What is Neuromorphic Computing
Neuromorphic Computing is a new approach to Artificial Intelligence. Neuromorphic Computing is one of the most active research areas in computational neuroscience, combining techniques from neural modeling, hardware design, and systems neuroscience. Neuromorphic is artificial intelligence that functions as our brain does. The idea behind Neuromorphic Computing is that it’s based on how your brain works- instead of trying to figure out what you’re thinking about by analyzing images or text, Neuristic Computers can think for themselves using their understanding of how things work in the real world.
The term “neuro” means nerve cells while “morphic” refers to the shape, so Neuromorphic Computing means an artificial intelligence modeled after human neurons!
What makes Neuristic Computers so unique?
Neurable technology has the remarkable ability to mimic how your brain works. Neuromorphic Computing uses its understanding of how things work in the real world rather than trying to figure out what you’re thinking about by analyzing images or text. Neuromorphic have the ability of self-thinking, which is a key factor in artificial intelligence that currently, only humans are capable of doing.
Neurable technology has been used for virtual reality and augmented reality headsets allowing them to respond intuitively rather than be programmed. In addition, neuromorphic technology can be trained on its own rather than pre-programmed for specific tasks. Once you have taught it how to do one thing, Neurable can adapt and change almost immediately!
What are some benefits of Neuromorphic Computing?
Neural Networks work more like actual brains; Neuromorphic Computing can mimic human brain functions. Neurable technology is not designed for a specific task but rather the ability to adapt and change based on its surroundings which means it’s self-learning! Neurable has been used in virtual reality and augmented headsets allowing them to respond intuitively rather than programmed. Neuromorphic technology can be trained independently rather than pre-programmed for specific tasks, which means that once you have taught Neurable technology how to do one thing, it can adapt and change almost immediately!
How Does Neuromorphic Computing Work?
Neuromorphic Computing works by using the brain’s processes to understand how things work in the real world. Neurable technology has been used for virtual reality and augmented reality headsets allowing them to respond intuitively rather than be programmed. Neuromorphic Technology can mimic human brain functions; it operates more like actual brains, where neural networks operate more like computers.
Examples of Companies That Are Using Neuromorphic Computing in Their Products
Neurable has been used for virtual reality and augmented headsets allowing Neuromorphic Technology to respond intuitively rather than having to be programmed in order to create artificial intelligence. Neurable technology will enable devices like the HTC Vive or Oculus Rift, which use virtual reality (VR) technologies, to be controlled by human thought using brain-computer interfaces (BCI).
The company is also working on a brain-computer interface that allows people with disabilities such as ALS, or Lou Gehrig’s disease, which causes the death of neurons controlling voluntary muscles, to control devices like drones or wheelchairs.
Another example of a company using neuromorphic computing technology is IBM’s TrueNorth chip, which was designed for deep learning at a large scale with low power consumption.
IBM’s TrueNorth neuromorphic computing system was designed by leveraging insights from neuroscience research at IBM and understanding the real-time computing demands for cognitive applications. The chip has a million neurons and 256 million synapses, with 4096 neurosynaptic cores connected via electronic versions of the spike-timing-dependent plasticity rule that is one of the basic rules for how our brains work.
Challenges of Implementing A System With Neuromorphic Capabilities
The challenges of implementing a system with neuromorphic capabilities are that there aren’t many applications available since the technology has only been around for about five years. Other challenges of creating artificial intelligence with Neurable technology include memory and storage requirements, power consumption, and connectivity – particularly in wireless solutions such as TrueNorth, which operate on very little power but require fast data connections to communicate with other devices.
A few challenges of implementing a system with neuromorphic capabilities include memory and storage requirements, power consumption, and connectivity – particularly in wireless solutions such as TrueNorth, which operate on very little power but require fast data connections to communicate with other devices.
How to Get Started With Building Your Design For An Artificial Intelligence System That Utilizes This Technology
To get started with building your design for an artificial intelligence system that utilizes this technology, you will need to understand how neuromorphic Computing works. First of all, to use Neurable technology requires a minimum set up which includes the following:
- A VR headset like HTC Vive or Oculus Rift
- Neurons – The equivalent of computer memory and processing power
- Synapses – The connections in the brain enable neurons to communicate with each other like a computer’s system bus, allowing information to flow from one place to another.
Once you have set up your VR headset and started programming it using Neurable technology, programmable devices such as Raspberry Pi can be used by students, scientists, and researchers to explore the possibilities of neuromorphic technologies.
Programmable devices like Raspberry Pi can be used by students, scientists, and researchers to start exploring the possibilities of neuromorphic technologies once you have set up your VR headset using Neurable technology.
Future Applications For This Type of Technology
Future applications for this type of technology include:
- Improving the capabilities of self-driving cars.
- Using neuromorphic Computing to improve medical imaging and diagnostics.
- Robotics that respond more accurately in different situations.
Another future application includes building virtual assistants that can understand human speech better than current voice recognition systems like Amazon Alexa or Google Assistant by utilizing brain activity data rather than the traditional matching sounds to words.
Another future application is building virtual assistants that can understand human speech better than current voice recognition systems like Amazon Alexa or Google Assistant by utilizing brain activity data rather than the traditional matching sounds to words.
This blog post will include a summary of the key points and interesting facts about neuromorphic Computing.
Artificial intelligence (AI) is one of the most popular topics in technology today. Still, there are some significant challenges to be overcome when building an artificial brain that can respond like a human’s, which is where Neuromorphic Computing comes in.