A few extracts from this webpage, interesting read and written by Peter Van Der Made himself...Brainchip can already identify and learn human voices, smart phones not far away IMO...
This design was used as the foundation for a US patent application. The patent has been granted (uspto.gov patent no. 8,250,011, granted in 2012). Since then additional functions have been added to improve the accuracy of the emulation, including long neurotransmitters and increased precision in STDP - BCM learning. After programming the FPGA fuse matrix the chip was tested in a sound recognition trial. The test setup consisted of a signal generator, an artificial cochlear (spectrum analyzer), an Actel experiment board containing the programmed ProAsic3 FPGA and a PC to monitor the process. The PC was connected to the experiment board's JTAG interface, enabling it to monitor all aspects of the design. The objective of this test was to prove the learning ability of the Synthetic Neuro-Anatomy design. Ten frequencies were selected, from 220 Hz up to 587 Hz at intervals that represented whole notes A, B, C, D, E, F, G,A',B',C'. These were applied one by one to the spectrum analyzer. The output of the spectrum analyzer was applied to the inputs of the synthetic neural matrix contained in the FPGA. At first none of the synthetic neurons responded to input signals. After exposing the synthetic neural matrix for several seconds to input pulses from the spectrum analyzer, changes in the synaptic registers were observed that indicated that the synthetic cells were learning. This process was continued for a total of nearly 6 minutes on all frequencies (30 seconds per frequency, plus set-up time). At this time the registers had settled, with synapses and neurons responding to the frequencies that they had learned. Next, the signal generator was disconnected and an audio signal of a recorded human voice was applied to the input of the spectrum analyzer. The synthetic neural matrix responded to the previously learned frequencies whenever they occurred in human speech. This indicates that the device is capable of autonomous learning from sensory input, and that the synthetic neurons perform the same function as the neurons in the auditory channel of the human midbrain. and short persistence
... The signal generator frequency was varied to simulate the frequency of common vowels in human speech. The synthetic neuro-anatomy learned to recognize ten sounds that were later also identified in a speech pattern. Learning time was less than 2 minutes. The design is highly repetitive, with each node an exact replica of every other node. It is therefore expected that the small scale design will scale quite well to a component containing at least 10,000 nodes. The connectome for the larger scale device will be the biological model of a cortical column. Obvious applications for this technology are in speech recognition, speaker recognition, and extraction of speech from a noisy background environment. Other experiments show that the devices can be successfully applied in applications such as visual image recognition, robotics, the emulation of brain modules on small desktop computers, autonomous learning machines used in exploration, unmanned vehicles, and robotics. The advantages of using a Synthetic Neuro-Anatomy device over a traditional programmed device are a shorter development track, better quality recognition, persistent learning after the initial commission of the system and the reusability of training models.
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