The Cell-Life Project: Converging technologies in the context of HIV/AIDS

This article presents the development of a technology initiative called Cell-Life which addresses the need for information management in the HIV/AIDS sector. Cell-Life started in 2001 as a research collaboration between staff of the Engineering Faculties at the University of Cape Town (UCT) and the Cape Peninsula University of Technology (CPUT). Based on the need to support the primary health care sector in providing sustainable treatment options for HIV+ people in under-resourced and rural areas, converging technologies were identified as a possible solution for creating a ‘virtual infrastructure’ between the patient and the medical staff. In 2003 the Government of South Africa clarified in its operational plan for HIV/AIDS that anti-retroviral treatment (ART) increased life expectancy of people living with AIDS. It also highlighted that provision of anti-retroviral drugs (ARVs) required the regular assessment of the compliance rate to the treatment plan in order to avoid side effects and multiple resistant strains. For under-resourced primary health care centres in disadvantaged areas, HIV/AIDS treatment, and particularly the requirement to monitor patients regularly, became a near impossible task. Cell-Life investigated the use of readily available information and communication technologies to support the provision and distribution of medication, continuous patient monitoring, and communication of relevant data. By combining open source software, cellular technologies and a new approach to software design, a variety of solutions were developed that would take cognisance of the context of HIV/AIDS support and treatment across the country. In 2006 Cell-Life became a not-for-profit organisation and was spun out of the University of Cape Town. The organization currently implements Information Communication Technology (ICT) systems that (as of late 2009) manage the dispensation of ARVs to approximately 70 000 patients, representing one-sixth of South Africans on state- or donor-sponsored ART. This article reflects on the development of Cell-Life as a case study for one of the first socially responsible research projects in the Engineering field at UCT and highlights some of the challenges, enablers and barriers experienced

Learning the Pseudoinverse Solution to Network Weights

The last decade has seen the parallel emergence in computational neuroscience and machine learning of neural network structures which spread the input signal randomly to a higher dimensional space; perform a nonlinear activation; and then solve for a regression or classification output by means of a mathematical pseudoinverse operation. In the field of neuromorphic engineering, these methods are increasingly popular for synthesizing biologically plausible neural networks, but the "learning method" - computation of the pseudoinverse by singular value decomposition - is problematic both for biological plausibility and because it is not an online or an adaptive method. We present an online or incremental method of computing the pseudoinverse, which we argue is biologically plausible as a learning method, and which can be made adaptable for non-stationary data streams. The method is significantly more memory-efficient than the conventional computation of pseudoinverses by singular value decomposition.Comment: 13 pages, 3 figures; in submission to Neural Network

A compact aVLSI conductance-based silicon neuron

We present an analogue Very Large Scale Integration (aVLSI) implementation that uses first-order lowpass filters to implement a conductance-based silicon neuron for high-speed neuromorphic systems. The aVLSI neuron consists of a soma (cell body) and a single synapse, which is capable of linearly summing both the excitatory and inhibitory postsynaptic potentials (EPSP and IPSP) generated by the spikes arriving from different sources. Rather than biasing the silicon neuron with different parameters for different spiking patterns, as is typically done, we provide digital control signals, generated by an FPGA, to the silicon neuron to obtain different spiking behaviours. The proposed neuron is only ~26.5 um2 in the IBM 130nm process and thus can be integrated at very high density. Circuit simulations show that this neuron can emulate different spiking behaviours observed in biological neurons.Comment: BioCAS-201

A Reconfigurable Mixed-signal Implementation of a Neuromorphic ADC

We present a neuromorphic Analogue-to-Digital Converter (ADC), which uses integrate-and-fire (I&F) neurons as the encoders of the analogue signal, with modulated inhibitions to decohere the neuronal spikes trains. The architecture consists of an analogue chip and a control module. The analogue chip comprises two scan chains and a twodimensional integrate-and-fire neuronal array. Individual neurons are accessed via the chains one by one without any encoder decoder or arbiter. The control module is implemented on an FPGA (Field Programmable Gate Array), which sends scan enable signals to the scan chains and controls the inhibition for individual neurons. Since the control module is implemented on an FPGA, it can be easily reconfigured. Additionally, we propose a pulse width modulation methodology for the lateral inhibition, which makes use of different pulse widths indicating different strengths of inhibition for each individual neuron to decohere neuronal spikes. Software simulations in this paper tested the robustness of the proposed ADC architecture to fixed random noise. A circuit simulation using ten neurons shows the performance and the feasibility of the architecture.Comment: BioCAS-201

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