Brainchip Holdings - Thought i would bring this to your attention

We are looking at the Worlds first and autonomous learning technology -


1) Patented in 2008 by Peter Van Der Made
They currently talking with fortune 500 companies about developments and Joint venture opportunities

2) Essentially BRAINCHIP replicates the biological behaviour of the human brain and uses 1/1000 of the consumption of power - The name of 3) The technology is SNAP (spiking neuron ADAPTIVE PROCESSOR )
Validated by some of the worlds pre- eminent neuroscientists

Spiking Neuron Adaptive Processor (SNAP)

BrainChip’s inventor, Peter van der Made, has created an exciting new Spiking Neural Networking technology that has the ability to learn autonomously, evolve and associate information just like the human brain. The technology is developed as a digital design containing a configurable “sea of biomimic neurons’.

The technology is fast, completely digital, and consumes very low power, making it feasible to integrate large networks into portable battery-operated products, something that has never been possible before.

BrainChip neurons autonomously learn through a process known as STDP (Synaptic Time Dependent Plasticity). BrainChip’s fully digital neurons process input spikes directly in hardware. Sensory neurons convert physical stimuli into spikes. Learning occurs when the input is intense, or repeating through feedback and this is directly correlated to the way the brain learns.

“Peter’s technology has the ability to learn autonomously and evolve.
It operates at the speed of the brain and has low power consumption.”
– Dr. Nick Spitzer, Director, Kavli Institute, California
Computing Artificial Neural Networks (ANNs)
The brain consists of specialized nerve cells that communicate with one another. Each such nerve cell is called a Neuron,. The inputs are memory nodes called synapses. When the neuron associates information, it produces a ‘spike’ or a ‘spike train’. Each spike is a pulse that triggers a value in the next synapse. Synapses store values, similar to the way a computer stores numbers. In combination, these values determine the function of the neural network. Synapses acquire values through learning.

In Artificial Neural Networks (ANNs) this complex function is generally simplified to a static summation and compare function, which severely limits computational power. BrainChip has redefined how neural networks work, replicating the behaviour of the brain. BrainChip’s artificial neurons are completely digital, biologically realistic resulting in increased computational power, high speed and extremely low power consumption.

The Problem with Artificial Neural Networks
Standard ANNs, running on computer hardware are processed sequentially; the processor runs a program that defines the neural network. This consumes considerable time and because these neurons are processed sequentially, all this delayed time adds up resulting in a significant linear decline in network performance with size.

BrainChip neurons are all mapped in parallel. Therefore the performance of the network is not dependent on the size of the network providing a clear speed advantage. So because there is no decline in performance with network size, learning also takes place in parallel within each synapse, making STDP learning very fast.

A hardware solution
BrainChip’s digital neural technology is the only custom hardware solution that is capable of STDP learning. The hardware requires no coding and has no software as it evolves learning through experience and user direction.

The BrainChip neuron is unique in that it is completely digital, behaves asynchronously like an analog neuron, and has a higher level of biological realism. It is more sophisticated than software neural models and is many orders of magnitude faster. The BrainChip neuron consists entirely of binary logic gates with no traditional CPU core. Hence, there are no ‘programming’ steps. Learning and training takes the place of programming and coding. Like of a child learning a task for the first time.

Software ‘neurons’, to compromise for limited processing power, are simplified to a point where they do not resemble any of the features of a biological neuron. This is due to the sequential nature of computers, whereby all data has to pass through a central processor in chunks of 16, 32 or 64 bits. In contrast, the brain’s network is parallel and processes the equivalent of millions of data bits simultaneously.

A significantly faster technology
Performing emulation in digital hardware has distinct advantages over software. As software is processed sequentially, one instruction at a time, Software Neural Networks perform slower with increasing size. Parallel hardware does not have this problem and maintains the same speed no matter how large the network is. Another advantage of hardware is that it is more power efficient by several orders of magnitude.

The speed of the BrainChip device is unparalleled in the industry.

For large neural networks a GPU (Graphics Processing Unit) is ~70 times faster than the Intel i7 executing a similar size neural network. The BrainChip neural network is faster still and takes far fewer CPU (Central Processing Unit) cycles, with just a little communication overhead, which means that the CPU is available for other tasks. The BrainChip network also responds much faster than a software network accelerating the performance of the entire system.

The BrainChip network is completely parallel, with no sequential dependencies. This means that the network does not slow down with increasing size.

Endorsed by the neuroscience community
A number of the world’s pre-eminent neuroscientists have endorsed the technology and are agreeing to joint develop projects.

BrainChip has the potential to become the de facto standard for all autonomous learning technology and computer products.

Patented
BrainChip’s autonomous learning technology patent was granted on the 21st September 2008 (Patent number US 8,250,011 “Autonomous learning dynamic artificial neural computing device and brain inspired system”). BrainChip is the only company in the world to have achieved autonomous learning in a network of Digital Neurons without any software.

A prototype Spiking Neuron Adaptive Processor was designed as a ‘proof of concept’ chip.

The first tests were completed at the end of 2007 and this design was used as the foundation for the US patent application which was filed in 2008. BrainChip has also applied for a continuation-in-part patent filed in 2012, the “Method and System for creating Dynamic Neural Function Libraries”, US Patent Application 13/461,800 which is pending.

Hardware only application so no software to impair its performance - milestone 1 ACHIEVED

https://www.google.com/patents/US20100076916

APPLICATIONS:

FIRST STAGE DEVELOPMENT APPLICATIONS
Obvious applications for this technology are in speech recognition, speaker recognition, and extraction of speech and sound from a noisy background environment. Other experiments show that the devices can also be successfully applied in applications such as visual image recognition, robotics and autonomous learning machines used in exploration and unmanned vehicles.

The advantages of using a Spiking Neuron Adaptive Processing (SNAP) device over a traditional programmed device are a shorter development path, faster response times, better quality recognition, persistent learning after the initial commission of the system and the re-usability of training models.

BrainChip’s ongoing development work is focused on the commercialisation of key applications that were prioritised after consultation and direction from potential technology partners located in California.The basis for these first stage developments is related to the requirements for specific applications from such companies. These discussions have focussed the BrainChip engineering department on:
Smart Phone
Smart Phone technology applications use unique voice and image identification capabilities to give the user a far greater interactive experience than ever before. These high volume applications, when development is completed, are expected to be licensed and generate royalties from leading smartphone chip manufacturers and will be used in smartphones, smart television sets and tablets.
Internet of Things (IoT)

IoT is a term used to describe the network of objects and people to the internet. ABI research (ABI) estimates there will be 30 billion devices connected to the Internet of Things by 2020. Applications for the IoT devices are broad and include Environmental monitoring, Manufacturing, Energy Management and many more. The estimated value of the IoT sector according to Cisco is US$14.6 trillion.

Robotic Technology
Hardware that is designed to interact with users visually and aurally could give the user an interactive experience which would be adapted for the individual and give a different and unique experience every time. Toys would be a good example of this application and as an added value, safety warning systems could also be inbuilt into the toys.

Limitless Possibilities
The BrainChip team has developed further interest for a more diverse range of applications from sectors including cyber security, gaming, driverless vehicles, and medical however, the capability of the technology is not limited to these sectors alone.

Read about BrianChipSolves the most fundamental problem in our neuro computing today

Applications are limitless:


The technology will be licensed which will provide "BRAINCHIP WITH A HIGH MARGIN BUSINESS"

https://www.google.com/patents/US20100076916