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home page filip piękniewski, phd san diego, ca e-mail: polish version i’m a researcher working on computer vision and ai. my main research objective is to introduce dynamics into machine learning which is currently dominated by statistics. having dynamics is necessary for ml models to produce true understanding of physical reality. my recent attempt to build a machine learning system that naturally incorporates time, online processing and unsupervised learning of the physics is described here (github code here ). until dynamics is deeply incorporated in machine learning models, they will remain “statistical machines” that “statistically work” which is not enough for a real world application such as robotics. my long term ambition is to address moravec’s paradox . you may read more in my blog on machine learning and other things . research my research interests recent research activities publications patents computer science in general computational intelligence neural networks (deep learning in current lingo) random graphs and small world networks hopfield networks, phase diagrams critical phenomena spiking neural networks texture analysis dynamic systems and fractals information theory computer modeling/gpu programming unix/linux/macosx administration creating a large scale predictive vision model incorporating feedback analysis of network topologies emerging from spiking neural models and other simplified computational models study of critical systems 2016 – unsupervised learning from continuous video in a scalable predictive recurrent network 2016 – fundamental principles of cortical computation: unsupervised learning with prediction, compression and feedback 2010 – persistent activation blobs in spiking neural networks with mexican hat connectivity – accepted to icaisc 2010 . supplemental matlab script and movie 1 , movie 2 . 2010 – spectra of the spike flow graphs of recurrent neural networks – presentation for the icann conference. 2010 – theoretical model for mesoscopic-level scale-free self-organization of functional brain networks , preprint submitted to ieee tnn ( bibtex ) 2009 – spectra of the spike flow graphs of recurrent neural networks preprint submitted to icann ( bibtex ) 2009 – robustness of power laws in degree distributions for spiking neural networks preprint submitted to ijcnn 2009. bibtex 2008 – phd thesis – defended on april 1st 2009 at warsaw university. 2008 – spontaneous scale-free structure of spike flow graphs in recurrent neural networks , preprint submitted to neural networks ( bibtex ) 2007 – emergence of scale-free graphs in dynamical spiking neural networks , a poster presented at 2007 international joint conference on neural networks, orlando 2007 ( bibtex ) 2007 – emergence of scale-free graphs in dynamical spiking neural networks , 2007 international joint conference on neural networks, orlando 2007 ( bibtex ) 2007 – emergence of scale-free spike flow graphs in recurrent neural networks , 2007 ieee symposium on foundations of computational intelligence, honolulu hawai 1-5 april 2007 ( bibtex ) 2006 – mesoscopic approach to locally hopfield neural networks in presence of correlated patterns , 2006 international joint conference on neural networks, vancouver, bc, canada july 16-21, 2006, pp 5899 ( bibtex ) 2005 – phase diagrams for locally hopfield neural networks in presence of correlated patterns ,proceedings of international joint conference on neural networks, montreal, canada, july 31 – august 4, 2005, pp 776 ( bibtex ) 2004 – visualizing and analyzing multidimensional output from mlp networks via barycentric projections , springer-verlag, lecture notes on artificial inteligence vol 3070 ( bibtex ) 2004 – visual comparison of performance for different activation functions in mlp networks , proceedings of internartional joint conference on neural networks, budapest, hungary 2004, pp 2947-2953. ( bibtex ) check my google scholar page for citations sensory input processing apparatus in a spiking neural network piekniewski, f., izhikevich, e., szatmary, b., & petre, c. (2012). u.s. patent application 13/465,903 . spiking neural network object recognition apparatus and methods piekniewski, f., izhikevich, e., szatmary, b., & petre, c. (2012). u.s. patent application 13/465,918 . spiking neural network feedback apparatus and methods piekniewski, f., izhikevich, e., szatmary, b., & petre, c. (2012). u.s. patent application 13/465,924 . elementary network description for efficient implementation of event-triggered plasticity rules in neuromorphic systems izhikevich, e. m., szatmary, b., petre, c., piekniewski, f., & nageswaran, j. m. (2013). u.s. patent no. 20,130,073,492 . washington, dc: u.s. patent and trademark office. elementary network description for efficient memory management in neuromorphic systems izhikevich, e. m., szatmary, b., petre, c., & piekniewski, f. (2013). u.s. patent no. 20,130,073,484 . washington, dc: u.s. patent and trademark office. elementary network description for neuromorphic systems izhikevich, e. m., szatmary, b., petre, c., nageswaran, j. m., & piekniewski, f. (2013). u.s. patent no. 20,130,073,495 . washington, dc: u.s. patent and trademark office. elementary network description for efficient link between neuronal models and neuromorphic systems izhikevich, e. m., petre, c., piekniewski, f., & szatmary, b. (2013). u.s. patent no. 20,130,073,498 . washington, dc: u.s. patent and trademark office. apparatus and method for partial evaluation of synaptic updates based on system events izhikevich, e. m., piekniewski, f., & nageswaran, j. m. (2013). u.s. patent no. 20,130,073,499 . washington, dc: u.s. patent and trademark office. apparatus and methods for synaptic update in a pulse-coded network izhikevich, e. m., piekniewski, f., & nageswaran, j. m. (2013). u.s. patent no. 20,130,073,491 . washington, dc: u.s. patent and trademark office. apparatus and methods for temporally proximate object recognition piekniewski, f. l., petre, c., sokol, s. h., szatmary, b., nageswaran, j. m., & izhikevich, e. m. (2012). u.s. patent no. 20,120,308,076 . washington, dc: u.s. patent and trademark office. apparatus and methods for temporally proximate object recognition piekniewski, f., petre, c., sokol, s., szatmary, b., nageswaran, j., & izhikevich, e. (2012). wipo patent no. 2012167164 . geneva, switzerland: world intellectual property organization. apparatus and methods for synaptic update in a pulse-coded network izhikevich, e., piekniewski, f., & nageswaran, j. (2013). wipo patent no. 2013043903 . geneva, switzerland: world intellectual property organization. elementary network description for neuromorphic systems izhikevich, e. m., szatmary, b., petre, c., nageswaran, j. m., & piekniewski, f. (2013). wipo patent no. 2013043610 . geneva, switzerland: world intellectual property organization. more submitted, check my google scholar page teaching 2013 2009/2010 spiking neural networks as a parallel computing paradigm june 2013, six lectures at faculty of mathematics and computer science, nicolaus copernicus university, toruń lecture notes mathematical foundations of neuroscience – winter semester 2009/2010. preliminary program. any supplemental material will be published here. lecture presentations: lecture 1 – basic facts about the brain and its analysis, sample questions lecture 2 – electrophysiology of neurons, sample questions lecture 3 – electrophysiology of neurons (continued), sample questions lecture 4 – 1d systems, sample questions lecture 5 – 2d systems, sample questions lecture 6 – bifurcations in 2d systems, sample questions lecture 7 – bifurcations ii, sample questions lecture 8 – classes of excitability, sample questions lecture 9 – simple models of neurons and synapses, sample questions lecture 10 – bursting, sample questions lecture 11 – bursting continued sample questions lecture 12 – synchronization sample questions lecture 13 – co

URL analysis for piekniewski.info

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http://filip.piekniewski.info/stuff/icaisc2009/80_8xslower.mov
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intranet.cs.man.ac.uk

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