Publications

Theses

  • “An Analysis of the Accuracy of a Parameter Optimization Technique” (M.Sc. Thesis), Report TE-54, Measurement Systems Laboratory, Massachusetts Institute of Technology, January 1974.
  • “Information, Consistent Estimation and Dynamic System Identification” (Ph.D. Thesis), Report ESL-R-18, Electronic Systems Laboratory, Massachusetts Institute of Technology, November 1976.

 

Refereed papers in professional journals

  • Y. Baram and N.R. Sandell, Jr., “An Information Theoretic Approach to Dynamical Systems Modeling and Identification”, IEEE Trans. on Automatic Control, Vol. AC-23, No. 1, pp. 61-66, February 1978.
  • Y. Baram and N.R. Sandell, Jr., “Consistent Estimation on Finite Parameter Sets with Application to Dynamic System Identification”, IEEE Trans. on Automatic Control, Vol. AC-23, No. 3, pp. 451-454, June 1978.
  • Y. Baram, “A Sufficient Condition for Consistent Discrimination Between Stationary Gaussian Models”, IEEE Trans. on Automatic Control, Vol. AC-23, No. 5, pp. 958-960, October 1978.
  • Y. Baram, “Non-Stationary Model Validation from Finite Data Records”, IEEE Trans. on Automatic Control, Vol. AC-25, No. 1, pp. 10-19, February 1980.
  • Y. Baram, “Decomposition and Convergence of Likelihood Ratios”, Information and Control, Vol. 45, No. 2, pp. 170-177, May 1980.
  • Y. Baram, “On the Linearly Constrained Minimum Mean Square Error Estimate”, IEEE Trans. on Automatic Control, Vol. AC-25, No. 3, p. 573, June 1980.
  • Y. Baram, “Truncation Error Bounds and Convergence of Least-Squares Estimates”, SIAM J. on Control and Optimization, Vol. 18, No. 4, pp. 346-351, July 1980.
  • Y. Baram, “Model Validation Using Mismatched Filters”, Int. J. of Control, Vol. 32, No. 2, pp. 191-198, August 1980.
  • Y. Baram, “Distance Measures for Stochastic Models”, Int. J. of Control, Vol. 33, No. 1, pp. 149-157, January 1981.
  • Y. Baram and Y. Be’eri, “Stochastic Model Simplification”, IEEE Trans. on Automatic Control, Vol. 26, No. 2, pp. 379-390, April 1981.
  • Y. Baram, “Realization and Reduction of Markovian Models from Non-Stationary Data”, IEEE Trans. on Automatic Control, Vol. AC-26, No. 6, pp. 1225-1231, December 1981.
  • Y. Baram, “Identifying Non-Stationary Measurement Noise in Linear Systems”, IEEE Trans. on Information Theory, Vol. IT-28, No. 1, pp. 122-123, January 1982.
  • Y. Baram, “Recovering the Poles of Linear Systems”, IEEE Trans. on Automatic Control, Vol. AC-27, No. 6, pp. 1157-1160, December 1982.
  • Y. Baram, “On Two Dimensional Data Representation by Radial Base Functions”, IEEE Trans. on Acoustics, Speech and Signal Processing, Vol. ASSP-32, No. 1, pp. 163-164, February 1984.
  • Y. Baram, “A Geometric Approach to Stochastic Model Reduction by Canonical Variables”, IEEE Trans. on Automatic Control, Vol. AC-29, No. 4, pp. 358-359, April 1984.
  • Y. Baram, “On Consistent Estimation of the Characteristic Coefficients and the Poles of Multivariate Linear Systems”, IEEE Trans. on Automatic Control, Vol. AC-29, No. 5, pp. 479-480, May 1984.
  • Y. Baram and U. Shaked, “On the Order of the Minimal Output Representation of Stochastic Linear Systems”, International Journal of Control, Vol. 40, No. 1, pp. 179-192, July 1984.
  • Y. Baram and M. Margalit, “Surface Fitting by Pseudo Potential Functions”, IEEE Trans. on Geoscience and Remote Sensing, Vol. GE-22, No. 5, pp. 455-461, September 1984.
  • Y. Baram and D. Eidelman, “An Information Approach to Fixed Gain Design”, IEEE Trans. on Aerospace and Electronic Systems, Vol. AES-21, No. 1, pp. 47-55, January 1985.
  • Y. Baram and U. Shaked, “Minimal Order Estimation of Continuous-Time Stochastic Linear Systems”, IEEE Trans. on Automatic Control, Vol. AC-30, No. 5, pp. 483-484, May 1985.
  • Y. Baram and U. Shaked, “Minimal Order Estimation of Discrete-Time Stochastic Linear Systems”, SIAM J. on Control and Optimization, Vol. 24, No. 4, pp. 817-820, July 1986.
  • Y. Baram, “A Lower Bound on the Mean Square Error of Reduced Order Estimators for Non-Linear Processes”, IEEE Trans. on Information Theory, Vol. IT-33, No. 6, November 1987.
  • Y. Baram and G. Kalit, “Order Reduction in Linear State Estimation under Performance Constraints”, IEEE Trans. on Automatic Control, Vol. AC-32, No. 11, November 1987.
  • Y. Baram and B. Porat, “Identification of Minimal Order State-Space Models from Stochastic Input-Output Data”, SIAM J. on Control and Optimization, Vol. 26, No. 1, Jan. 1988.
  • Y. Baram and U. Shaked, “On the Internal Structure of Minimal Realizations”, Automatica, vol.24, no.5, pp. 715-717, September 1988.
  • Y. Baram and T. Kailath, “Estimability and Regulability of Linear Systems”, IEEE Trans. on Automatic Control, vol. AC-33, no. 12, pp. 1116-1121, December 1988.
  • R.E. Bach, Jr. and Y. Baram, “Recursive Inversion of Externally Defined Linear Systems”, IEEE Trans. on Automatic Control, vol. AC-34, no.5, pp. 635-637, June 1989.
  • Y. Baram, “Associative Memory in Fractal Neural Networks”, IEEE Trans. on Systems, Man and Cybernetics, vol. SMC-18, no.5, pp. 1133-1141, October 1989.
  • Y. Baram, “Construction of Linear Predictors for Stationary Vector Sequences”, IEEE Trans. on Automatic Control, vol. 35, no. 2, pp. 236-239, February 1990.
  • Y. Baram, “Closed Forms for the Levinson Coefficients of Polynomial Compensators and Inverse Systems”, IEEE Trans. on Automatic Control, vol. 35, no. 8, pp. 929-932, August 1990.
  • Y. Baram, “Ground States of Partially Connected Neural Networks”, (invited paper), Proceedings of the IEEE, Special Issue on Neural Networks, pp. 1575-1578, October 1990.
  • Y. Baram, “Memory Capacity of Ternary Hebbian Networks”, IEEE Trans. on Information Theory, vol. 37, no.3, pp. 528-534, May 1991.
  • Y. Baram, “Encoding Unique Global Minima in Nested Neural Networks”, IEEE Trans. on Information Theory, vol. 37, no. 4, pp. 1158-1162, July 1991.
  • Y. Baram and D. Sal’ee, “Lower Bounds on the Capacities of Binary and Ternary Networks Storing Sparse Random Vectors”, IEEE Trans. on Information Theory, vol. 38, no. 6, pp. 1633–1647, November 1992.
  • D. L. Ringach and Y. Baram, “A Diffusion Mechanism for Obstacle Detection from Size–Change Information,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 16, No. 1 pp. 76 — 80, January 1994.
  • Y. Baram, “Corrective Memory by a Symmetric Sparsely Encoded Network,” IEEE Trans. on Information Theory, Vol. 40, No. 2, pp. 429 — 438, March 1994.
  • N. Peterfreund and Y. Baram, “Second–Order Bounds on the Domain of Attraction and the Rate of Convergence of Nonlinear Dynamical Systems and Neural Networks,” IEEE Trans. on Neural Networks, Vol. 5, No. 4, pp. 551 — 560, July 1994.
  • Y. Baram, “Memorizing Binary Vector sequences by Sparsely Encoded Networks,” IEEE Trans. on Neural Networks, Vol. 5, No. 6, pp. 974–981, November 1994.
  • D. Sal’ee and Y. Baram, “High-Capacity Hebbian Storage by Sparse Sampling,” IEEE Trans. on Neural Networks, Vol. 6, No. 2, pp. 349–356, March 1995.
  • Y. Baram and Y. Barniv, “Obstacle Detection by Recognizing Binary Expansion Patterns,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 32, No. 1, pp. 191–198, January 1996.
  • Y. Baram, “A Bird’s Eye View on the Descent Trajectory,” IEEE Trans. on Aerospace and Electronic Systems,Vol. 32, No. 3, pp. 1085–1087, July 1996.
  • Y. Baram, “Classification by Balanced Representation,” Neurocomputing, Vol. 13, pp. 347–357, 1996.
  • Z. Roth and Y. Baram, “Multi–Dimensional Density Shaping by Sigmoids,” IEEE Trans. on Neural Networks, Vol. 7, No. 5, pp. 1291–1298, Septembe 1996.
  • Y. Baram, Y. Barniv and T. Sony, “Detecting Collision from Gray–Level Expansion by a Neural Network,” Neurocomputing, Vol. 16, pp. 77-84, 1997.
  • N. Peterfreund and Y. Baram, “Convergence Analysis of Nonlinear Dynamical Systems by Nested Lyapunov Functions,” IEEE Trans. on Automatic Control, Vol. 43, No. 8, pp. 1179-1184, August 1998.
  • Y. Baram, “Classification by Maximum-Information Density Intersection,” Neural Processing Letters, Vol. 8, No. 1, pp. 1 – 8, August 1998.
  • Y. Baram, “Partial Classification: The Benefit of Deferred Decision,” IEEE Trans. on Pattern Analysis and Machine intelligence, Vol. 20, No. 8, pp. 769 – 776, August 1998.
  • N. Peterfreund and Y. Baram, “Trajectory Control of Convergent Networks,” Neural Processing Letters, Vol. 8, No. 2, pp. 99-106, October 1998.
  • Y. Baram, “A Geometric Approach to Consistent Classification,” Pattern Recognition, Vol. 33, pp. 177-184, 2000.
  • Y. Baram, “Walking on Tiles,” Neural Processing Letters, Vol. 10, No. 2, pp. 81-87, October 1999.
  • Y. Baram, “Bayesian Classification by Iterated Weighting,” Neurocomputing, Vol. 25, pp. 73-79, 1999.
  • Y. Baram. Random Embedding Machines for Pattern Recognition. Neural Computation, Vol. 13, pp. 2533-2548, 2001.
  • M. Zlochin and Y. Baram. Manifold Stochastic Dynamics for Bayesian Learning. Neural Computation, Vol. 13, pp. 2549-2572, 2001.
  • Y. Baram, J. Aharon-Peretz, Y. Simionovici, L. Ron. Walking on Virtual Tiles. Neural Processing Letters, Vol. 16, pp. 227 – 233, 2002.
  • Y. Baram, R. El-Yaniv, J. Luz. On-line Choice of Active Learning Algorithms. J. of Machine Learning Research (JMLR)}, Vol. 5, pp. 255-291, March 2004.
  • Y. Baram. Learning by Kernel Polarization. Neural Computation, Vol. 17:6, pp. 1264-1275, June, 2005.
  • Y. Baram and A. Miller. Virtual Reality Cues for Improvement of Gait in Multiple Sclerosis Patients. Neurology, 66;178-181, January, 2006. (Research Award for the Best Platform Presentation in Research in Multiple Sclerosis of the 19th Annual Meeting of the Consortium of Multiple Sclerosis Centers, Orlando, Florida, June, 2005)
  • S. Polak, Y. Barniv and Y. Baram. Head Motion Anticipation for Virtual-Environment Applications using Kinematics and EMG Energy. IEEE Trans. on Systems, Man andCyberneticas, Part A, Vol. 36m No. 3, pp. 569-576, May 2006.
  • Y. Baram and A. Miller. Auditory Feedback for Improvement of Gait in Multiple Sclerosis Patients. Journal of the Neurological Sciences, 254, 90-94, February, 2007.
  • Y. Baram. Closed-loop augmented reality for movement disorders. (invited paper) Frontiers in Neuroscience, Special Issue on Augmented Cognition, 3 (1), 112-113, May 2009.
  • Y. Baram, J. Aharon-Peretz, Ruben Lenger. Virtual Reality Feedback for Gait Improvement in Patients with Idiopathic Senile Gait Disorders and in Patient with History of strokes.  Journal of the American Geriatrics Society, Volume 58 Issue 1, 191-192, January 2010.
  • Y. Baram, A. Miller. Glide-Symmetric locomotion reinforcement in patients with multiple sclerosis by visual feedback. Disability and Reabilitation: Assistive Technology;5(5):323-6. 2010.
  • A. J. Espay, Y. Baram, A. K. Dwivedi, R. Shukla, M. Gartner, L. Gaines, A. P. Duker, F. J. Revilla. At-home training with closed-loop augmented-reality cueing device for improvement of gait in patients with Parkinson’s disease. Journal of Rehabilitation Research & Development (JRRD) Vol. 47, No. 6, pp. 573-582, 2010.
  • Y. Baram and R. Lenger. Gait improvement in patients with cerebral palsy by visual and auditory feedback. Neuromodulation, 15(1):48-52, Jan-Feb,2012
  • Y. Baram. Noninvertibility, Chaotic coding and chaotic multiplexity in synaptically modulated neural firing.  Neural Computation, Vol. 24, No. 3, pp. 676-699, March 2012.
  • T. Salman and Y. Baram. Quantum Set Intersection and its Application to Associative Memory. Journal of Machine Learning Research (JMLR) 13, 3177-3206, 2012.
  • Y. Baram. Global Attractor Alphabet of Neural Firing Modes,” Journal of Neurophysiology. Vol. 110, pp. 907-915, 2013.
  • Y. Baram.  (Review Article). Virtual sensory feedback for gait improvement in neurological patients. Frontiers in Neurology, Vol. 4, Article 138, Oct. 2013.
  • Velu, P. D., Mullen, T., Noh, E., Valdivia, M., Poizner, H., Baram, Y., de Sa, V. R. (2013). Effect of visual feedback on the occipito-parietal-motor network in Parkinsons disease patients with freezing of gait. Frontiers in Neurology, 4(209), 2013.
  • Badarny S., Aharon-Peretz J., Susel Z.,Habib G., Baram Y. Virtual Reality Feedback Cues for Improvement of Gait in Patients with Parkinsons Disease. Tremor and Other Hyperkinet Mov., 4, 2014.
  • Baram, Y., Aharon-Peretz, J., Badarny, S., Susel, Z. & Schlesinger, I. Closed-loop auditory feedback for the improvement of gait in patients with Parkinson’s disease. J. Neurol. Sci. 363 (15), 104–106, 2016.
  • Baram, Y. Developmental metaplasticity in neural circuit codes of  firing and structure. Neural Networks 85, 182–196, 2017.
  • Baram, Y. Transformative Autonomous Entrainment of Gait in Neurological Patients (Invited Review). J Neurolog Neurosci 8 (1): 177, 2017.
  • Baram, Y. Embedding Neurological Autonomy in Gait Entrainment (Invited Editorial). J Neurolog Neurosci 8 (2):182, 2017.
  • Baram, Y. Asynchronous Segregation of Cortical Circuits and Their Function: A Life-long Role for Synaptic Death. AIMS Neuroscience, 2017, 4(2): 87-101.

 

Papers in Conference Proceedings

  • Y. Baram, “Separation and Validation of Linear Models: Model Set Compatibility”, Proceedings of the Lawrence Symposium on Systems and Decision Sciences, pp. 256-260, Berkeley,California, October 1977.
  • Y. Baram and N.R. Sandell, Jr., “An Information Theoretic Approach to Dynamical Systems Modeling and Identification”, Proceedings of the Conference on Decision and Control, (CDC), pp. 1113-1118, New Orleans, December 1977.
  • Y. Baram and Y. Be’eri, “Simplification of High Order and Time Varying Linear Gaussian Models”, Proceedings of the Joint Automatic Control Conference (JACC), Paper FA8-B, San Francisco, August 1980.
  • Y. Baram and D. Eidelman, “Fixed-Gain Controller Design for Aircraft”, Proceedings of the 23rd Israel Annual Conference on Aviation and Astronautics, pp. 211-216, Haifa, February 1981.
  • Y. Baram and M. Margalit, “Reconstruction and Compression of Two Dimensional Fields from Sampled Data by Pseudo-Potential Functions”, Proceedings of the NATO Advanced Study Institute on Non-Linear Stochastic Problems, pp. 511-524, Algarve, Portugal, May 1982.
  • Y. Baram and D. Eidelman, “Fixed-Gain Controller and Filter Design for Stochastic Systems with Application to Aircraft”, Proceedings of the International Federation of Automatic Control (IFAC) Symposium on Identification and System Parameter Estimation, pp. 1582-1587, Arlington, Virginia, June 1982.
  • Y. Baram, “Stochastic Realization and Reduction of Processes Generated by Time Invariant Systems”, Proceedings of the American Control Conference (ACC), pp. 612-613, Arlington,Virginia, June 1982.
  • Y. Baram and U. Shaked, “Mode Cancellation and Retention in Stochastic Linear Systems”, Proceedings of the American Control Conference (ACC), pp. 677-679, Arlington, Virginia, June 1982.
  • Y. Baram, “Minimal Order Representation, Estimation and Feedback of Stochastic Linear Systems”, Proceedings of the FTNS Conference on Mathematical Systems Theory, Beer-Sheva,Israel, June 1983.
  • Y. Baram and U. Shaked, “Minimal Order Estimation of Multivariable, Continuous Time Stochastic Linear Systems”, Proceedings of the American Control Conference (ACC), pp. 496-499, San Diego, California, June 1984.
  • Y. Baram and U. Shaked, “Order Minimality of Multivariable, Continuous-Time Stochastic Realizations”, Proceedings of the 7th IFAC Symposium on Identification and System Parameter Estimation, pp. 765-768, York, U.K., July 1985.
  • Y. Baram and U. Shaked, “Minimal Order Estimation of Multivariable, Discrete Time Stochastic Linear Systems”, Proceedings of the 24th IEEE Conference on Decision and Control (CDC), pp. 1637-1639, Fort Lauderdale, Florida, December 1985.
  • Y. Baram and B. Porat, “Identification of Minimal Order State-Space Models from Stochastic Input-Output Data”, Proceedings of the American Control Conference (ACC), pp. 2022-2026, Seattle, Washington, June 1986.
  • Y. Baram, “Linear Estimation in Reduced Dimension”, Proceedings of the 18th JAACE Symposium on Stochastic Systems Theory and its Applications, pp. 101-104, Tokyo, Japan, October 1986.
  • Y. Baram and G. Kalit, “Mean-Square Error Bounds for Reduced Order Linear State Estimation”, Proceedings of the American Control Conference (ACC), Minneapolis, Minnesota, June 1987.
  • Y. Baram and T. Kailath, “Estimability and Regulability of Linear Systems”, Proceedings of the Conference on Decision and Control (CDC), Los Angeles, California, December 1987.
  • Y. Baram, “Associative Memory in Neural Networks”, The 16th Conv. of Electrical and Electronics Engineers in Israel, Tel-Aviv, March 1989.
  • Y. Baram, “Nested Neural Networks and their Codes”, Proceedings of the IEEE International Symposium on Information Theory, San Diego, CA, p. 9, January 1990.
  • D. Ringach and Y. Baram, “A Diffusion Mechanism for Obstacle Detection from Size-Change Information”, The 17th Conv. of Electrical and Electronics Engineers in Israel, Tel-Aviv, March 1991.
  • Y. Baram, “Classification, Mapping and Memory by Artificial Neural Networks”, The 17th Conv. of Electrical and Electronics Engineers in Israel, Tel-Aviv, March 1991.
  • Y. Baram, “Corrective Memory by a Symmetric Sparsely Encoded Network,” Proceedings of the IEEE International Symposium on Information Theory, San Antonio, Texas, p. 430, January 1993.
  • Y. Baram, “Hebbian Classification in and around the Unit Cube,” Proceedings of the IEEE International Symposium on Information Theory, Trondheim, Norway, June 1994.
  • Y. Baram, “Classification by Balanced, Linearly separable Representations,” Proceedings of the International Conference on Neural Networks, Orlando, Florida, pp. 3032–3037, June 1994.
  • Y. Baram and Y. Barniv, “Obstacle Detection by Recognizing Binary Expansion Patterns,” Proceedings of the International Conference on Neural Networks (ICNN), Orlando, Florida, pp. 4175–4179, June 1994.
  • Y. Baram and Z. Roth, “Forecasting by Density Shaping using Neural Networks,” Proceedings of the 1995 IEEE Conference on Computational Intelligence for Financial Engineering (CIFER), pp. 57–71, New York, NY, April 1995.
  • Y. Baram, Y. Barniv and T. Sony, “Detecting Collision from Gray–Level Expansion by a Neural Network,” Proceedings of the International Conference on Neural Networks and Signal Processing, Nanjing. P. R. China, December 1995.
  • Y. Baram and Z. Roth, “Multi–Dimensional Density Shaping by Sigmoids,” Proceedings of the IEEE International Conference on Neural Networks, Perth, Western Australia, November 1995.
  • Y. Baram, “Soft Prediction of Stock Behaviour by Spherical Extension,” Proceedings of the Conference on Neural Networks in the Capital Markets (NNCM), Pasadena, CA, November 1996.
  • Y. Baram, “Firm and Soft Classification in Real Space,” Proceedings of the Conference on Neural Information Processing Systems (NIPS), Denver, CO, December 1996.
  • Y. Baram, “Soft Nearest-Neighbor Classification,” Proceedings of the IEEE International Conference on Neural Networks (ICNN), Houston, TX, June 1997.
  • Y. Baram, “Partial Classification: the Benefit of Deferred Decision,” invited for oral presentation at the IEEE Conference on Knowledge-Based Electronic Systems, Adelaide Australia, April 1998.
  • Y. Baram, “Bayesian Classification by iterated Priors,” accepted for oral presentation at the IEEE International Conference on Neural Networks, Anchorage, Alaska, April 1998.
  • M. Zlochin and Y. Baram, “A Differential-Geometric Approach to Learning,” Conference on Neural Computation in Science and Technology, Jerusalem, Israel, October, 1999.
  • M. Zlochin and Y. Baram, “Manifold Stochastic Dynamics for Bayesian Learning,” full oral presentation (one of 25 out of 467 submitted) Neural Information Processing Systems (NIPS) Conference, Denver, CO, November 1999.
  • Y. Baram, “Low-complexity pattern recognition by random relative clustering,” Proceedings of the International Conference of Pattern Recognition (ICPR), Barcelona, Spain, Track II, pp. 752-758, September 2000.
  • Y. Baram, J. Aharon-Peretz, Y. Simionovici, L. Ron, “Gait Manegement in Parkinson’s Patients by Virtual Reality in Closed-Loop,” oral presentation, Annual Meeting of the Israeli Neurological Society, Zichron Yaakov, November 2000.
  • Y. Baram, J. Aharon-Peretz, Y. Simionovici, L. Ron, “Walking on Tiles: Virtual Reality in Closed-Loop Improves Gait in Parkinson’s Patients,” oral presentation at the 53’rd Annual Meeting of the American Academy of Neurology, Philadelphia, PA, May 2001, {\it Neurology}, Vol. 56, No. 8, Supplement 3, p. A308, April 2001.
  • G. Shakhnarovich, R. El-Yaniv, Y. Baram, “Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation,” Proceedings of the International Conference of Machine Learning, ICML, Boston, June 2001.
  • M. Zlochin and Y. Baram, “The Bias-Variance Dilema of the Monte-Carlo Method,” in Artificial Neural Networks – ICANN 2001, Dorffner, G., Bischof’ H., Hornik, K. (Eds.), Lecture Notes in Computer Science, Vol. 2130, Springer Verlag, 2001.
  • G. Shakhnarovich, R. El-Yaniv, Y. Baram, “Data Cloning for Accuracy Estimation,” ICML, Boston MA, June 2001.
  • M. Zlochin and Y. Baram, “Hybrid Adaptive Importance Sampling,” IJCNN, Honolulu Hawaii, May 2002.
  • Y. Baram R. El-Yaniv and J. Luz, “Online Choice of Active Learning Algorithms,” Proceedings of the International Conference on Machine Learning (ICML) 2003, pp. 19 – 26
  • Y. Baram and A. Miller, “Virtual Reality Cues for Improvement of Gait in Multiple Sclerosis Patients”, 19’th Annual Conference of the Consortium of Multiple Sclerosis Centers (CMSC), Orlando, Florida, June 2005 (The Research Award for the Best Platform Presentation in Research in Multiple Sclerosis)
  • Y. Baram and A. Miller,”Sensory Feedback for Improvement of Gait in Patients with Movement Disorders, 21st Congress of the European Committee for Treatment and Research in Multiple Sclerosis, Saloniki, Greece, August 2005.
  • Y. Baram and A. Miller,”Auditory Feedback for Improvement of Gait in Patients with Multiple Sclerosis,” 22nd Congress of the European Committee for Treatment and Research inMultiple Sclerosis, Madrid, Spain, September 2006.
  • Y. Baram, S. Badarny and J. Aharon-Peretz, “Virtual Reality Feedback Cues for Improvement of Gait in Patients with Parkinson’s Disease,” 10th International Congress of Parkinson’s Disease and Movement Disorders, Kyoto, Japan, October 2006.
  • Y. Baram, R. El-Yaniv and J. Luz, “Online Choice of Active Learning Algorithms,” Proceedings of ICML 2003, pp. 19-26.
  • A. J. Espay, Y. Baram, N. Burton, M. Gartner, H. A. Miranda, A. P. Duker, F. J. Revilla, At-home training with virtual reality cues for improvement of gait in patients with Parkinson’s disease. Accepted for platform presentation, 60th Annual Meeting of the American Academy of Neurology, Chicago, IL, April 2008.
  • Y. Baram, Gait Regulation by Nested Phase-Locked Inhibit-and-Fire Neuronal Circuits, 60th Annual Meeting of the American Academy of Neurology, Chicago, IL, April 2008.
  • Y. Baram, Dopaminetgic neuron regulation with and without dopamine. 2nd International on Gait and Mental Function, Amsterdam, February 2008.
  • Penick D, Soper A, Belagaje S, Kissela B, Espay A, Baram Y, Dunning K, Effect of Dynamic Visual and Auditory Feedback on Gait in Persons with Stroke, APTA CSM, Alexandria, VA, 2009.
  • Y. Baram, A. Miller, Glide-Symmetric Visual Feedback for MS Patients ENS, Milan, Italy, 2009
  • Y. Baram, R. Lenger, Virtual Reality Visual Feedback for Gait Improvement in Children with Cerebral Palsy, ENS, Milan, Italy, 2009.
  • Y. Baram, J. Aharon-Peretz, R. Lenger, Virtual reality cues for gait improvement in patients with idiopathic senile gait disorders and in patient with history of previous strokes, ESC,Stokholm, Sweden 2009.
  • Y. Baram, R. Lenger, Gait improvement in patients with cerebral palsy by visual and auditory feedback, VR, Haifa 2009.
  • Y. Baram, Autoregressive representation of firing activity in neural networks, 15th International Conference on Neurocybernetics, Rostov, Russia, 2009,
  • Y. Baram “Augmented reality for gait improvement in movement disorders patients,”(invited lecture) IBM Symposium on Synthetic Reality, Haifa, Israel, January 2010.
  • Y. Baram “Gait Regulation and Rehabilitation by Augmented Reality,”, 14th International Conference on Cognitive and Neural Systems, Boston, MA, May 2010.
  • Y.Baram “Augmented Reality for Gait Regulation and Rehabilitation,”, BIT’s 1st Annual World Congress of NeuroTalk, Singapore, June, 2010.
  • Y.Baram “Gait Improvement in Patients with Cerebral Palsy by Visual and Auditory Feedback,” International Neuromodulation Society 10th World Congress, London, May 2011.
  • T.Salman and Y.Baram “Quantum Associative Memory as an operator,”Proceedings of the IADIS International Conference on Intelligent Systems and Agents, pp. 97-101, Rome, Italy, July 2011.
  • Y. Baram “Sensory feedback for gait improvement in movement disorders patients,” TCII Symposium on Healthier Living, New York, NY July 2012.
  • Y. Baram “Sliding Global Attractors of Neural Learning and Memory,” Proceedings of the 4th International Conference on Neural Computation Theory and Applications (NCTA),pp  570-575, Barcelona, Oct. 2012.
  • Y. Baram “Sensory feedback Navigation and control of humans with Parkinson’s and other Movement Disorders,”Proceedings of the Itzhack I. Bar-Itzhack Memorial Symposium on Estimation, Navigation and Spacecraft Control, pp 266-269, Haifa, Oct. 2012.
  • Jayasinghe, N., Baram, Y., Hillstrom, H., Backus, S., Ganz, S., & Spielman, S. Gait training using visual and auditory feedback cues with older adults who are fearful of falling.  Annual meeting of the Gerontological Society of America, New Orleans, LA. November 20, 2013.
  • Y. Baram. Virtual Reality Device for MS patients (Keynote lecture). The Second Pan-European Multiple Sclerosis Multi-Stakeholder Colloquium. Brussels, Belgium, May 15-16, 2015.
  • Y. Baram.   Virtual Reality Based Interventions for Gait and Balance in Multiple Sclerosis (Keynote Lecture). 6th International Symposium on Gait and Balance in Multiple Sclerosis. Portland, Oregon, September 9-10, 2016.

 

Research Reports

  • Y. Baram, “On-Orbit State Estimator for the Space Shuttle Vehicle Autopilot”, SSV Memo 74-10c-21. The Charles Stark Draper Laboratory, Inc., Sept. 1974.
  • Y. Baram, “Incorporating Pilot Commands in the Longitudinal Control System of the F-8 Aircraft”, NASA Grant HSG-1018, Interim Report 10, Electronic Systems Laboratory, MIT, Dec. 1975.
  • Y. Baram and N.R. Sandell, Jr., “Consistent Estimation on Finite Parameter Sets with Application to Dynamic System Identification”, ESL-P-699, Electronic Systems Laboratory, MIT, July 1976.
  • Y. Baram, “Unified Formulation and Computer Program Specification for Bias Estimation from a Single Test”, IOM-YB-77-1, The Analytic Sciences Corporation, March 1977.
  • Y. Baram, “Multiple Experiment Residual Analysis for Model Validation”, IOM-YB-77-2, The Analytic Sciences Corporation, April 1977.
  • Y. Baram, “MERA Supplementary Note: Shift Determination”, IOM-YB-77-4, The Analytic Sciences Corporation, April 1977.
  • Y. Baram, “Separation and Validation of Linear Models with Application to Bias Source Investigation”, IOM-YB-77-5, The Analytic Sciences Corporation, June 1977.
  • Y. Baram, “Eliminating Initialization Effects from the Multiple Experiment Residual Analysis”, IOM-YB-77-6, The Analytic Sciences Corporation, August 1977.
  • Y. Baram, “Noncentral Chi-Square Subroutines”, IOM-YB-77-7, The Analytic Sciences Corporation, August 1977.
  • Y. Baram, “System Identification from Repeated Operations”, IOM-YB-77-8, The Analytic Sciences Corporation, November 1977.
  • Y. Baram, “On the Feasibility of Analytic Power Study for Residual Analysis”, IOM-YB-77-9, The Analytic Sciences Corporation, December 1977.
  • Y. Baram, “Residual Tests using Quadratic Form Distributions. Part I: Original Coordinates”, IOM-YB-78-1, The Analytic Sciences Corporation, March 1978.
  • Y. Baram, “Residual Tests using Quadratic Form Distributions. Part II: Principal Coordinates”, IOM-YB-78-2, The Analytic Sciences Corporation, March 1978.
  • Y. Baram and D. Eidelman, “Fixed Gain Controller Design for Aircrafts”, Report No. AFOSR-80-0178-002, U.S. Air Force of Scientific Research, June 1981.
  • Y. Baram, “Orthogonal Patterns in Binary Neural Networks”, NASA Technical Memorandum No. 100060, March 1988.
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