect the basic connectivity, similar to canonical behaviours found in the majority of vertebrate retinas (including human retinas). The architecture, was trained using images from a custom dataset generated in laboratory settings. Simulation results revealed that each cell model is sensitive to vertical and horizontal movements, with a detection error of 6.75% contrasted against the teaching signals (expected output signals) used to train the MHSNN. The experimental evaluation of the methodology shows that the MH SNN was not scalable because of the overall number of neurons and synapses which lead to the development of the HSMD. The HSMD algorithm enhanced an existing Dynamic Background subtraction (DBS) algorithm using a customised 3-layer SNN. The customised 3-layer SNN was used to stabilise the foreground information of moving objects in the scene, which improves the object motion detection. The algorithm was compared against existing background subtraction approaches, available on the Open Computer Vision (OpenCV) library, specifically on the 2012 Change Detection (CDnet2012) and the 2014 Change Detection (CDnet2014) benchmark datasets. The accuracy results show that the HSMD was ranked overall first and performed better than all the other benchmarked algorithms on four of the categories, across all eight test metrics. Furthermore, the HSMD is the first to use an SNN to enhance the existing dynamic background subtraction algorithm without a substantial degradation of the frame rate, being capable of processing images 720 × 480 at 13.82 Frames Per Second (fps) (CDnet2014) and 720 × 480 at 13.92 fps (CDnet2012) on a High Performance computer (96 cores and 756 GB of RAM). Although the HSMD analysis shows good Percentage of Correct Classifications (PCC) on the CDnet2012 and CDnet2014, it was identified that the 3-layer customised SNN was the bottleneck, in terms of speed, and could be improved using dedicated hardware. The NeuroHSMD is thus an adaptation of the HSMD algorithm whereby the SNN component has been fully implemented on dedicated hardware [Terasic DE10-pro Field-Programmable Gate Array (FPGA) board]. Open Computer Language (OpenCL) was used to simplify the FPGA design flow and allow the code portability to other devices such as FPGA and Graphical Processing Unit (GPU). The NeuroHSMD was also tested against the CDnet2012 and CDnet2014 datasets with an acceleration of 82% over the HSMD algorithm, being capable of processing 720 × 480 images at 28.06 fps (CDnet2012) and 28.71 fps (CDnet2014)

Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations