BRN Patent Citations by IBM and Qualcomm

http://www.ibm.com/blogs/think/2016/01/13/ibms-cognitive-computing-innovation-surges/

IBM’s Cognitive Computing Innovation Surges

IBM’s work in cognitive computing emerged as a major factor among the patents awarded to the company by the US. Patent and Trademark Office. More than 800 of IBM’s 7,355 2015 patents are related to cognitive computing, a 52% increase from the previous year.
Last year, IBM announced that it has realigned its entire business strategy around the promise of cognitive computers, which can learn, reason, and efficiently process diverse data types all while interacting with people in natural and familiar ways.
IBM’s leaders believe cognitive computing will have a profound impact on business and society, helping people to make better decisions in their personal and professional lives, democratizing expertise, and transforming industries and professions.
Simply put, IBM’s innovation agenda calls for developing cognitive solutions delivered via a cloud platform to transform industries.
Here are some highlights among last year’s cognitive computing innovations:
Creating machines that mimic the brain: Conventional computers are good at math, but they can’t tell the difference between a tree and a truck—at least, not without using a truckload of computing power. A invented a new kind of processor chip that draws inspiration from the structure and function of the human brain while exploiting creative possibilities in modern technology — to learn and adapt on the fly, and is 10,000 times more energy efficient than conventional chips. (Patent US8977583) ((cites our patent. see below for all citations))

http://www.google.com.au/patents/US20100076916#legal-events

BrainChip:
Autonomous Learning Dynamic Artificial Neural Computing Device and Brain Inspired System
US 20100076916 A1
ABSTRACT
A hierarchical information processing system is disclosed having a plurality of artificial neurons, comprised of binary logic gates, and interconnected through a second plurality of dynamic artificial synapses, intended to simulate or extend the function of a biological nervous system. The system is capable of approximation, autonomous learning and strengthening of formerly learned input patterns. The system learns by simulated Synaptic Time Dependent Plasticity, commonly abbreviated to STDP. Each artificial neuron consisting of a soma circuit and a plurality of synapse circuits, whereby the soma membrane potential, the soma threshold value, the synapse strength and the Post Synaptic Potential at each synapse are expressed as values in binary registers, which are dynamically determined from certain aspects of input pulse timing, previous strength value and output pulse feedback...

	Citing Patent	Filing date	Publication date	Applicant	Title
1	US8712940	31 May 2011	29 Apr 2014	International Business Machines Corporation	Structural plasticity in spiking neural networks with symmetric dual of an electronic neuron
2	US8843425	29 Jul 2011	23 Sep 2014	International Business Machines Corporation	Hierarchical routing for two-way information flow and structural plasticity in neural networks
3	US8868477	29 Mar 2012	21 Oct 2014	International Business Machines Coproration	Multi-compartment neurons with neural cores
4	US8892485	8 Jul 2010	18 Nov 2014	Qualcomm Incorporated	Methods and systems for neural processor training by encouragement of correct output
5	US8909575	29 Feb 2012	9 Dec 2014	Qualcomm Incorporated	Method and apparatus for modeling neural resource based synaptic placticity
6	US8914315 *	27 Jan 2012	16 Dec 2014	International Business Machines Corporation	Multi-compartment neuron suitable for implementation in a distributed hardware model by reducing communication bandwidth
7	US8959040 *	8 Mar 2012	17 Feb 2015	Hrl Laboratories, Llc	Spike timing dependent plasticity apparatus, system and method
8	US8977583	29 Mar 2012	10 Mar 2015	International Business Machines Corporation	Synaptic, dendritic, somatic, and axonal plasticity in a network of neural cores using a plastic multi-stage crossbar switching
9	US8996430	27 Jan 2012	31 Mar 2015	International Business Machines Corporation	Hierarchical scalable neuromorphic synaptronic system for synaptic and structural plasticity
10	US9053428	21 Jul 2011	9 Jun 2015	Qualcomm Incorporated	Method and apparatus of robust neural temporal coding, learning and cell recruitments for memory using oscillation
11	US9064215	14 Jun 2012	23 Jun 2015	Qualcomm Incorporated	Learning spike timing precision
12	US9087302	7 Jun 2012	21 Jul 2015	Samsung Electronics Co., Ltd.	Synapse for function cell of spike timing dependent plasticity (STDP), function cell of STDP, and neuromorphic circuit using function cell of STDP
13	US9092735	21 Sep 2011	28 Jul 2015	Qualcomm Incorporated	Method and apparatus for structural delay plasticity in spiking neural networks
14	US9111224 *	19 Oct 2011	18 Aug 2015	Qualcomm Incorporated	Method and apparatus for neural learning of natural multi-spike trains in spiking neural networks
15	US9111225	8 Feb 2012	18 Aug 2015	Qualcomm Incorporated	Methods and apparatus for spiking neural computation
16	US9129220	7 Jul 2010	8 Sep 2015	Qualcomm Incorporated	Methods and systems for digital neural processing with discrete-level synapes and probabilistic STDP
17	US9147155	16 Aug 2011	29 Sep 2015	Qualcomm Incorporated	Method and apparatus for neural temporal coding, learning and recognition
18	US9183495	8 Aug 2012	10 Nov 2015	International Business Machines Corporation	Structural plasticity in spiking neural networks with symmetric dual of an electronic neuron
19	US9189731	24 Mar 2014	17 Nov 2015	International Business Machines Corporation	Structural plasticity in spiking neural networks with symmetric dual of an electronic neuron
20	US20110004579 *	29 Sep 2008	6 Jan 2011	Greg Snider	Neuromorphic Circuit
21	US20130103626 *	19 Oct 2011	25 Apr 2013	Qualcomm Incorporated	Method and apparatus for neural learning of natural multi-spike trains in spiking neural networks
22	US20130198121 *	27 Jan 2012	1 Aug 2013	International Business Machines Corporation	Multi-compartment neuron suitable for implementation in a distributed hardware model by reducing communication bandwidth
23	WO2012006592A1 *	8 Jul 2011	12 Jan 2012	Qualcomm Incorporated	Methods and systems for neural processor training by encouragement of correct output
24	WO2013059703A1 *	19 Oct 2012	25 Apr 2013	Qualcomm Incorporated	Method and apparatus for neural learning of natural multi-spike trains in spiking neural networks
25	WO2013119872A1 *	7 Feb 2013	15 Aug 2013	Qualcomm Incorporated	Methods and apparatus for spiking neural computation

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