SECURE VOICE SIGNATURE COMMUNICATIONS SYSTEM

Abstract

Embodiments of the present invention provides a system and a method for connecting two or more parts of a distributed and spatio-temporal spiking neural network by a means of communication, such as the Internet, used for recognizing and identifying acoustic signals using acoustic signature recognition by means of a spatio-temporal neural network. The first artificial intelligent device identifies features in a series of spatio-temporal pulse streams received from an artificial cochlear, and learns to respond to the pulse streams. The features of the pulse stream identifying an event learned by the first artificial intelligent device are transmitted to the remote artificial intelligent device over a communication protocol via a Series Address Event Representation bus, where the remote artificial intelligent device learns to respond. Further, a computing device may be connected to the remote artificial intelligent device for analyzing and controlling one or more appliances from anywhere in the world.

Description

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims benefit to U.S. Provisional Patent Application Ser. No. 62/018,562, filed on Jun. 28, 2014 and claims benefit to U.S. Provisional Patent Application Ser. No. 62/019,399, filed on Jun. 30, 2014, the disclosures of each of which are hereby incorporated by reference in their entirety.

FIELD OF INVENTION

The present invention generally relates to communications system over a communication network. In particular, the present invention relates to a system and method to couple two or more parts of a spatio-temporal spiking neural network, such as to provide acoustic signature identification or other cognitive tasks and the related communications between two or more components of a spatio-temporal spiking neural network.

BACKGROUND OF INVENTION

Neural networks have been around since the mid-1940's. Since then, many different neural models have been conceived with the aim to reproduce the considerable cognitive powers of the brain. These Artificial Neural Networks vary from simple sigmoid networks (perceptron) to complex biologically-accurate Spatio-temporal Spiking Neural Networks. It becomes increasingly difficult to determine the distinctions between these networks. For the purpose of clarity, Neural Networks can be divided into four distinct groups;

• • Computer software neural networks also referred to as ‘Sigmoid Networks’
  • Computer software simulation of biologically realistic neural networks
  • Analog hardware emulation of a sigmoid networks or spiking neural networks
  • Digital hardware logic emulation of sigmoid networks or spiking neural networks

Within each of these categories a number of variations exist that differ in accuracy and computational efficacy. Computer software neural networks generally exist of sigmoid function neurons wherein activated values are added and a non-linear mathematical function, such as a logistics function, is applied to the sum. Computer software simulations of biologically accurate neuron models, such as the Hodgkin-Huxley model of the giant squid neuron, focus on building accurate mathematical models of biological networks. In Analog hardware networks, a model is constructed in VLSI, consisting of transistors that behave more or less like synapses and an Integrate and Fire neural network. In Digital hardware emulation, the same is accomplished using logic gates. Each variation has its specific advantages and application areas.

The above mentioned technologies lack in efficiently capturing the dynamics of a biological neuronal networks, including spike-timing dependent plasticity, the effects of neurotransmitters and neuromodulators and diverse spike timing representations. Further, the present technologies do not provide computational retrieving and storing learned tasks, so that they may be able to be re-learned by another dynamic neural network. Additionally, the technologies lack in fast speeds and stability.

Further, the present technologies do not describe communications between two or more components of artificial neural network, that may be implemented in various applications, such as communication to a remote neural network irrespective of the location, controlling and analyzing a connected appliance or equipment.

Therefore, there exist a need to provide a system and a method to capture dynamics of biological neuronal networks, including spike-timing dependent plasticity, and couple two or more parts of a spatio-temporal spiking neural network, such as to provide cognitive tasks, and resultantly provide communication between them.

SUMMARY OF INVENTION

Therefore, the present invention relates to a system and a method for providing communication between two or more components of a spatio-temporal spiking neural network, such as to provide acoustic signature identification or other cognitive tasks and the related communications between two or more components of a spatio-temporal spiking neural network.

An embodiment of the present invention provides an apparatus for identifying and learning acoustic signature of a plurality of auditory signals comprising: an input sensor configured to capture a varying potential produced in the plurality of auditory signals from an auditory source; a series of resonators configured to convert the varying potential into a plurality of streams of electrical pulses with spatio-temporal distribution; an artificial intelligent device identifies one or more features of the pulse streams of acoustic signals, representing the acoustic signature, by association in a dynamic spatio-temporal neural network; and the artificial intelligent device learns to respond to the acoustic signature of the acoustic signals by modifying synaptic strengths in the dynamic spatio-temporal neural network.

Another embodiment of the present invention provides a communication system comprising: a first artificial intelligent device consisting of a dynamic spatio-temporal neural network configured to receive and learn from a plurality of pulse streams of acoustic signals with spatio-temporal distribution; and at least one remote artificial intelligent device consisting of a dynamic spatio-temporal neural network communicating with the first artificial intelligent device, through a communication channel, configured to receive and respond to the pulse streams.

Yet another embodiment of the invention provides a method for identifying and learning acoustic signature of a plurality of auditory signals comprising: capturing, by an input sensor, a varying potential produced in the plurality of auditory signals from an auditory source; converting the varying potential into a plurality of streams of electrical pulses with spatio-temporal distribution, by a series of resonators; identifying, by an artificial intelligent device, one or more features of the pulse streams of acoustic signals, representing the acoustic signature, through association in a dynamic spatio-temporal neural network; and learning, by the artificial intelligent device, to respond to the acoustic signature of the acoustic signals by modifying synaptic strengths in the dynamic spatio-temporal neural network.

An embodiment of the present invention discloses communication to a remote spatio-temporal spiking neural network for receiving spatio-temporal pulse streams via a parallel or serial communication such as the Internet.

Another embodiment of the present invention discloses a communication system in order to analyze and control an appliance irrespective of the location.

Yet another embodiment of the present invention provides a secure communication system because each spatio-temporal neural network is trained to respond to a unique voice signature.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates a system for communicating two or more components of a dynamic spatio-temporal neural network, in accordance with an embodiment of the present invention.

FIG. 2 illustrates a method of learning and identifying acoustic signature of auditory signals using a dynamic spatio-temporal neural network, in accordance with an embodiment of the present invention.

FIG. 3 illustrates a method of communicating two or more components of a dynamic spatio-temporal neural network, in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

In the following detailed description of embodiments of the invention, numerous specific details are set forth in order to provide a thorough understanding of the embodiment of invention. However, it will be obvious to a person skilled in art that the embodiments of invention may be practiced with or without these specific details. In other instances well known methods, procedures and components have not been described in details, so as not to unnecessarily obscure aspects of the embodiments of the invention.

Furthermore, it will be clear that the invention is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions and equivalents will be apparent to those skilled in the art, without parting from the spirit and scope of the invention.

According to the present invention, the Dynamic Spatio-Temporal Neural Network model described here captures the dynamics of biological neuronal networks, including spike-timing dependent plasticity, the effects of neurotransmitters and neuromodulators and diverse spike timing representations, allowing the model to perform functions that are presently beyond the scope of Artificial Neural Networks.

The Digital neural spiking neural networks and particularly the Dynamic Spatio-Temporal Neural Network offer a number of advantages over the prior art technologies. The neural model exhibits higher speed, excellent stability and very low power dissipation due to an asynchronous digital organization, with a further advantage that learned functions can be computationally retrieved and stored. Similar network models and libraries are disclosed in U.S. Pat. No. 8,250,011 and US Pub. No. 2013/0297537, both of which were invented by the current inventor incorporated by reference herein.

Power dissipation is extremely low. The digital circuit is very stable and its behavior is completely repeatable. Its characteristics are not dependent on any process technology. The neural model is elaborate, including the effects of spike interval neuro-transmitter reuptake, leakage currents, synaptic plasticity, and threshold plasticity.

Learning takes place through the modification of neurotransmitter levels resulting from feedback from the post-synaptic neuron, and the intensity of input spikes. Synaptic variables can be computationally retrieved through a microprocessor interface enabling the storage and subsequent reuse of learned tasks.

In the present invention, a system is provided that comprises an artificial intelligent device, consisting of dynamic spatio-temporal neural network, for identifying cognitive signals and learning the ways to respond to the cognitive signals. Further, the system provides for transmitting the signals to and hence, communicating with a remote artificial intelligent device, consisting of dynamic spatio-temporal neural network, over a communication protocol, such as the Internet. Additionally, the system disclosed in the present invention provides for analyzing and controlling one or more appliances from a remote location. Due to the communication protocol, the Internet, a user may be able to analyze or control his/her appliances from a remote location also by utilizing the artificial intelligent device of dynamic spatio-temporal neural network that recognizes, indentifies and learns to respond in a particular way to particular signals with respect to a particular user.

FIG. 1 illustrates a system for communicating two or more components of a dynamic spatio-temporal neural network, in accordance with an embodiment of the present invention.

The present invention provides a system comprising an artificial intelligent device 104 of dynamic spatio-temporal neural network. The artificial intelligent device 104 receives a plurality of input pulses in form of stimuli. Stimuli may be in form of auditory signals, human speech, animal, or any object, or any other type of stimuli generated from different event such as, object recognition, hand or body movement, and others. The system 100 relates to an artificial device 104 that receives input pulses in the form of auditory signals, and recognizes and identifies the input signals by implementing the spatio-temporal neural network comprised in the artificial intelligent device 104. Subsequently, the artificial intelligent device 104 learns to respond to the input pulses.

In biological sound sensing system, sound produces pressure waves in a cochlear that stimulates sensory neurons. The sensory neurons each produce a potential that varies at the same rate as the sound pressure waves of various frequencies. Since the artificial neural network approximates the biological neural network, the artificial intelligent device 104 receives plurality of input pulses, in the form of acoustic signals or sound waves.

One or more acoustic signals are captured by an input sensor from an auditory source such as the human voice, animal or insect activity or mechanical vibration. The input sensor is further connected to an artificial cochlear 102 consisting of a series of resonators. The sound waves in the acoustic signals produces pressure waves in the artificial cochlear 102 that stimulates sensory neurons in the cochlear 102. As mentioned above, the sensory neurons each produce a potential that varies at the same rate as the sound pressure waves of various frequencies. The artificial cochlear 102 outputs a plurality of pulse patterns with a spatio-temporal distribution that is indicative of features in the acoustic signals. The artificial cochlear 102 connects to a first artificial intelligent device 104. The first artificial intelligent device 104 consists of a spatio-temporal neural network.

The varying potential produced at the artificial cochlear 102 connects to a series of resonators that approximates the function of hair cells in the biological cochlear, producing a series of spatio-temporal pulses that are equivalent to the action potentials in the biological cochlear nerve. Further, the pulse streams with a spatio-temporal distribution are input to the connected artificial intelligent device 104 that consists of a Spatio-Temporal Neural Network. The Spatio-Temporal Neural Network in the artificial intelligent device 104 is subsequently trained to respond to a limited set of acoustic signatures.

The method of identifying acoustic signatures is by association in a Dynamic Spatio-Temporal Neural Network. Each synapse circuit in the Spatio-Temporal Neural Network of the artificial intelligent device 104 performs a temporal integration function on a single pulse stream. The Dendrite is a mechanism that provides spatial integration functions in a biological neuron. In the Spatio-Temporal Neural network, the values resulting from temporal integration in each artificial synapse circuit, the result of pulse interval and pulse frequency are integrated in an artificial dendrite circuit. Multiple synapse circuits are connected to each dendrite circuit, each synapse generating a single value. These values are spatially integrated in the dendrite circuit. One or more dendrite circuits connect to a soma circuit. In biological neurons, the soma consists of the nucleus of the cell and performs a global integration function. The artificial soma circuit integrates the values from a plurality of dendric circuits. The soma circuit connects to an n artificial axon circuit generates an output pulse sequence that is proportional to the total of integrated dendric values.

Synapses receive feedback from the Post Synaptic Neuron. The state of synapse circuit is changed according to the timing of input and feedback pulses. This comprises a learning function, known as Synaptic Time Dependent Plasticity. In this way by means of Synaptic Time Dependent Plasticity, the spatio-temporal neural network of the artificial intelligent device 104 is trained by exposing it to acoustic pattern information through the input sensor. Once a particular acoustic signature has been learned, the learning function can be mitigated by applying a value that represents a Neuromodulator. The acoustic signature is internally represented as pulses, also known as spikes, with spatio-temporal distribution.

Therefore, the system 100 provides an artificial intelligent device 104 consisting of spatio-temporal neural network that recognizes and identifies acoustic signature in one or more auditory signals. Subsequently, the artificial intelligent device 104 learns to respond to the auditory signals.

Further, the present invention also provides the system 100 to couple two or more parts/components of a spatio-temporal spiking neural network comprised in an artificial intelligent device, such as to provide acoustic signature identification or other cognitive tasks and the related communications between two or more components of a spatio-temporal spiking neural network. Each component comprises a plurality of neural processing cores and synaptic memory.

The system 100 provides connecting two or more parts or components of a distributed and spatio-temporal spiking neural network, comprised in the artificial intelligent devices, by some means of parallel or serial communication protocol 108, such as the Internet. In an embodiment, the communication protocol may be Intranet or a fast serial bus. A plurality of spike times, originating neuron and destinations, are transmitted as packets of information, and feedback from a remote spiking neural network is received as packets of information. The protocol for these packets of information can be any established standard, including but not limited to PCI (Peripheral Component Interconnect), PCIe (PCI express), USB (Universal Serial Bus), or TCP (Transmission Control Protocol).

As described above in FIG. 1, a distributed spiking neural network of the artificial intelligent device 104 is used in the recognition and identification of acoustic signals using acoustic signature recognition by means of a spatio-temporal neural network, in accordance with an embodiment of the present invention. Further, referring to FIG. 1, the first artificial intelligent device 104 connects to a remote artificial intelligent device 104A via a Serial Address Event Representation Bus 106, over the TCP/IP 108. The pulse stream patterns with spatio-temporal distribution generated at the first artificial intelligent device 104 defining the features of the events of the input pulses are transmitted to the remote artificial intelligent device 104A. The pulse stream patterns with spatio-temporal distribution consist of the pulse timing and spatial distribution that transmit key aspects of the acoustic signature in the input auditory signals. The remote artificial intelligent device 104A receives spatio-temporal pulse streams via a Serial Address Event Representation 106A. After receiving the pulse stream patterns, the remote artificial intelligent device 104A recognizes, identifies the pulse stream patterns and learns to respond in the same as the first artificial intelligent device 104, by means of Synaptic Time Dependent Plasticity.

The pulse streams with spatio-temporal distribution communicate over two linked Address Event Representation (AER) busses (106 and 106A). The properties of the AER bus are that each activation event of an artificial neuron is represented as the address of that neuron. The matrix of parallel processing artificial neurons is indexed, with each artificial neuron assigned an index number. This index number represents its address. In an embodiment, the internal bus 106 is 16 bits wide and allows for ‘near’ connections, while the external serial bus 106A contains a 40 bits address field, allowing a maximum of 240 connections. The communications protocol 108 is optimized to avoid bus contention. In a preferred embodiment, the serial AER communication bus 106 is connected to a remote Dynamic Spatio-Temporal Neural network of a remote artificial intelligent device 104A through a communication link, such as the Internet 108. The remote Dynamic Spatio-Temporal Neural Network of the remote artificial intelligent device 104A has the same structure as of the Dynamic Spatio-Temporal Neural Network of the first artificial intelligent device 104.

In an embodiment, the remote artificial intelligent device 104A connects to a computing device for the purpose of analysis or controlling an appliance. The remote artificial intelligent device 104A connects with a microprocessor 110 that further is connected with one or more appliances that need to be controlled remotely.

Therefore, the system 100 provides a communication system for connecting two or more components a distributed spatio-temporal neural network comprised in one or more artificial devices, where the input spikes is communicated in a communication protocol over the Internet 108, so that physical layers of the artificial neural networks can be separated by miles but still be part of the same network.

FIG. 2 illustrates a method of learning and identifying acoustic signature of auditory signals using a dynamic spatio-temporal neural network, in accordance with an embodiment of the present invention. According to the present invention, an artificial intelligent device 104 identifies and recognizes features in a plurality of spatio-temporal pulses received by it, implementing Synaptic Time Dependent Plasticity. The artificial intelligent device 104 comprises of a dynamic spatio-temporal neural network. The artificial intelligent device 104 receives a plurality of input pulses in form of stimuli. Stimuli may be in form of auditory signals, human speech, animal, or any object, or any other type of stimuli generated from different event such as, object recognition, hand or body movement, and others.

According to the method 200, an input sensor captures a plurality of acoustic signals from an auditory source such as the human voice, animal or insect activity or mechanical vibration, at step 202. An artificial cochlear 102 is further connected to the input sensor, where the artificial cochlear 102 consists of a series of resonators. The artificial cochlear 102 receives the acoustic signals from the input sensor, at step 204. The sound waves in the acoustic signals produces pressure waves in the artificial cochlear 102 that stimulates sensory neurons in the cochlear 102, at step 206. Thereafter, at step 208, the sensory neurons of the artificial cochlear 102 each produce a potential that varies at the same rate as the sound pressure waves of various frequencies. The varying potential produced at the artificial cochlear 102 connects to a series of resonators that approximates the function of hair cells in the biological cochlear, producing a series of spatio-temporal pulses that are equivalent to the action potentials in the biological cochlear nerve.

Further at step 210, the artificial cochlear 102 outputs a plurality of pulse patterns with a spatio-temporal distribution that is indicative of features in the acoustic signals. The artificial cochlear 102 connects to a first artificial intelligent device 104. The first artificial intelligent device 104 consists of a spatio-temporal neural network. The Spatio-Temporal Neural Network in the artificial intelligent device 104 is subsequently trained to respond to a limited set of acoustic signatures.

After receiving a series of spatio-temporal neural network, at step 212, the artificial intelligent device 104 of spatio-temporal neural network recognizes and identifies the acoustic signatures in the auditory signals by association in a dynamic spatio-temporal neural network. Each synapse circuit in the Spatio-Temporal Neural Network of the artificial intelligent device 104 performs a temporal integration function on a single pulse stream. The Dendrite is a mechanism that provides spatial integration functions in a biological neuron. In the Spatio-Temporal Neural network, the values resulting from temporal integration in each artificial synapse circuit, the result of pulse interval and pulse frequency are integrated in an artificial dendrite circuit. Multiple synapse circuits are connected to each dendrite circuit, each synapse generating a single value. These values are spatially integrated in the dendrite circuit. One or more dendrite circuits connect to a soma circuit. In biological neurons, the soma consists of the nucleus of the cell and performs a global integration function. The artificial soma circuit integrates the values from a plurality of dendric circuits. The soma circuit connects to an n artificial axon circuit generates an output pulse sequence that is proportional to the total of integrated dendric values.

Synapses receive feedback from the Post Synaptic Neuron. The state of synapse circuit is changed according to the timing of input and feedback pulses. This comprises a learning function, known as Synaptic Time Dependent Plasticity. In this way by means of Synaptic Time Dependent Plasticity, the spatio-temporal neural network of the artificial intelligent device 104 is trained by exposing it to acoustic pattern information through the input sensor. Once a particular acoustic signature has been learned at step 212, the learning function can be mitigated by applying a value that represents a Neuromodulator. The acoustic signature is internally represented as pulses, also known as spikes, with spatio-temporal distribution.

FIG. 3 illustrates a method of communicating two or more components of a dynamic spatio-temporal neural network, in accordance with an embodiment of the present invention. The present invention also provides a method for communication between two or more components a distributed spatio-temporal neural network comprised in one or more artificial devices. The communication occurs by a means of parallel or serial communication protocol 108, such as the Internet. A plurality of spike times, originating neuron and destinations, are transmitted as packets of information, and feedback from a remote spiking neural network is received as packets of information. The protocol for these packets of information can be any established standard, including but not limited to PCI (Peripheral Component Interconnect), PCIe (PCI express), USB (Universal Serial Bus), or TCP (Transmission Control Protocol).

As shown in FIG. 3, the method 300 describes a step 302 where a first artificial intelligent device 104 recognizes and identifies plurality of acoustic signals using acoustic signature recognition by means of a spatio-temporal neural network. The first artificial intelligent device 104 connects to a remote artificial intelligent device 104A via a Serial Address Event Representation Bus 106, over the TCP/IP 108. The pulse stream patterns with spatio-temporal distribution generated at the first artificial intelligent device 104 defining the features of the events of the input pulses are transmitted to the Serial Address Event Representation (abbreviated as AER) Bus 106, over the TCP/IP 108, at step 304. The pulse stream patterns with spatio-temporal distribution consist of the pulse timing and spatial distribution that transmit key aspects of the acoustic signature in the input auditory signals.

Further, the AER bus transmits the pulse streams with spatio-temporal neural network to the remote artificial intelligent device 104A, at step 306. The remote artificial intelligent device 104A has the same structure as of the Dynamic Spatio-Temporal Neural Network of the first artificial intelligent device 104. The pulse streams with spatio-temporal distribution communicate over two linked Address Event Representation (AER) busses (106 and 106A). The AER bus 106 and 106A represents each activation event of an artificial neuron as the address of that neuron. The matrix of parallel processing artificial neurons is indexed, with each artificial neuron assigned an index number. This index number represents its address. In an embodiment, the internal bus 106 is 16 bits wide and allows for ‘near’ connections, while the external serial bus 106A contains a 40 bits address field, allowing a maximum of 240 connections. The communications protocol 108 is optimized to avoid bus contention.

After receiving the pulse stream patterns, the remote artificial intelligent device 104A recognizes, identifies the pulse stream patterns and learns to respond in the same manner as the first artificial intelligent device 104, by means of Synaptic Time Dependent Plasticity, at step 308. In a further embodiment, the remote artificial intelligent device 104A is connected to a computing device for analysis and controlling of an appliance. Therefore, at step 310, the remote artificial intelligent device 104A connects with a microprocessor further is connected with one or more appliances that need to be controlled remotely.

Advantageously, the present invention provides a system 100 comprising artificial intelligent device 104 communicating with a remote artificial intelligent device 104A, where the remote artificial intelligent device 104A efficiently controls one or more appliances. Therefore, the system 100 controls the appliances from anywhere in the world using a user's device equipped with the first artificial intelligent device 104. Also, the system 100 provides secure communication, since each neural network in the artificial intelligent device is trained to a unique voice signature of its owner. Further, the transfer of pulse streams is in AER format, which contains voice signature of the user/owner in timing between pulses distribution.

Claims

1. An apparatus for identifying and learning an acoustic signature of a plurality of auditory signals comprising:

an input sensor configured to capture a varying potential produced in the plurality of auditory signals from an auditory source;

a series of resonators configured to convert the varying potential into a plurality of streams of electrical pulses with spatio-temporal distribution;

an artificial intelligent device identifies one or more features of the pulse streams of acoustic signals, representing the acoustic signature, by association in a dynamic spatio-temporal neural network; and

the artificial intelligent device learns to respond to the acoustic signature of the acoustic signals by modifying synaptic strengths in the dynamic spatio-temporal neural network.

2. The apparatus as claimed in claim 1, wherein the streams of electrical pulses with spatio-temporal distribution are indicative of frequency and amplitude over time of the plurality of pulse streams.

3. The apparatus as claimed in claim 1, wherein the learning of the artificial intelligent device is through the modification of synaptic strengths by Synaptic Time Dependent Plasticity and feedback from Post Synaptic Neuron.

4. The apparatus as claimed in claim 1, wherein the artificial intelligent device is further configured to learn to respond to at least one acoustic signature for a particular each one of a plurality of captured acoustic waveforms.

5. The apparatus as claimed in claim 1, wherein the learning function can be mitigated by applying a value that represents a Neuromodulator in the dynamic spatio-temporal neural network of the artificial intelligent device.

6. The apparatus as claimed in claim 1, wherein the series of resonators is comprised in an artificial cochlear.

7. The apparatus as claimed in claim 1, wherein the auditory source may be a human speech

8. A communication system comprising:

a first artificial intelligent device consisting of a dynamic spatio-temporal neural network configured to receive and learn from a plurality of pulse streams of acoustic signals with spatio-temporal distribution; and

atleast one remote artificial intelligent device consisting of a dynamic spatio-temporal neural network communicating with the first artificial intelligent device, through a communication channel, configured to receive and respond to the pulse streams.

9. The communication system as claimed in claim 8, wherein the first artificial intelligent device and the remote artificial intelligent device comprise spatio-temporal spiking neural network and dynamic synapse circuits configured to process the plurality of parallel streams of pulses.

10. The communication system as claimed in claim 8, wherein the first artificial intelligent device is configured to:

receive a plurality of pulse streams of acoustic signals with spatio-temporal distribution, representing an acoustic signature;

identify one or more features of the pulse streams, by association in a dynamic spatio-temporal neural network; and

learn to respond to the pulse streams of acoustic signals by modifying synaptic strengths in the dynamic spatio-temporal neural network.

11. The communication system as claimed in claim 8, wherein the first artificial intelligent device communicates with the remote artificial intelligent device through the communication protocol via a Series Address Event Representation bus.

12. The communication system as claimed in claim 8, wherein the first artificial intelligent device transmits plurality of spike times or pulse streams, originating neuron and destinations as packets of information, and receives feedback from the remote artificial intelligent device as packets of information.

13. The communication system as claimed in claim 8, wherein the communication protocol for transmission of the packets of information can be any established standard, includes PCI (Peripheral Component Interconnect), PCIe (PCI express), USB (Universal Serial Bus), or TCP (Transmission Control Protocol).

14. The communication system as claimed in claim 8, wherein the first artificial intelligent device receives plurality of pulse streams of acoustic signals with spatio-temporal distribution from an artificial cochlear, which further receives a varying potential in pulse streams from an input sensor configured to capture the varying potential in the pulse stream of the acoustic signals.

15. The communication system as claimed in claim 8, wherein the remote artificial intelligent device is configured to communicate with a computing device that is intended to control one or more appliance.

16. A method for identifying and learning acoustic signature of a plurality of auditory signals comprising:

capturing, by an input sensor, a varying potential produced in the plurality of auditory signals from an auditory source;

converting the varying potential into a plurality of streams of electrical pulses with spatio-temporal distribution, by a series of resonators;

identifying, by an artificial intelligent device, one or more features of the pulse streams of acoustic signals, representing the acoustic signature, through association in a dynamic spatio-temporal neural network; and

learning, by the artificial intelligent device, to respond to the acoustic signature of the acoustic signals by modifying synaptic strengths in the dynamic spatio-temporal neural network.

17. The method as claimed in claim 16 further comprising transmitting said one or more features of the pulse streams of acoustic signals representing the acoustic signature from the artificial intelligent device to a remote artificial intelligent device and receiving feedback over a communication channel.

18. The method as claimed in claim 17, the communication channel includes PCI (Peripheral Component Interconnect), PCIe (PCI express), USB (Universal Serial Bus), or TCP (Transmission Control Protocol).

19. The method as claimed in claim 17 further comprising communicating of the remote artificial intelligent device with a computing device for analyzing and controlling one or more appliances.

20. The method as claimed in claim 16, wherein the first artificial intelligent device and the remote artificial intelligent device comprises a spatio-temporal neural network and dynamic synapse circuits configured to process parallel streams of pulses.

21. The method as claimed in claim 16, wherein the streams of electrical pulses with spatio-temporal distribution are indicative of frequency and amplitude over time of the plurality of pulse streams.

22. The method as claimed in claim 16, wherein the learning of the artificial intelligent device is through the modification of synaptic strengths by Synaptic Time Dependent Plasticity and feedback from Post Synaptic Neuron.

23. The method as claimed in claim 16, wherein the remote artificial intelligent device identifies the pulse streams and learns to respond in the same manner as the artificial intelligent device.