Visualizing and Analyzing Multidimensional Output from MLP Networks via Barycentric Projections. - Filip Piekniewski; Leszek Rybicki (ICAISC, 2004)

Visual comparison of performance for different activation functions in MLP networks. - Filip Piekniewski; Leszek Rybicki (IJCNN, 2004)

Phase diagrams for locally Hopfield neural networks in presence of correlated patterns. - Filip Piekniewski; Tomasz Schreiber (IJCNN, 2005)

Mesoscopic Approach to Locally Hopfield Neural Networks in Presence of Correlated Patterns. - Filip Piekniewski (IJCNN, 2006)

Emergence of Scale-free Spike Flow Graphs in Recurrent Neural Networks. - Filip Piekniewski; Tomasz Schreiber (FOCI, 2007)

Spontaneous scale-free structure of spike flow graphs in recurrent neural networks. - Filip Piekniewski; Tomasz Schreiber (Neural Networks, 2008)

Spectra of the Spike Flow Graphs of Recurrent Neural Networks. - Filip Piekniewski (ICANN (2), 2009)

Robustness of power laws in degree distributions for spiking neural networks. - Filip Piekniewski (IJCNN, 2009)

Persistent Activation Blobs in Spiking Neural Networks with Mexican Hat Connectivity. - Filip Piekniewski (ICAISC (2), 2010)

Theoretical Model for Mesoscopic-Level Scale-Free Self-Organization of Functional Brain Networks. - Jaroslaw Piersa; Filip Piekniewski; Tomasz Schreiber (IEEE Trans. Neural Networks, 2010)

Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent Network. - Filip Piekniewski; Patryk A. Laurent; Csaba Petre; Micah Richert; Dimitry Fisher; Todd Hylton (CoRR, 2016)

Fundamental principles of cortical computation: unsupervised learning with prediction, compression and feedback. - Micah Richert; Dimitry Fisher; Filip Piekniewski; Eugene M. Izhikevich; Todd Hylton (CoRR, 2016)