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Publications of Elie Bienenstock

High-dimensional sphere embedding - An application to POS induction

Yariv Maron, Michael Lamar and Elie Bienenstock (2010)

Advances in Neural Information Processing Systems 23, J. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R.S. Zemel and A. Culotta eds., 1567--1575.

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Latent Descriptor Clustering for Unsupervised POS Induction
Michael Lamar, Yariv Maron and Elie Bienenstock (2010)

Proceedings of the 2010 Conference on Empirical Methods In Natural Language Processing, Association for Computational Linguistics, Cambridge, MA, 799--809.

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Precise spatio-temporal patterns among visual cortical areas and their relation to visual-stimulus processing

Inbal Ayzenshtat, Elhanan Meirovithz, Hadar Edelman,Uri Werner-Reiss, Elie Bienenstock, Moshe Abeles, and Hamutal Slovin,

The Journal of Neuroscience (2010) 30(33):11232--11245.

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SVD and Clustering for Unsupervised POS Tagging

Michael Lamar, Yariv Maron, Mark Johnson, and Elie Bienenstock (2010)

Proceedings of the ACL 2010 Conference Short Papers,
Association for Computational Linguistics, Uppsala, Sweden, 215--219,
Best short paper award.
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Estimating the Entropy of Binary Time Series: Methodology, Some Theory and a Simulation Study

Yun Gao, Ioannis Kontoyiannis, and Elie Bienenstock, 2008, Entropy 10, no. 2: 71--99.

(pdf) (abstract)

Neocortical self-structuration as a basis for learning

Doursat, R. & Bienenstock, E. (2006) 5th International Conference on Development and Learning (ICDL 2006), May 31-June 3, 2006, Indiana University, Bloomington, Indiana.

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From the entropy to the statistical structure of spike trains

Yun Gao, Ioannis Kontoyiannis, and Elie Bienenstock, IEEE International Symposium on Information Theory, Seattle, WA, July 2006

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Single-trial prediction of discrete hand movements with electroencephalography

Jerome N. Sanes, Timothy O'Keefe, Richard Archibald, Elie Bienenstock, Abstract, Human Brain Mapping 2006.

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The self-organization of synfire patterns
Rene Doursat and Elie Bienenstock, to appear: 10th International Conference on Cognitive and Neural Systems (ICCNS), Boston University, Massachusetts, May 17-20, 2006.

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Bayesian Population Decoding of Motor Cortical Activity Using a Kalman Filter

Wei Wu, Yun Gao, Elie Bienenstock, John P. Donoghue, Michael J. Black, Neural Computation, Volume 18, Number 1, January 2006, pp. 80-118(39), (abstract), (pdf preprint), (pdf from publisher)

Autoassociative Memory Retrieval and Spontaneous Activity Bumps in Small-World Networks of Integrate-and-Fire Neurons,

Anastasia Anishchenko, Elie Bienenstock, and Alessandro Treves, March 2004.

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Modeling and decoding motor cortical activity using a switching Kalman filter
Wu, W., Black, M. J., Mumford, D., Gao, Y., Bienenstock, E., Donoghue, J. P.,
IEEE Trans. Biomedical Engineering, 51(6):933-942, June 2004.
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Estimating the Entropy Rate of Spike Trains

Yun Gao, Ioannis Kontoyiannis and Elie Bienenstock, preprint, February 2004.

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Using Statistics of Natural Images to Facilitate Automatic Receptive Field Analysis,

Matthew Harrison, Stuart Geman and Elie Bienenstock, Preprint, February 2004.

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At what time scale does the nervous system operate?
N.G. Hatsopoulos, S. Geman, A. Amarasingham, and E. Bienenstock.  Neurocomputing, 52--54, pp 25--29 (2003).

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Connecting brains with machines: The neural control of 2D cursor movement,
Black, M. J., Bienenstock, E., Donoghue, J. P., Serruya, M., Wu, W., Gao, Y.,
1st International IEEE/EMBS Conference on Neural Engineering, pp. 580-583, Capri, Italy, March 20-22, 2003.
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A quantitative comparison of linear and non-linear models of motor cortical activity for the encoding and decoding of arm motions,
Gao, Y., Black, M. J., Bienenstock, E., Wu, W., Donoghue, J. P.,
1st International IEEE/EMBS Conference on Neural Engineering, pp. 189-192, Capri, Italy, March 20-22, 2003.
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A switching Kalman filter model for the motor cortical coding of hand motion,
Wu, W., Black, M. J., Mumford, D., Gao, Y., Bienenstock, E., Donoghue, J. P.,
Proc. IEEE Engineering in Medicine and Biology Society, pp. 2083-2086, Sept. 2003
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Neural decoding of cursor motion using a Kalman filter,
Wu, W., Black, M. J., Gao, Y., Bienenstock, E., Serruya, M., Shaikhouni, A., Donoghue, J. P.,
Advances in Neural Information Processing Systems 15, S. Becker, S. Thrun and K. Obermayer (Eds.), MIT Press, pp. 117-124, 2003.
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Inferring hand motion from multi-cell recordings in motor cortex using a Kalman filter,
Wu, W., Black, M. J., Gao, Y., Bienenstock, E., Serruya, M., and Donoghue, J. P., SAB'02-Workshop on Motor Control in Humans and Robots: On the Interplay of Real Brains and Artificial Devices, August 10, 2002, Edinburgh, Scotland (UK), pp. 66-73.
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Probabilistic inference of arm motion from neural activity in motor cortex,
Gao, Y., Black, M. J., Bienenstock, E., Shoham, S., Donoghue, J., Advances in Neural Information Processing Systems, NIPS 14, Thomas G. Dietterich, Sue Becker, and Zoubin Ghahramani, eds., The MIT Press, pp 213-220 (2002).
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Inferring hand motion from multi-cell recordings in motor cortex using a Kalman filter,
W. Wu, M. Black, Y. Gao, E. Bienenstock, M. Serruya, J. Donoghue.Program No. 357.5. Washington, DC: Soc. Neurosci. Abstr., (2002).

Encoding/decoding of arm kinematics from simultaneously recorded MI neurons,
Gao, Y., Bienenstock, E., Black, M., Shoham, S., Serruya, M., Donoghue, J., Society for Neuroscience Abst. Vol. 27, Program No. 572.14 (2001).
(abstract)

Assessing Precise Temporal Patterns of Spikes among Cortical Neurons,

Nicholas G. Hatsopoulos, Asohan Amarasingham, Elie Bienenstock, Stuart Geman and John P. Donoghue, Soc. Neurosci. Abstr., 27: 63.1 (2001).

A Statistical Tool for Testing Hypothesis about the Temporal Resolution of Neural Activity,

Akira Date, Elie Bienenstock and Stuart Geman, Soc. Neurosci. Abstr., 26: 828.6 (2000).

A statistical technique for the detection of fine temporal structure in multi-neuronal spike trains,

Akira Date, Elie Bienenstock and Stuart Geman, Soc. Neurosci. Abstr., 25, part 2, p.1411 (1999).

Regulated Criticality in the Brain?
Elie Bienenstock and Daniel Lehmann, Advances in Complex Systems, 1:361-384 (1998).
abstract
FULL ARTICLE: postscript -- pdf (pages are in reverse order)

On the Temporal Resolution of Neural Activity
Akira Date, Elie Bienenstock and Stuart Geman, Technical Report, Division of Applied Mathematics, Brown University (1998).
abstract
full article (postscript)

Correlational Models of Synaptic Plasticity: Development, Learning and Cortical Dynamics of Mental Representations
Yves Fregnac and Elie Bienenstock, In: Mechanistic Relationships between Development and Learning, T.J. Carew, R. Menzel and C.J. Shatz eds, Wiley and sons, pp 113-148 (1998).

Compositionality, MDL Priors, and Object Recognition
Elie Bienenstock, Stuart Geman, and Daniel Potter, In: Advances in Neural Information Processing Systems 9 , M.C. Mozer, M.I. Jordan, and T. Petsche eds, MIT Press, pp 838-844 (1997).
abstract
full article (postscript)

full article (pdf)

On the Dimensionality of Cortical Graphs
Elie Bienenstock, J. Physiol., Paris, 90: 251-256 (1996).
abstract
full article (postscript)

Composition
Elie Bienenstock, In: Brain Theory - Biological Basis and Computational Theory of Vision, Ad Aertsen and Valentino Braitenberg eds, Elsevier, pp 269-300 (1996).
abstract
full article (postscript)

Compositionality in Neural Systems
Elie Bienenstock and Stuart Geman, In: The Handbook of Brain Theory and Neural Networks, M. Arbib ed, Bradford Books/MIT Press, pp 223-226 (1995).
first paragraph
full article (postscript)

full article (pdf)

A Model of Neocortex
Elie Bienenstock, Network: Computation in Neural Systems, 6: 179-224 (1995)
abstract
full article (postscript)

full article (pdf)

Comment on: The Hebbian paradigm reintegrated: Local reverberations as internal representations by D.J. Amit
Elie Bienenstock and Stuart Geman, Behavioral and Brain Sciences, 18: 627--628 (1995).

A Shape-Recognition Model Using Dynamical Links
Elie Bienenstock and Rene Doursat, Network: Computation in Neural Systems, 5: 241--258 (1994).
abstract

Comment on: Neural Networks: A Review from a Statistical Perspective by B. Cheng and D.M. Titterington
Stuart Geman and Elie Bienenstock, Statistical Science, 9: 36--38 (1994).

Neural Networks and the Bias/Variance Dilemma
Stuart Geman, Elie Bienenstock and Rene Doursat, Neural Computation, 4: 1--58 (1992).

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Suggestions for a Neurobiological Approach to Syntax
Elie Bienenstock, In: Proceedings of Second Interdisciplinary Workshop on Compositionality in Cognition and Neural Networks, Abbaye de Royaumont, France, June 29--30, 1992, D. Andler, E. Bienenstock and B. Laks eds., pp. 13--21.

Cellular Analogs of Visual Cortical Epigenesis: 1. Plasticity of Orientation Selectivity
Yves Fregnac, Daniel Shulz, Simon Thorpe and Elie Bienenstock, The Journal of Neuroscience, 12 (4): 1280--1300 (1992).

Notes on the Growth of a Composition Machine
Elie Bienenstock, In: Proceedings of First Interdisciplinary Workshop on Compositionality in Cognition and Neural Networks, Abbaye de Royaumont, France, May 27--28, 1991, D. Andler, E. Bienenstock and B. Laks eds., pp. 25--43.

Visual pattern processing using a neural-network based approach
Elie Bienenstock, In: Les Sciences Cognitives en Debat, Editions du CNRS, Paris, Gerard Vergnaud ed., pp. 301--315 (1991)

Vision naturelle et vision artificielle
Elie Bienenstock, In: Les reseaux de neurones--Rapport de l'Observatoire Francais des Techniques Avancees, pp. 93--117, Masson, Paris (1991).

Issues of Representation in Neural Networks
Elie Bienenstock and Rene Doursat, In: Representations of Vision: Trends and Tacit Assumptions in Vision Research, A. Gorea, ed., pp. 47--67, Cambridge University Press (1991).

Spatio-Temporal Coding and the Compositionality of Cognition
Elie Bienenstock and Rene Doursat, Proceedings of Workshop on Temporal Correlations and Temporal Coding in the Brain, R. Lestienne, ed., pp. 42--47, Paris, April 25--27, 1990.

Hebbian Synapses and Visual Cortical Plasticity
Yves Fregnac, Elie Bienenstock and  Daniel Shulz, Proceedings of AFCET International Conference on Neural Networks, Lyon, (1990).

A Cursory Introduction to the Physicists' Neural Networks
Leon Personnaz,  Gerard Dreyfus and Elie Bienenstock, Journal de Physique, Paris, 50 C3: 207--208 (1989).

Elastic Matching and Pattern Recognition in Neural Networks
Elie Bienenstock and Rene Doursat, In: Neural Networks: From Models to Applications, L. Personnaz and G. Dreyfus eds., pp. 472--482, IDSET, Paris, (1989).

Relational Models In Natural and Artificial Vision
Elie Bienenstock, In: Neural Computers, R. Eckmiller and Ch. von der Malsburg eds., pp. 61--70, Springer-Verlag, Berlin (1988).

Neural-Like Graph-Matching Techniques for Image Processing
Elie Bienenstock, In:Organization of Neural Networks, W. von Seelen, G. Shaw, and U.M. Leinhos eds., pp. 211--235, VCH Verlagsgesellschaft, Weinheim, Germany (1988).

A Cellular Analogue of Visual Cortical Plasticity
Yves Fregnac, Dan Shulz, Simon Thorpe and Elie Bienenstock, Nature, 333: 367--370 (1988).

A Neural Network for Invariant Pattern Recognition
Christoph von der Malsburg and Elie Bienenstock, Europhysics Letters, 4: 121--126 (1987).

A Neural Network for the Retrieval of Superimposed Connection Patterns
Elie Bienenstock and Christoph von der Malsburg, Europhysics Letters, 3: 1243--1249 (1987).

Connectionist Approaches to Vision
Elie Bienenstock, Technical Report, Dept. of Developmental Neurobiology, Orsay University (1986).

Statistical Coding and Short-Term Synaptic Plasticity: A Scheme for Knowledge Representation in the Brain
Christoph von der Malsburg and Elie Bienenstock, In: Disordered Systems and Biological Organization, E. Bienenstock, F. Fogelman and G. Weisbuch eds., pp. 247--272, Springer-Verlag, Berlin (1986).

Disordered Systems and Biological Organization (Book)
Proceedings of a NATO Workshop at Les Houches, France, March 1985, Elie Bienenstock, Francoise Fogelman-Soulie and Gerard Weisbuch Eds., Springer-Verlag, Berlin (1986).

Dynamics of the Central Nervous System
Elie Bienenstock, In: Dynamics of Macrosystems, J.P. Aubin, D. Saari, and K. Sigmund, eds., Springer-Verlag, Berlin, pp. 3--20 (1985).

Une approche topologique de l'objet mental
Elie Bienenstock, In: Les theories de la complexite, F. Fogelman-Soulie, ed., Le Seuil, Paris, pp. 238--255 (1991).

Neurophysiological Models, Spin Models, and Pattern Formation
Michelle Schatzman and Elie Bienenstock, Technical Report PAM-240, Center for Pure and Applied Mathematics, University of California, Berkeley (1984).

Ionophoretic Clamp of Activity in Visual Cortical Neurons in the Cat: A Test of Hebb's Hypothesis
Yves Fregnac, Simon Thorpe and Elie Bienenstock, Proceedings of the Physiological Society, 21-23 July 1983, Journal of Physiology, 345: 123P (1983).

Cooperation and Competition in Central Nervous System Development: A Unifying Approach
Elie Bienenstock, In: Synergetics of the Brain, E. Basar, H. Flohr, H. Haken, and A.J. Mandell, eds., Springer-Verlag, Berlin, pp. 250--263 (1983).

A Theory for the Development of Neuron Selectivity: Orientation Specificity and Binocular Interaction in Visual Cortex
Elie Bienenstock, Leon N. Cooper, and P. Munro, The Journal of Neuroscience, 2: 32--48 (1982).

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Specific Functional Modifications of Individual Cortical Neurons triggered by Vision and Passive Eye Movement in Immobilized Kittens
Yves Fregnac and Eie Bienenstock, In: Doc. Ophthal. Proc. Series, L. Maffei, ed. W. Junk Publishers, The Hague,  30: 100--108 (1981).

Effect of Neonatal Unilateral Enucleation on the Development of Orientation Selectivity in the Primary Visual Cortex of Normally and Dark-reared Kittens
Yves Fregnac, Yves Trotter, Pierre Buisseret, Elyane Gary-Bobo, Elie Bienenstock and Michel Imbert,
Experimental Brain Research, 42: 453--466 (1981).

Sets of Degrees of Computable Fields
Elie Bienenstock, Israel Journal of Mathematics, 27: 348--356 (1977).

ABSTRACTS

Estimating the Entropy of Binary Time Series: Methodology, Some Theory and a Simulation Study

Yun Gao, Ioannis Kontoyiannis, and Elie Bienenstock, Entropy 10, no. 2: 71-99.

Partly motivated by entropy-estimation problems in neuroscience, we present a detailed and extensive comparison between some of the most popular and effective entropy estimation methods used in practice: The plug-in method, four different estimators based on the Lempel-Ziv (LZ) family of data compression algorithms, an estimator based on the Context-Tree Weighting (CTW) method, and the renewal entropy estimator. METHODOLOGY: Three new entropy estimators are introduced; two new LZ-based estimators, and the renewal entropy estimator, which is tailored to data generated by a binary renewal process. For two of the four LZ-based estimators, a bootstrap procedure is described for evaluating their standard error, and a practical rule of thumb is heuristically derived for selecting the values of their parameters in practice. THEORY: We prove that, unlike their earlier versions, the two new LZ-based estimators are universally consistent, that is, they converge to the entropy rate for every finite-valued, stationary and ergodic process. An effective method is derived for the accurate approximation of the entropy rate of a finite-state hidden Markov model (HMM) with known distribution. Heuristic calculations are presented and approximate formulas are derived for evaluating the bias and the standard error of each estimator. SIMULATION: All estimators are applied to a wide range of data generated by numerous different processes with varying degrees of dependence and memory. The main conclusions drawn from these experiments include: (i) For all estimators considered, the main source of error is the bias. (ii) The CTW method is repeatedly and consistently seen to provide the most accurate results. (iii) The performance of the LZ-based estimators is often comparable to that of the plug-in method. (iv) The main drawback of the plug-in method is its computational inefficiency; with small word-lengths it fails to detect longer-range structure in the data, and with longer word-lengths the empirical distribution is severely undersampled, leading to large biases. (pdf)

Bayesian Population Decoding of Motor Cortical Activity Using a Kalman Filter

Wei Wu, Yun Gao, Elie Bienenstock, John P. Donoghue, Michael J. Black, Neural Computation, Volume 18, Number 1, January 2006, pp. 80-118(39).

Effective neural motor prostheses require a method for decoding neural activity representing desired movement. In particular, the accurate reconstruction of a continuous motion signal is necessary for the control of devices such as computer cursors, robots, or a patient's own paralyzed limbs. For such applications, we developed a real-time system that uses Bayesian inference techniques to estimate hand motion from the firing rates of multiple neurons. In this study, we used recordings that were previously made in the arm area of primary motor cortex in awake behaving monkeys using a chronically implanted multielectrode microarray. Bayesian inference involves computing the posterior probability of the hand motion conditioned on a sequence of observed firing rates; this is formulated in terms of the product of a likelihood and a prior. The likelihood term models the probability of firing rates given a particular hand motion. We found that a linear gaussian model could be used to approximate this likelihood and could be readily learned from a small amount of training data. The prior term defines a probabilistic model of hand kinematics and was also taken to be a linear gaussian model. Decoding was performed using a Kalman filter, which gives an efficient recursive method for Bayesian inference when the likelihood and prior are linear and gaussian. In off-line experiments, the Kalman filter reconstructions of hand trajectory were more accurate than previously reported results. The resulting decoding algorithm provides a principled probabilistic model of motor-cortical coding, decodes hand motion in real time, provides an estimate of uncertainty, and is straightforward to implement. Additionally the formulation unifies and extends previous models of neural coding while providing insights into the motor-cortical code.

Encoding/decoding of arm kinematics from simultaneously recorded MI neurons

Gao, Y., Bienenstock, E., Black, M., Shoham, S., Serruya, M., and Donoghue, J.

Statistical learning and probabilistic inference methods were used to (i) investigate the nature of encoding in motor cortex, (ii) characterize probabilistic relationships between arm kinematics (hand position or velocity) and activity of a simultaneously recorded neural population, and (iii) optimally reconstruct (decode) hand trajectory from population activity. Data was obtained from simultaneous multiple electrode recordings of 25 neurons in the arm area of primary motor cortex (MI) while subjects manually tracked a smoothly and randomly moving visual target (Paninski et al., this volume). Statistical learning methods were used to derive optimal Bayesian estimates of the conditional probability of firing for each cell given the kinematic variables. Non-parametric models of conditional firing were learned using regularization (smoothing) techniques with cross-validation and suggest that the cells encode information about the position and velocity of the hand. Decoding involves the inference of the kinematics from the firing rate of the cells. The posterior probability distribution of the kinematics given the spike data was represented with discrete samples. Predictions of hand parameters were estimated in 50msec intervals using a Bayesian estimation method called particle filtering. Experiments with real and synthetic data suggested that this approach provides probabilistically sound estimates of kinematics and allows the probabilistic combination of information from multiple neurons, the use of priors, and the rigorous evaluation of models and results.

Regulated Criticality in the Brain?
Elie Bienenstock and Daniel Lehmann, Advances in Complex Systems, in press (1999).
Abstract: We propose that a regulation mechanism based on Hebbian covariance plasticity may cause the brain to operate near criticality. We analyze the effect of such a regulation on the dynamics of a network with excitatory and inhibitory neurons and uniform connectivity within and across the two populations. We show that, under broad conditions, the system converges to a critical state lying at the common boundary of three regions in parameter space; these correspond to three modes of behavior: high activity, low activity, oscillation.
full text (postscript)

On the Temporal Resolution of Neural Activity
Akira Date, Elie Bienenstock and Stuart Geman (1998).
Abstract: An important issue regarding brain function is the existence and role of fine temporal structure in neural activity. Multi-neuronal recording techniques are now available to study this issue. We present a simple statistical method devised to detect fine temporal structure in simultaneously recorded spike processes. We apply this method to data recorded from monkey Supplementary Motor Area, and show a preliminary result which suggests that the nervous system may indeed use a temporal resolution of about 10 ms (or higher).
full text (postscript)

Compositionality, MDL Priors, and Object Recognition
Elie Bienenstock, Stuart Geman, and Daniel Potter, In: Advances in Neural Information Processing Systems 9 , M.C. Mozer, M.I. Jordan, and T. Petsche eds, MIT Press, pp 838-844 (1997).
Abstract: Images are ambiguous at each of many levels of a contextual hierarchy. Nevertheless, the high-level interpretation of most scenes is unambiguous, as evidenced by the superior performance of humans. This observation argues for global vision models, such as deformable templates. Unfortunately, such models are computationally intractable for unconstrained problems. We propose a compositional model in which primitives are recursively composed, subject to syntactic restrictions, to form tree-structured objects and object groupings. Ambiguity is propagated up the hierarchy in the form of multiple interpretations, which are later resolved by a Bayesian, equivalently minimum-description-length, cost functional.
full text (postscript)

On the Dimensionality of Cortical Graphs
Elie Bienenstock, J. Physiol., Paris, 90: 251-256 (1996).
Abstract: We propose to use a random-graph model of cortex as a tabula-rasa state, to be contrasted with various types of regular connectivity patterns. Key in our analysis is the notion of graph-theoretic dimensionality, closely linked to that of graph diameter. Our discussion focuses on patterns of synfire type, and on the synfire-superposition model proposed in previous papers
full article (postscript)

Composition
Elie Bienenstock, In: Brain Theory - Biological Basis and Computational Theory of Vision, Ad Aertsen and Valentino Braitenberg eds, Elsevier, pp 269-300 (1996).
Abstract: This chapter is a composition on the theme of binding and composition. The binding problem---the investigation of the mechanisms used by the brain to bind with each other the representations of different parts or features of an object---has motivated a number of studies in recent years. In particular, attention has been given to the possible role of accurate temporal structure of multi-neuron spike trains in cortex (von der Malsburg 1981; Gray et al. 1989; Eckhorn et al. 1988). The present paper uses linguistic examples to suggest that the mental material that is operated upon by the establishment of dynamical bonds largely consists, in itself, of dynamical bonds. Further, using the construction game Lego as a metaphor for compositional symbol systems, we stress the role of cooperativity among elementary bonds. We use the term map to refer to a coherent system of bonds, i.e., a system where elementary bonds cooperate with each other. We propose to view the fundamental operation of mental composition as consisting of the doing and undoing of maps between maps... between maps. Cooperative binding in Lego can be characterized mathematically as the establishment of commutative mapping diagrams, and we outline an approach to language in which the construction of meaning consists of the establishment of recursively embedded commutative mapping diagrams. Drawing from these considerations, we suggest that the mechanism the brain uses to achieve recursive composition may consist of the cooperative binding of complex spatio-temporal patterns. Particularly interesting in this context is the synfire-chain model proposed by Abeles (1982) to explain the accurately timed events in cortex reported by him and his group. This model offers a simple and plausible example of a system of recursively bound spatio-temporal neural activity patterns. Moreover, in this model, the interpretation of cooperative binding as the establishment of commutative mapping diagrams is straightforward, and consistent with the generally accepted principle of Hebbian plasticity.
full text (postscript)

Compositionality in Neural Systems
Elie Bienenstock and Stuart Geman, In: The Handbook of Brain Theory and Neural Networks, M. Arbib ed, Bradford Books/MIT Press, pp 223-226 (1995).
Abstract: Compositionality refers to our ability to construct mental representations, hierarchically, in terms of parts and their relations. The ``rules" of composition are such that (i) we have at our disposal an infinite repertoire of hierarchically constructed entities built from relatively small numbers of lower-level constituents---a phenomenon sometimes referred to as (infinite) productivity---and (ii) allowable constructions nevertheless respect specific constraints, whereby overwhelmingly most combinations are made meaningless.
full text (postscript)

A Model of Neocortex
Elie Bienenstock, Network: Computation in Neural Systems, 6: 179-224 (1995)
Abstract: Prompted by considerations about (i) the compositionality of cognitive functions; (ii) the physiology of individual cortical neurons; (iii) the role of accurately-timed spike patterns in cortex; (iv) the regulation of global cortical activity, we suggest that the dynamics of cortex on the 1-ms time scale may be described as the activation of circuits of the synfire-chain type (Abeles 1982, 1991). We suggest that the fundamental computational unit in cortex may be a wave-like spatio-temporal pattern of synfire type, and that the binding mechanism underlying compositionality in cognition may be the accurate synchronization of synfire waves that propagate simultaneously on distinct, weakly-coupled, synfire chains. We propose that Hebbian synaptic plasticity may result in a superposition of synfire chains in cortical connectivity, whereby a given neuron participates in many distinct chains. We investigate the behavior of a much-simplified model of cortical dynamics devised along these principles. Calculations and numerical experiments are performed based on an assumption of randomness of stored chains, in the style of statistical physics. It is demonstrated that: (i) there exists a critical value for the total length of stored chains; (ii) this storage capacity is linear in the network's size; (iii) the behavior of the network around the critical point is characterized by the self-regulation of the number of synfire waves coactive in the network at any given time.
full text (postscript)

A Shape-Recognition Model Using Dynamical Links,
Elie Bienenstock and Rene Doursat, Network: Computation in Neural Systems, 5: 241--258 (1994).
Abstract: A shape-recognition method is proposed, inspired from the dynamic-link theory of von der Malsburg (1981). The quality of a match between two images is assessed through an elastic cost functional; the minimal value reached by the cost over a suitably-defined space of maps is viewed as a distance between these two images. Experiments on nearest-neighbor classification of handwritten numerals are presented, using a computationally-effective procedure for finding a reliable estimate of the matching distance.