Teaching a machine to sense its environment is one of the most intractable problems of computer science, but one European project is looking to nature for help in cracking the conundrum. It combined streams of sensory data to produce an adaptive, composite impression of surroundings in near real-time. The team brought together electronic engineers, computer scientists, neuroscientists, physicists, and biologists. It looked at basic neural models for perception and then sought to replicate aspects of these in silicon.
“The objective was to study sensory fusion in biological systems and then translate that knowledge into the creation of intelligent computational machines,” says Martin McGinnity, Professor of Intelligent Systems Engineering and Director of the Intelligent Systems Engineering Laboratory (ISEL) at the University of Ulster’s Magee Campus and coordinator of the Future and Emerging Technologies(FET) initiative-funded SENSEMAKER project of the IST programme.
SENSEMAKER took its inspiration from nature by trying to replicate aspects of the brain’s neural processes, which capture sensory data from eyes, ears and touch, and then combines these senses to present a whole picture of the scene or its environment. For example, sight can identify a kiwi, but touch can help tell if that kiwi is ripe, unripe or over-ripe.
To explore these aspects of biological perception SENSEMAKER first developed a model of human perception, based on the best available data from the biological and neurological sciences.
Biological neurons use short and sudden increases in voltage to send information. These signals are more commonly known as action potentials, spikes or pulses. Computer science calls the phenomenon Spiking Neural Networks. More traditional or classical artificial neural networks use a simpler model. “The traditional model of an artificial neural network is quite removed from biological neurons, while the spiking neural networks we used are more faithful to what happens in the real biological brain,” says Professor McGinnity.
Similarly, adaptation is another aspect of the biological model, known as plasticity, where data flows through new routes in the brain to add further resources to data capture. If repeated over time, this plasticity becomes learning, where well-travelled routes through the brain become established and reinforce the information that passes.
As the model was being established, the team developed hardware demonstrators to implement and test components of the overall sensory fusion system. One project partner, the Ruprecht Karl Universitaet in Heidelberg, focused on implementations based on classical traditional neural networks – essentially large arrays of simple threshold devices. In parallel the ISEL group used Field Programmable Gate Arrays (FPGAs) to implement large arrays of spiking neural networks for emulation of a number of components of the sensory system, particularly the visual processing element.
“FPGAs are hardware computing platforms that can be dynamically reconfigured and as such, are ideal for exploring artificial representations of biological neurons, since their ability to reconfigure can be exploited, to some extent to mimic the plasticity of biological networks of neurons,” says Professor McGinnity.
Spiking neurons are more biologically compatible compared to traditional classical neural networks, such as the McCulloch-Pitts threshold neuron, because the time between spikes and their cumulative effect determine when the neuron fires. By using an advanced FPGA computing platform, ISEL were able to implement large networks of spiking neurons and synapses, and test the biological approaches for sensory fusion. The FPGA approach allows for flexibility, both in terms of rapid prototyping and the ease with which different neuron models can be implemented and tested.
However, dedicated analogue or mixed analogue-digital circuitry allows for greater integration and lower power operations. To exploit these properties, the Heidelberg group developed a spiking neuron Application Specific Integrated Circuit device, so as to be able to emulate larger constituent components of biological sensory systems. A prototype device had been submitted for fabrication when the project completed, but when fabricated will be exploited in a follow-up European project.
