Feature
Belgian researchers have found ways of mimicking the human brain to improve sensors and the way they pass data to central computers
The human brain is much more efficient than the world’s most powerful computers. A human brain with an average volume of about 1,260cm3 consumes about 12W (watts) of power.
Using this biological marvel, the average person learns a very large number of faces in very little time. It can then recognise one of those faces right away, regardless of the expression. People can also glance at a picture and recognise objects from a seemingly infinite number of categories.
Compare that to the most powerful supercomputer in the world, Frontier, which runs at Oak Ridge National Laboratory, spanning 372m2 and consuming 40 million watts of power at peak. Frontier processes massive amounts of data to train artificial intelligence (AI) models to recognise a large number of human faces, as long as the faces aren’t showing unusual expressions.
But the training process consumes a lot of energy – and while the resulting models run on smaller computers, they still use a lot of energy. Moreover, the models generated by Frontier can only recognise objects from a few hundred categories – for example, person, dog, car, and so on.
Scientists know some things about how the brain works. They know, for example, that neurons communicate with each other using spikes (thresholds of accumulated potential). Scientists have used brain probes to look deeply into the human cortex and register neuronal activity. Those measurements show that a typical neuron spikes only a few times per second, which is very sparse activation. On a very high level, this and other basic principles are clear. But the way neurons compute, the way they participate in learning, and the way connections are made and remade to form memories is still a mystery.
Nevertheless, many of the principles researchers are working on today are likely to be part of a new generation of chips that replace computer processing units (CPUs) and graphics processing units (GPUs) 10 or more years from now. Computer designs are also likely to change, moving away from what is called the von Neumann architecture, where processing and data are in different locations and share a bus to transfer information.
New architectures will, for example, collocate processing and storage, as in the brain. Researchers are borrowing this concept and other features of the human brain to make computers faster and more power efficient. This field of study is known as neuromorphic computing, and a lot of the work is being done at the Interuniversity Microelectronics Centre(Imec) in Belgium.
“We tend to think that spiking behaviour is the fundamental level of computation within biological neurons. There are much deeper lying computations going on that we don’t understand – probably down to the quantum level,” says Ilja Ocket, programme manager for Neuromorphic Computing at Imec.
“Even between quantum effects and the high-level behavioural model of a neuron, there are other intermediate functions, such as ion channels and dendritic calculations. The brain is much more complicated than we know. But we’ve already found some aspects we can mimic with today’s technology – and we are already getting a very big payback.”
There is a spectrum of techniques and optimisations that are partially neuromorphic and have already been industrialised. For example, GPU designers are already implementing some of what has been learned from the human brain; and computer designers are already reducing bottlenecks by using multilayer memory stacks. Massive parallelism is another bio-inspired principle used in computers – for example, in deep learning.
Nevertheless, it is very hard for researchers in neuromorphic computers to make inroads in computing because there is already too much momentum around traditional architectures. So rather than try to cause disruption in the computer world, Imec has turned its attention to sensors. Researchers at Imec are looking for ways to “sparsify” data and to exploit that sparsity to accelerate processing in sensors and reduce energy consumption at the same time.
“We focus on sensors that are temporal in nature,” says Ocket. “This includes audio, radar and lidar. It also includes event-based vision, which is a new type of vision sensor that isn’t based on frames but works instead on the principle of your retina. Every pixel independently sends a signal if it senses a significant change in the amount of light it receives.
“We borrowed these ideas and developed new algorithms and new hardware to support these spiking neural networks. Our work now is to demonstrate how low power and low latency this can be when integrated onto a sensor.”
Spiking neural networks on a chip
A neuron accumulates input from all the other neurons it is connected to. When the membrane potential reaches a certain threshold, the axon – the connection coming out of the neuron – emits a spike. This is one of the ways your brain performs computation. And this is what Imec now does on a chip, using spiking neural networks.
“We use digital circuits to emulate the leaky, integrate and fire behaviour of biological spiking neurons,” says Ocket. “They are leaky in the sense that while they integrate, they also lose a bit of voltage on their membrane; they are integrating because they accumulate spikes coming in; and they are firing because the output fires when the membrane potential reaches a certain threshold. We mimic that behaviour.”
The benefit of that mode of operation is that until data changes, no events are generated, and no computations are done in the neural network. Consequently, no energy is used. The sparsity of the spikes within the neural network intrinsically offers low power consumption because computing does not occur constantly.
A spiking neural network is said to be recurrent when it has memory. A spike is not just computed once. Instead, it reverberates into the network, creating a form of memory, which allows the network to recognise temporal patterns, similarly to what the brain does.
Using spiking neural network technology, a sensor transmits tuples that include the X coordinate and the Y coordinate of the pixel that’s spiking, the polarity (whether it’s spiking upward or downward) and the time it spikes. When nothing happens, nothing is transmitted. On the other hand, if things change in a lot of places at once, the sensor creates a lot of events, which becomes a problem because of the size of the tuples.
To minimise this surge in transmission, the sensor does some filtering by deciding on the bandwidth it should output based on the dynamics of the scene. For example, in the case of an event-based camera, if everything in a frame changes, the camera sends too much data. A frame-based system would handle that much better because it has a constant data rate. To overcome this problem, designers put a lot of intelligence on sensors to filter data – one more way of mimicking human biology.
“The retina has 100 million receptors, which is like having 100 million pixels in your eye,” says Ocket. “But the optical fibre that goes through your brain only carries a million channels. So, this means the retina carries out a 100 times compression – and this is real computation. Certain features are detected, like motion from left to right, from top to bottom, or little circles. We are trying to mimic the filtering algorithm that goes on the retina in these event-based sensors, which operate on the edge and feeds data back to a central computer. You might think of the computation going on in the retina as a form of edge AI.”
People have been mimicking spiking neurons in silicon since the 1980s. But the main obstacle preventing this technology from reaching a market or any kind of real application was training spiking neural networks as efficiently and conveniently as deep neural networks are trained. “Once you establish good mathematical understanding and good techniques to train spiking neural networks, the hardware implementation is almost trivial,” says Ocket.
In the past, people would build spiking into their network chips and then do a lot of fine-tuning to get the neural networks to do something useful. Imec took another approach, developing algorithms in software that showed that a given configuration of spiking neurons with a given set of connections would perform to a certain level. Then they built the hardware.
This kind of breakthrough in software and algorithms is unconventional for Imec, where progress is usually in the form of hardware innovation. Something else that was unconventional for Imec was that they did all this work in standard CMOS, which means their technology can be quickly industrialised.
The future impact of neuromorphic computing
“The next direction we’re taking is towards sensor fusion, which is a hot topic in automotive, robotics, drones and other domains,” says Ocket. “A good way of achieving very high-fidelity 3D perception is to combine multiple sensory modalities. Spiking neural networks will allow us to do that with low power and low latency. Our new target is to develop a new chip specifically for sensor fusion in 2023.
“We aim to fuse multiple sensor streams into a coherent and complete 3D representation of the world. Like the brain, we don’t want to have to think about what comes from the camera versus what comes from the radar. We are going for an intrinsically fused representation.
“We’re hoping to show some very relevant demos for the automotive industry – and for robotics and drones across industries – where the performance and the low latency of our technology really shines,” says Ocket. “First we’re looking for breakthroughs in solving certain corner cases in automotive perception or robotics perception that are aren’t possible today because the latency is too high, or the power consumption is too high.”
Two other things Imec expects to happen in the market are the use of event-based cameras and sensor fusion. Event-based cameras have a very high dynamic range and a very high temporal resolution. Sensor fusion might take the form of a single module with cameras in the middle, some radar antennas around it, maybe a lidar, and data is fused on the sensor itself, using spiking neural networks.
But even when the market takes up spiking neural networks in sensors, the larger public may not be aware of the underlying technology. That will probably change when the first event-based camera gets integrated into a smartphone.
“Let’s say you want to use a camera to recognise your hand gestures as a form of human-machine interface,” explains Ocket. “If that were done with a regular camera, it would constantly look at each pixel in each frame. It would snap a frame, and then decide what’s happening in the frame. But with an event-based camera, if nothing is happening in its field of view, no processing is carried out. It has an intrinsic wake-up mechanism that you can exploit to only start computing when there’s sufficient activity coming off your sensor.”
Human-machine interfaces could suddenly become a lot more natural, all thanks to neuromorphic sensing.
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FAQs
Are neural networks inspired by the human brain? ›
Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.
What inspired neural networks? ›Neural networks are inspired by the way the human brain works. A human brain can process huge amounts of information using data sent by human senses (especially vision).
What type of computer architecture modeled after the human brains network of neurons? ›Neuromorphic computing is a method of computer engineering in which elements of a computer are modeled after systems in the human brain and nervous system. The term refers to the design of both hardware and software computing elements.
What technology mimics the human brain? ›Neural network: A subset of machine learning that mimics the neurons in the human brain and how they signal to one another. Neural networks pass data through interconnected layers of nodes until the network creates the output. Neural networks are at the heart of deep learning algorithms.
Which theory the human brain was described as a neural network? ›The idea of neural networks began unsurprisingly as a model of how neurons in the brain function, termed 'connectionism' and used connected circuits to simulate intelligent behaviour . In 1943, portrayed with a simple electrical circuit by neurophysiologist Warren McCulloch and mathematician Walter Pitts.
What type of neural network is the human brain? ›The neurons in the human brain perform their functions through a massive inter-connected network known as synapses.
Which neural networks algorithms are inspired from the structure and functioning of the brain? ›Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.
What is the first generation of neural networks? ›The first artificial neural network was invented in 1958 by psychologist Frank Rosenblatt. Called Perceptron, it was intended to model how the human brain processed visual data and learned to recognize objects. Other researchers have since used similar ANNs to study human cognition.
How does a neural network imitate a human brain? ›How does a basic neural network work? A neural network is a network of artificial neurons programmed in software. It tries to simulate the human brain, so it has many layers of "neurons" just like the neurons in our brain. The first layer of neurons will receive inputs like images, video, sound, text, etc.
Which computer base model is developed after human brain? ›Neuromorphic chips are based on the human brain, with deeply connected artificial neurons and synapses.
Which of the following are the computer system inspired by the biological neural neural networks? ›
Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains.
What is the difference between human brain and artificial neuron network model? ›For one, human brains are far more complex and sophisticated than neural networks. Additionally, human brains are able to learn and adapt much more quickly than neural networks. Finally, human brains are able to generate new ideas and concepts, while neural networks are limited to the data they are given.
Which type of technology is used to study the brain? ›Magnetic resonance imaging (MRI) uses changes in electrically charged molecules in a magnetic field to form images of the brain. Both technologies are more precise than ordinary X-rays and can help find problems when people fall ill.
What technology is used in brain research? ›“Neurotechnology” refers to any technology that provides greater insight into brain or nervous system activity, or affects brain or nervous system function. Neurotechnology can be used purely for research purposes, such as experimental brain imaging to gather information about mental illness or sleep patterns.
Can a human brain be simulated on a computer? ›K computer and human brain
In late 2013, researchers in Japan and Germany used the K computer, then 4th fastest supercomputer, and the simulation software NEST to simulate 1% of the human brain. The simulation modeled a network consisting of 1.73 billion nerve cells connected by 10.4 trillion synapses.
A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain.
Who came up with the theory called the brain dominance theory where in the brain is divided into 4 quadrants *? ›Developed by Ned Herrmann, Whole Brain® Thinking divides the brain into four quadrants. Each quadrant represents a different part of the brain: Analytical, Practical, Relational, Experimental.
What type of computer system can recognize and act on patterns or trends that it detects in large sets of data and is developed to operate like the human brain? ›AI relies on algorithms to solve a problem or identify patterns in big data sets. Cognitive computing systems have the loftier goal of creating algorithms that mimic the human brain's reasoning process to solve problems as the data and the problems change.
Why is important for neurons to form neural network in human nervous system? ›In order for neurons to communicate, they need to transmit information both within the neuron and from one neuron to the next. This process utilizes both electrical signals as well as chemical messengers. The dendrites of neurons receive information from sensory receptors or other neurons.
What machine learning model is most closely related to a neuron in a neural network? ›Multilayer Perceptron (MLP) is a class of feed-forward artificial neural networks. The term perceptron particularly refers to a single neuron model that is a precursor to a larger neural network.
What are the characteristics of the neural network system of the human brain? ›
How Neural Networks Work. A simple neural network includes an input layer, an output (or target) layer and, in between, a hidden layer. The layers are connected via nodes, and these connections form a “network” – the neural network – of interconnected nodes. A node is patterned after a neuron in a human brain.
What type of neural networks has gates in the neural network that control the flow of information? ›Long Short-Term Memory (LSTM) Networks
LSTM is a type of RNN that is designed to handle the vanishing gradient problem that can occur in standard RNNs. It does this by introducing three gating mechanisms that control the flow of information through the network: the input gate, the forget gate, and the output gate.
Deep learning is a subset of machine learning (ML), where artificial neural networks—algorithms modeled to work like the human brain—learn from large amounts of data.
What is the term known as on which the neural network algorithms build a model based on sample data? ›Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.
What is the most common type of neural network? ›The four most common types of neural network layers are Fully connected, Convolution, Deconvolution, and Recurrent, and below you will find what they are and how they can be used.
What is the most basic type of neural network? ›Perceptron. The Perceptron is the most basic and oldest form of neural networks. It consists of just 1 neuron which takes the input and applies activation function on it to produce a binary output. It doesn't contain any hidden layers and can only be used for binary classification tasks.
What is a specific type of machine learning that uses layers of artificial neural networks to mimic brain functions? ›Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data.
Which of the following is an AI function that mimics the working of the human brain in processing data for use in detecting objects recognizing speech? ›Deep learning is an AI function that mimics the workings of the human brain in processing data for use in detecting objects, recognizing speech, translating languages, and making decisions.
Which are three types of machine learning? ›The three machine learning types are supervised, unsupervised, and reinforcement learning.
Which of the following is called the brain of the computer and its job is to carry out commands? ›The CPU is the brain of a computer, containing all the circuitry needed to process input, store data, and output results.
What was the name of the first model which simulated the working of human brain? ›
McCulloch-Pitts Neuron — Mankind's First Mathematical Model Of A Biological Neuron. It is very well known that the most fundamental unit of deep neural networks is called an artificial neuron/perceptron.
What is the state of AI where it can fully replicate the human brain called? ›Strong AI, also known as artificial general intelligence (AGI), describes programming that can replicate the cognitive abilities of the human brain.
What is the inspiration of artificial neural network and how does it work? ›An Artificial Neural Network (ANN) is a computational model inspired by networks of biological neurons, wherein the neurons compute output values from inputs.
What is the main similarity of the human brain and neural networks? ›The most obvious similarity between a neural network and the brain is the presence of neurons as the most basic unit of the nervous system. But the manner in which neurons take input in both cases is different.
What type of imaging is used by researchers to study the developing brain? ›Quantitative MRI (magnetic resonance imaging): Measures fetal brain tissue volume and brain fold development using a magnetic field and radio waves.
What is the main research method used to study the brain now? ›Such techniques include computed tomography (CT), positron emission tomography (PET) and single photon emission computed tomography (SPECT) scans.
What types of research are used to study the human brain? ›Magnetoencephalography (MEG) and electroencephalography (EEG) are two other types of functional neuroimaging techniques used to map brain activity. Some neuroimaging researchers combine data from more than one neuroimaging technique to create a more complete analysis of the patient's brain.
What is brain inspired intelligence technology? ›Brain-Inspired Intelligence explores systematic theories, methodologies, and techniques to imitate and enhance human intelligence, which has been paid long-lasting enthusiasms from human society.
Does the human brain have software? ›The biological brain does not require engineered software to function. It rather self-organises in a learning process through continuous interaction with the physical world.
How many computers does it take to simulate a human brain? ›Each of our brain cells could work like a mini-computer, according to the first recording of electrical activity in human cells at a super-fine level of detail.
What structure is neural network architecture inspired by? ›
Artificial Neural Networks (ANNs) make up an integral part of the Deep Learning process. They are inspired by the neurological structure of the human brain.
What is the origin of deep neural network? ›The history of deep learning can be traced back to 1943, when Walter Pitts and Warren McCulloch created a computer model based on the neural networks of the human brain.
On what structure is an artificial neural network based an artificial neural network is based on interconnected? ›An artificial neural network is an interconnected group of nodes, inspired by a simplification of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another.
What structure are artificial neural networks based on quizlet? ›To create AI systems using a bottom-up approach, artificial neural networks (ANNs) have been developed. - ANNs are constructed based on the structure of the human brain using loosely connected artificial neurons.
What are the 3 components of the neural network? ›There are typically three parts in a neural network: an input layer, with units representing the input fields; one or more hidden layers; and an output layer, with a unit or units representing the target field(s). The units are connected with varying connection strengths (or weights).
What is the architecture of a neural network? ›What Is Neural Network Architecture? The architecture of neural networks is made up of an input, output, and hidden layer. Neural networks themselves, or artificial neural networks (ANNs), are a subset of machine learning designed to mimic the processing power of a human brain.
How does a neural network work? ›A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain.
How many types of artificial neural networks are there? ›The 7 Types of Artificial Neural Networks ML Engineers Need to Know.