Dr. Monika Hofmann analyzes the principles and potential of neuromorphic engineering, including its many transformative applications.
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Neuromorphic Engineering is set to revolutionize the way we gather and process information. Combining principles from neuroscience, biology, computer science, and electrical engineering to create systems that can learn and adapt like humans, these brain-inspired systems are much faster and far more efficient at image recognition, pattern detection, and decision-making tasks. Leveraging the power of neural networks, they can handle massive amounts of data quickly and accurately. Current 'smart' sensors are generally sensors with a built-in computer. Neuromorphic Engineering aims to develop smart sensors with a built-in brain by combining bio-inspired sensors with bio-inspired signal processing to design and develop sensors and software that empower technology to rapidly see and understand the world.
By looking at how biology senses the world and how biological brains process information, neuromorphic engineers seek to build electronics and algorithms that tackle real-world problems with the power efficiency, robustness and reliability of biological systems. Dr Monika Hofmann has expanded her knowledge to the captivating field of neuromorphic engineering in a career spanning over two decades as the CEO and President of Axia Consult, a global strategy consulting and business development firm specializing in the space sector. In this article, Dr Hofmann explores this interdisciplinary field combining principles from neuroscience, biology, neurophysiology, cognitive psychology, mathematics, physics, computer science, and engineering to develop brain-inspired architectures designed to solve hard problems.
What is Neuromorphic Engineering?
At its core, it is a revolutionary approach of brain-inspired computing that mimics the structure and functionality of our brains in artificial systems. Neuromorphic sensors essentially emulate the way in which biological sensors work, most notably the visual system. Neuromorphic sensors can directly sense sudden changes, again much like the human eye can, and then send that imagery to be processed.
Neuromorphic Engineering is a sub-field of Electrical Engineering that aims to apply knowledge of how signals are processed in the brain to build electronic signal processing systems that vastly outperform current digital signal processing systems Leveraging neuroscience, it designs efficient computing systems for complex cognitive tasks. It focuses on neural networks of artificial neurons connected through synapses, like the biological brain. Thus, it enables machines to process data & execute computations with remarkable efficiency. It also incorporates principles of neuro-plasticity, allowing systems to adapt & learn from their environment.
Neuromorphic engineers draw from several disciplines - combining principles derived from computer science, biology, psychology, neurophysiology, mathematics, electronic engineering and physics - to create bio-inspired computer systems and hardware. Of the brain's biological structures, neuromorphic architectures are most often modeled after neurons and synapses. In essence one of the main questions that neuromorphic researchers seek to answer is: With all the stimuli constantly bombarding it, how does the brain determine what is out there in the world ? How does it determine what it needs to pay attention to and when?
Extensive investigations combined with theoretical, computational and electronic modeling studies are required to begin to discover how the brain achieves this. The object is to design and develop sensors and software that empower technology to rapidly see and understand the world in real time.
The possibilities of neuromorphic systems have been known for decades but computer chip design and production limitations restricted growth in a field that only now is being overcome by “bio-inspired spiking processor designs” that mimic the way in which the brain processes information.
For the full benefit of neuromorphic engineering, researchers and developers must collaborate with neuroscientists. This interdisciplinary approach provides a better understanding of the brain's functioning and ensures the development of accurate and efficient brain-inspired computing systems.
Understanding the Brain's Neural Networks
To understand the brain's neural networks, neuromorphic engineers must delve into the structure and function of neurons, and explore how to best emulate these networks in artificial systems. In essence they must reverse engineer the brain.
Simulating the brain starts with understanding the activity of a single neuron. From there, it quickly gets very complicated. To reconstruct the brain with computers, neuroscientists have to first understand how one brain cell communicates with another using electrical and chemical signals, and then describe these events using code. At this point, neuroscientists can begin to build digital copies of complex neural networks to learn more about how those networks interpret and process information.
Exploring the Structure and Function of Neurons
Neurons, the building blocks of the brain's neural networks, transmit electrical and chemical signals, enabling us to think, feel, and act. By studying their morphology and interconnectivity, we gain new insights into the brain.
Each neuron has a distinct structure. The cell body contains the nucleus and organelles. Dendrites extend from the cell body, receiving signals from other neurons. Signals travel through the axon, which may be wrapped in a myelin sheath. At synapses, neurons communicate by releasing neurotransmitters.
Neurons come in different shapes and sizes, allowing them to perform varied functions. Some have short structures for rapid processing in specific regions. Others have extensive branching patterns for long-distance communication. Certain features influence neuronal activity and synaptic plasticity.
Emulating Neural Networks in Artificial Systems
Mimicking the brain's intricate neural networks in artificial systems has long been a captivating field of research. Algorithms and architectures are designed to simulate neuron behavior. ANNs are used for speech recognition, image classification, and natural language processing.
Emulated neural networks possess the ability to learn from experience. This plasticity enables them to self-optimize, improving performance over time. In 1943, McCulloch and Pitts proposed a computational model that imitated neuron behavior. This was a historic milestone, laying the foundation for future research, and was followed by Hebb’s theories about how brains learn, guided primarily by a rule introduced in 1949 by the Canadian psychologist Donald Hebb, which is often paraphrased as “Neurons that fire together, wire together.”
Researchers now routinely use AI to better understand the brain and the research works both ways. Today, deep nets rule AI in part because of an algorithm called backpropagation, or backprop. The algorithm enables deep nets to learn from data, endowing them with the ability to classify images, recognize speech, translate languages, make sense of road conditions for self-driving cars, and accomplish a host of other tasks.
Researchers are now thinking about more biologically plausible learning mechanisms that might at least match the success of backpropagation in the human brain. Three of them - feedback alignment, equilibrium propagation and predictive coding- have shown particular promise. Other researchers are also incorporating the properties of certain types of cortical neurons and processes such as attention into their models. All these efforts are bringing us closer to understanding the algorithms that may be at work in the brain.
About Us: Dr. Monika Hofmann is renowned space consulting professional and the President and CEO of Axia Consult, a global strategy and business development firm. Dr. Hofmann’s career in space consulting spans across more than two decades.
Contact Info:
Name: Monika Hofmann
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Organization: Axia Consult
Website: https://monikahofmannfl.com/
Release ID: 89104006
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