On the Allocation of Documents in Multiprocessor Information Retrieval Systems. - Ophir Frieder; Hava T. Siegelmann (SIGIR, 1991)

Integrating Implicit Answers with Object-Oriented Queries. - Hava T. Siegelmann; B. R. Badrinath (VLDB, 1991)

The Complexity of Language Recognition by Neural Networks. - Hava T. Siegelmann; Eduardo D. Sontag; C. Lee Giles (IFIP Congress (1), 1992)

Some Structural Complexity Aspects of Neural Computation. - José L. Balcázar; Ricard Gavaldà; Hava T. Siegelmann; Eduardo D. Sontag (SCT, 1993)

On the Power of Sigmoid Neural Networks. - Joe Kilian; Hava T. Siegelmann (COLT, 1993)

Document Allocation In Multiprocessor Information Retrieval Systems. - Hava T. Siegelmann; Ophir Frieder (Advanced Database Systems, 1993)

Neural Programming Language. - Hava T. Siegelmann (AAAI, 1994)

On a Learnability Question Associated to Neural Networks with Continuous Activations (Extended Abstract). - Bhaskar DasGupta; Hava T. Siegelmann; Eduardo D. Sontag (COLT, 1994)

On The Computational Power of Probabilistic and Faulty Neural Networks. - Hava T. Siegelmann (ICALP, 1994)

An integrated symbolic and neural network architecture for machine learning in the domain of nuclear engineering. - Ephraim Nissan; Hava T. Siegelmann; Alex Galperin (ICPR (2), 1994)

Towards Full Automation of the Discovery of Heuristics in a Nuclear Engineering Project: Integration With a Neural Information Language. - Ephraim Nissan; Hava T. Siegelmann; Alex Galperin; Shuky Kimhi (ISMIS, 1994)

Analog Computation via Neural Networks. - Hava T. Siegelmann; Eduardo D. Sontag (Theor. Comput. Sci., 1994)

Recurrent Neural Networks. - Hava T. Siegelmann (Computer Science Today, 1995)

Input variable selection for neural networks: application to predicting the U.S. business cycle. - Joachim Utans; John E. Moody; Steven Rehfuss; Hava T. Siegelmann (CIFEr, 1995)

What NARX Networks Can Compute. - Bill G. Horne; Hava T. Siegelmann; C. Lee Giles (SOFSEM, 1995)

Welcoming the Super Turing Theories. - Hava T. Siegelmann (SOFSEM, 1995)

On the Computational Power of Neural Nets. - Hava T. Siegelmann; Eduardo D. Sontag (J. Comput. Syst. Sci., 1995)

On the complexity of training neural networks with continuous activation functions. - Bhaskar DasGupta; Hava T. Siegelmann; Eduardo D. Sontag (IEEE Trans. Neural Networks, 1995)

Recurrent Neural Networks and Finite Automata. - Hava T. Siegelmann (Comput. Intell., 1996)

The Dynamic Universality of Sigmoidal Neural Networks. - Joe Kilian; Hava T. Siegelmann (Inf. Comput., 1996)

On NIL: the Software Constructor of Neural Networks. - Hava T. Siegelmann (Parallel Process. Lett., 1996)

The Simple Dynamics of Super Turing Theories. - Hava T. Siegelmann (Theor. Comput. Sci., 1996)

Turing Universality of Neural Nets (Revisited). - João Pedro Guerreiro Neto; Hava T. Siegelmann; José Félix Costa; Carmen Paz Suárez Araujo (EUROCAST, 1997)

A Generic Approach for Identification of Event Related Brain Potentials via a Competitive Neural Network Structure. - Daniel H. Lange; Hava T. Siegelmann; Hillel Pratt; Gideon F. Inbar (NIPS, 1997)

Neural Dynamics with Stochasticity. - Hava T. Siegelmann (Summer School on Neural Networks, 1997)

The complexity of language recognition by neural networks. - Hava T. Siegelmann; C. Lee Giles (Neurocomputing, 1997)

Computational power of neural networks: a characterization in terms of Kolmogorov complexity. - José L. Balcázar; Ricard Gavaldà; Hava T. Siegelmann (IEEE Trans. Inf. Theory, 1997)

Multiprocessor Document Allocation: A Genetic Algorithm Approach. - Ophir Frieder; Hava T. Siegelmann (IEEE Trans. Knowl. Data Eng., 1997)

Computational capabilities of recurrent NARX neural networks. - Hava T. Siegelmann; Bill G. Horne; C. Lee Giles (IEEE Trans. Syst. Man Cybern. Part B, 1997)

High Order Eigentensors as Symbolic Rules in Competitive Learning. - Hod Lipson; Hava T. Siegelmann (Hybrid Neural Systems, 1998)

Attractor systems and analog computation. - Hava T. Siegelmann; Shmuel Fishman (KES (1), 1998)

A Theory of Complexity for Continuous Time Dynamics. - Hava T. Siegelmann; Asa Ben-Hur; Shmuel Fishman (MCU (1), 1998)

Neural networks and analog computation - beyond the Turing limit. - Hava T. Siegelmann (Birkhäuser, 1999)

Noisy Neural Networks and Generalizations. - Hava T. Siegelmann; Alexander Roitershtein; Asa Ben-Hur (NIPS, 1999)

Stochastic Analog Networks and Computational Complexity. - Hava T. Siegelmann (J. Complex., 1999)

Discontinuities in Recurrent Neural Networks. - Ricard Gavaldà; Hava T. Siegelmann (Neural Comput., 1999)

Nine switch-affine neurons suffice for Turing universality. - Hava T. Siegelmann; Maurice Margenstern (Neural Networks, 1999)

Finite Versus Infinite Neural Computation. - Hava T. Siegelmann (Finite Versus Infinite, 2000)

A Support Vector Clustering Method. - Asa Ben-Hur; Hava T. Siegelmann; David Horn 001; Vladimir Vapnik (ICPR, 2000)

A Support Vector Method for Clustering. - Asa Ben-Hur; David Horn 001; Hava T. Siegelmann; Vladimir Vapnik (NIPS, 2000)

Macroscopical Molecular Computation with Gene Networks. - Hava T. Siegelmann; Asa Ben-Hur (UMC, 2000)

Clustering Irregular Shapes Using High-Order Neurons. - Hod Lipson; Hava T. Siegelmann (Neural Comput., 2000)

Sensor registration using neural networks. - Haim Karniely; Hava T. Siegelmann (IEEE Trans. Aerosp. Electron. Syst., 2000)

Overcoming selective ensemble averaging: unsupervised identification of event-related brain potentials. - Daniel H. Lange; Hava T. Siegelmann; Hillel Pratt; Gideon F. Inbar (IEEE Trans. Biomed. Eng., 2000)

Verifying Properties of Neural Networks. - Pedro Rodrigues; José Félix Costa; Hava T. Siegelmann (IWANN (1), 2001)

Computation in Gene Networks. - Asa Ben-Hur; Hava T. Siegelmann (MCU, 2001)

Active Information Retrieval. - Tommi S. Jaakkola; Hava T. Siegelmann (NIPS, 2001)

Neural Computing. - Hava T. Siegelmann (Bull. EATCS, 2001)

Support Vector Clustering. - Asa Ben-Hur; David Horn 001; Hava T. Siegelmann; Vladimir Vapnik (J. Mach. Learn. Res., 2001)

A Theory of Complexity for Continuous Time Systems. - Asa Ben-Hur; Hava T. Siegelmann; Shmuel Fishman (J. Complex., 2002)

Symbolic Processing in Neural Networks. - João Pedro Guerreiro Neto; Hava T. Siegelmann; José Félix Costa (J. Braz. Comput. Soc., 2003)

Probabilistic analysis of a differential equation for linear programming. - Asa Ben-Hur; Joshua Feinberg; Shmuel Fishman; Hava T. Siegelmann (J. Complex., 2003)

Neural and Super-Turing Computing. - Hava T. Siegelmann (Minds Mach., 2003)

Time-Warped Longest Common Subsequence Algorithm for Music Retrieval. - AnYuan Guo; Hava T. Siegelmann (ISMIR, 2004)

On probabilistic analog automata. - Asa Ben-Hur; Alexander Roitershtein; Hava T. Siegelmann (Theor. Comput. Sci., 2004)

Introducing an active cluster-based information retrieval paradigm. - Oscar Loureiro; Hava T. Siegelmann (J. Assoc. Inf. Sci. Technol., 2005)

Adaptive Multi-modal Sensors. - Kyle Ira Harrington; Hava T. Siegelmann (50 Years of Artificial Intelligence, 2006)

Circadian synchrony in networks of protein rhythm driven neurons. - William S. Bush; Hava T. Siegelmann (Complex., 2006)

Multi-Agent System that Attains Longevity via Death. - Megan M. Olsen; Hava T. Siegelmann (IJCAI, 2007)

The Self-Construction and -Repair of a Foraging Organism by Explicitly Specified Development from a Single Cell. - Fabian Roth; Hava T. Siegelmann; Rodney J. Douglas (Artif. Life, 2007)

Neural Networks. - Hava T. Siegelmann; Bhaskar DasGupta; Derong Liu 001 (Handbook of Approximation Algorithms and Metaheuristics, 2007)

Emotional Robotics: Tug of War. - David G. Cooper; Dov Katz; Hava T. Siegelmann (AAAI Spring Symposium: Emotion, Personality, and Social Behavior, 2008)

Emotions for Strategic Real-Time Systems. - Megan M. Olsen; Kyle Ira Harrington; Hava T. Siegelmann (AAAI Spring Symposium: Emotion, Personality, and Social Behavior, 2008)

Robust artificial life via artificial programmed death. - Megan M. Olsen; N. Siegelmann-Danieli; Hava T. Siegelmann (Artif. Intell., 2008)

Drug Addiction: A Computational Multiscale Model Combining Neuropsychology, Cognition and Behavior. - Yariv Z. Levy; Dino Levy; Jerrold S. Meyer; Hava T. Siegelmann (BIOSIGNALS, 2009)

Text-based Reasoning with Symbolic Memory Model. - Kun Tu; Hava T. Siegelmann (NeSy, 2009)

Identification and control of intrinsic bias in a multiscale computational model of drug addiction. - Yariv Z. Levy; Dino Levy; Jerrold S. Meyer; Hava T. Siegelmann (SAC, 2010)

Complex Systems Science and Brain Dynamics. - Hava T. Siegelmann (Frontiers Comput. Neurosci., 2010)

Conspecific Emotional Cooperation Biases Population Dynamics: A Cellular Automata Approach. - Megan M. Olsen; Kyle Ira Harrington; Hava T. Siegelmann (Int. J. Nat. Comput. Res., 2010)

Activity Inference through Commonsense. - Kun Tu; Megan M. Olsen; Hava T. Siegelmann (AAAI Spring Symposium: Logical Formalizations of Commonsense Reasoning, 2011)

Evolving recurrent neural networks are super-Turing. - Jérémie Cabessa; Hava T. Siegelmann (IJCNN, 2011)

Communicated somatic markers benefit both the individual and the species. - Kyle Ira Harrington; Megan M. Olsen; Hava T. Siegelmann (IJCNN, 2011)

The Computational Power of Interactive Recurrent Neural Networks. - Jérémie Cabessa; Hava T. Siegelmann (Neural Comput., 2012)

A year of neural network research: Special Issue on the 2011 International Joint Conference on Neural Networks. - Jean-Philippe Thivierge; Ali A. Minai; Hava T. Siegelmann; Cesare Alippi; Michael Georgiopoulos (Neural Networks, 2012)

Guest Editorial: Biologically Inspired Human-Robot Interactions - Developing More Natural Ways to Communicate with our Machines. - Frederick C. Harris Jr.; Jeffrey L. Krichmar; Hava T. Siegelmann; Hiroaki Wagatsuma (IEEE Trans. Auton. Ment. Dev., 2012)

Multiscale Agent-based Model of Tumor Angiogenesis. - Megan M. Olsen; Hava T. Siegelmann (ICCS, 2013)

Probability-Generated Aggregators. - Evgeny Kagan 001; Alexander N. Rybalov; Hava T. Siegelmann; Ronald R. Yager (Int. J. Intell. Syst., 2013)

Development of Physical Super-Turing Analog Hardware. - Arthur Steven Younger; Emmett Redd; Hava T. Siegelmann (UCNC, 2014)

Report on UCNC 2014. - Hava T. Siegelmann; Rudolf Freund (Bull. EATCS, 2014)

The Super-Turing Computational Power of plastic Recurrent Neural Networks. - Jérémie Cabessa; Hava T. Siegelmann (Int. J. Neural Syst., 2014)

Implementation of universal computation via small recurrent finite precision neural networks. - J. Nicholas Hobbs; Hava T. Siegelmann (IJCNN, 2015)

EyeFrame: Real-Time Memory Aid Improves Human Multitasking via Domain-General Eye Tracking Procedures. - P. Taylor; Noah Bilgrien; Ze He; Hava T. Siegelmann (Frontiers ICT, 2015)

Human Strategies for Multitasking, Search, and Control Improved via Real-Time Memory Aid for Gaze Location. - P. Taylor; Ze He; Noah Bilgrien; Hava T. Siegelmann (Frontiers ICT, 2015)

Preface. - Hava T. Siegelmann (Theor. Comput. Sci., 2016)

Resting state neural networks and energy metabolism. - Raymond Noack; Chetan Manjesh; Miklós Ruszinkó; Hava T. Siegelmann; Robert Kozma 001 (IJCNN, 2017)

Insulin Regimen ML-based control for T2DM patients. - Mark Shifrin; Hava T. Siegelmann (CoRR, 2017)

Neural Networks. - Bhaskar DasGupta; Derong Liu 001; Hava T. Siegelmann (Handbook of Approximation Algorithms and Metaheuristics (1), 2018)

Unsupervised Learning with Self-Organizing Spiking Neural Networks. - Hananel Hazan; Daniel J. Saunders; Darpan T. Sanghavi; Hava T. Siegelmann; Robert Kozma 001 (IJCNN, 2018)

STDP Learning of Image Patches with Convolutional Spiking Neural Networks. - Daniel J. Saunders; Hava T. Siegelmann; Robert Kozma 001; Miklós Ruszinkó (IJCNN, 2018)

Evolution of Abstraction Across Layers in Deep Learning Neural Networks. - Robert Kozma 001; Roman Ilin; Hava T. Siegelmann (INNS Conference on Big Data, 2018)

BindsNET: A machine learning-oriented spiking neural networks library in Python. - Hananel Hazan; Daniel J. Saunders; Hassaan Khan; Darpan T. Sanghavi; Hava T. Siegelmann; Robert Kozma 001 (CoRR, 2018)

Error Forward-Propagation: Reusing Feedforward Connections to Propagate Errors in Deep Learning. - Adam A. Kohan; Edward A. Rietman; Hava T. Siegelmann (CoRR, 2018)

BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python. - Hananel Hazan; Daniel J. Saunders; Hassaan Khan; Devdhar Patel; Darpan T. Sanghavi; Hava T. Siegelmann; Robert Kozma 001 (Frontiers Neuroinformatics, 2018)

Toward Modeling Regeneration via Adaptable Echo State Networks. - Jennifer Hammelman; Hava T. Siegelmann; Santosh Manicka; Michael Levin 001 (From Parallel to Emergent Computing, 2019)

Models of Situated Intelligence Inspired by the Energy Management of Brains. - Robert Kozma 001; Raymond Noack; Hava T. Siegelmann (SMC, 2019)

Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games. - Devdhar Patel; Hananel Hazan; Daniel J. Saunders; Hava T. Siegelmann; Robert Kozma 001 (CoRR, 2019)

Deep Neural Networks Abstract Like Humans. - Alex Gain; Hava T. Siegelmann (CoRR, 2019)

Minibatch Processing in Spiking Neural Networks. - Daniel J. Saunders; Cooper Sigrist; Kenneth Chaney; Robert Kozma 001; Hava T. Siegelmann (CoRR, 2019)

Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to Atari Breakout game. - Devdhar Patel; Hananel Hazan; Daniel J. Saunders; Hava T. Siegelmann; Robert Kozma 001 (Neural Networks, 2019)

Locally connected spiking neural networks for unsupervised feature learning. - Daniel J. Saunders; Devdhar Patel; Hananel Hazan; Hava T. Siegelmann; Robert Kozma 001 (Neural Networks, 2019)

Abstraction Mechanisms Predict Generalization in Deep Neural Networks. - Alex Gain; Hava T. Siegelmann (ICML, 2020)

Minibatch Processing for Speed-up and Scalability of Spiking Neural Network Simulation. - Daniel J. Saunders; Cooper Sigrist; Kenneth Chaney; Robert Kozma 001; Hava T. Siegelmann (IJCNN, 2020)

Adaptive Neural Connections for Sparsity Learning. - Alex Gain; Prakhar Kaushik; Hava T. Siegelmann (WACV, 2020)

Lattice map spiking neural networks (LM-SNNs) for clustering and classifying image data. - Hananel Hazan; Daniel J. Saunders; Darpan T. Sanghavi; Hava T. Siegelmann; Robert Kozma 001 (Ann. Math. Artif. Intell., 2020)

Near-optimal insulin treatment for diabetes patients: A machine learning approach. - Mark Shifrin; Hava T. Siegelmann (Artif. Intell. Medicine, 2020)

Nanotechnology-inspired Information Processing Systems of the Future. - Randy Bryant; Mark D. Hill; Tom Kazior; Daniel Lee; Jie Liu; Klara Nahrstedt; Vijay Narayanan; Jan M. Rabaey; Hava T. Siegelmann; Naresh R. Shanbhag; Naveen Verma; H.-S. Philip Wong (CoRR, 2020)

Turing Completeness of Bounded-Precision Recurrent Neural Networks. - Stephen Chung; Hava T. Siegelmann (NeurIPS, 2021)

Replay in Deep Learning: Current Approaches and Missing Biological Elements. - Tyler L. Hayes; Giri P. Krishnan; Maxim Bazhenov; Hava T. Siegelmann; Terrence J. Sejnowski; Christopher Kanan (Neural Comput., 2021)

Automatic Transpiler that Efficiently Converts Digital Circuits to a Neural Network Representation. - Devdhar Patel; Ignacio Gavier; Joshua Russell; Andrew Malinsky; Edward A. Rietman; Hava T. Siegelmann (IJCNN, 2022)

Memory via Temporal Delays in weightless Spiking Neural Network. - Hananel Hazan; Simon Caby; Christopher Earl; Hava T. Siegelmann; Michael Levin 001 (CoRR, 2022)

Forward Signal Propagation Learning. - Adam A. Kohan; Edward A. Rietman; Hava T. Siegelmann (CoRR, 2022)

Energy-based General Sequential Episodic Memory Networks at the Adiabatic Limit. - Arjun Karuvally; Terry J. Sejnowski; Hava T. Siegelmann (CoRR, 2022)

QuickNets: Saving Training and Preventing Overconfidence in Early-Exit Neural Architectures. - Devdhar Patel; Hava T. Siegelmann (CoRR, 2022)

Biological underpinnings for lifelong learning machines. - Dhireesha Kudithipudi; Mario Aguilar-Simon; Jonathan Babb; Maxim Bazhenov; Douglas Blackiston; Josh C. Bongard; Andrew P. Brna; Suraj Chakravarthi Raja; Nick Cheney; Jeff Clune; Anurag Reddy Daram; Stefano Fusi; et al. (Nat. Mach. Intell., 2022)

Temporally Layered Architecture for Adaptive, Distributed and Continuous Control. - Devdhar Patel; Joshua Russell; Francesca Walsh; Tauhidur Rahman; Terrence J. Sejnowski; Hava T. Siegelmann (AAMAS, 2023)

General Sequential Episodic Memory Model. - Arjun Karuvally; Terrence J. Sejnowski; Hava T. Siegelmann (ICML, 2023)

Neural Network Compiler for Parallel High-Throughput Simulation of Digital Circuits. - Ignacio Gavier; Joshua Russell; Devdhar Patel; Edward A. Rietman; Hava T. Siegelmann (IPDPS, 2023)

Episodic Memory Theory of Recurrent Neural Networks: Insights into Long-Term Information Storage and Manipulation. - Arjun Karuvally; Peter DelMastro; Hava T. Siegelmann (TAG-ML, 2023)

Temporal Weights. - Adam A. Kohan; Edward A. Rietman; Hava T. Siegelmann (CoRR, 2023)

Temporally Layered Architecture for Efficient Continuous Control. - Devdhar Patel; Terrence J. Sejnowski; Hava T. Siegelmann (CoRR, 2023)

On the Dynamics of Learning Time-Aware Behavior with Recurrent Neural Networks. - Peter DelMastro; Rushiv Arora; Edward A. Rietman; Hava T. Siegelmann (CoRR, 2023)

Episodic Memory Theory for the Mechanistic Interpretation of Recurrent Neural Networks. - Arjun Karuvally; Peter DelMastro; Hava T. Siegelmann (CoRR, 2023)

Neuromorphic high-frequency 3D dancing pose estimation in dynamic environment. - Zhongyang Zhang; Kaidong Chai; Haowen Yu; Ramzi Majaj; Francesca Walsh; Edward Jay Wang; Upal Mahbub; Hava T. Siegelmann; Donghyun Kim 002; Tauhidur Rahman (Neurocomputing, 2023)

Hidden Traveling Waves bind Working Memory Variables in Recurrent Neural Networks. - Arjun Karuvally; Terrence J. Sejnowski; Hava T. Siegelmann (ICML, 2024)

A collective AI via lifelong learning and sharing at the edge. - Andrea Soltoggio; Eseoghene Ben-Iwhiwhu; Vladimir Braverman; Eric Eaton; Benjamin Epstein; Yunhao Ge; Lucy Halperin; Jonathan P. How; Laurent Itti; Michael A. Jacobs; Pavan Kantharaju; Long Le; et al. (Nat. Mac. Intell., 2024)

Optimizing Attention and Cognitive Control Costs Using Temporally Layered Architectures. - Devdhar Patel; Terrence J. Sejnowski; Hava T. Siegelmann (Neural Comput., 2024)

Signal Propagation: The Framework for Learning and Inference in a Forward Pass. - Adam A. Kohan; Edward A. Rietman; Hava T. Siegelmann (IEEE Trans. Neural Networks Learn. Syst., 2024)

Overcoming Slow Decision Frequencies in Continuous Control: Model-Based Sequence Reinforcement Learning for Model-Free Control. - Devdhar Patel; Hava T. Siegelmann (ICLR, 2025)

Optimizing Neural Network Representations of Boolean Networks. - Joshua Russell; Ignacio Gavier; Devdhar Patel; Edward A. Rietman; Hava T. Siegelmann (ICLR, 2025)

Distributed Multi-Agent Lifelong Learning. - Prithviraj Tarale; Edward A. Rietman; Hava T. Siegelmann (Trans. Mach. Learn. Res., 2025)

Transient Dynamics in Lattices of Differentiating Ring Oscillators. - Peter DelMastro; Arjun Karuvally; Hananel Hazan; Hava T. Siegelmann; Edward A. Rietman (CoRR, 2025)

Bridging Expressivity and Scalability with Adaptive Unitary SSMs. - Arjun Karuvally; Franz Nowak; Thomas A. Keller 001; Carmen Amo Alonso; Terrence J. Sejnowski; Hava T. Siegelmann (CoRR, 2025)

Reservoir Computation with Networks of Differentiating Neuron Ring Oscillators. - Alexander Yeung; Peter DelMastro; Arjun Karuvally; Hava T. Siegelmann; Edward A. Rietman; Hananel Hazan (CoRR, 2025)