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Publications of Elie Bienenstock
Estimating
the Entropy of Binary Time Series: Methodology, Some Theory and a Simulation
Study
Yun Gao, Ioannis Kontoyiannis, and Elie Bienenstock, (2008) Preprint.
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.
Single-trial prediction of discrete hand
movements with electroencephalography
Jerome N. Sanes, Timothy O'Keefe, Richard Archibald, Elie Bienenstock, Abstract, Human Brain Mapping 2006.
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.
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.
(pdf)
Estimating the Entropy Rate of Spike Trains
Yun Gao, Ioannis Kontoyiannis and Elie
Bienenstock, preprint, February 2004.
Matthew Harrison, Stuart Geman and Elie
Bienenstock, Preprint, February 2004.
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,
(pdf)
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,
(pdf)
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
(pdf)
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.
(pdf)
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.
(pdf)
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).
(pdf)
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.
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)
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)
On the Dimensionality of Cortical
Graphs
Elie Bienenstock, J. Physiol.,
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)
A Model of Neocortex
Elie Bienenstock, Network: Computation in Neural Systems, 6: 179-224
(1995)
abstract
full article
(postscript)
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 René 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 René Doursat, Neural Computation, 4:
1--58 (1992).
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 Frégnac, 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,
Visual pattern processing using a
neural-network based approach
Elie Bienenstock, In: Les Sciences Cognitives en Debat, Editions du
CNRS, Paris, Gérard 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 Avancées, pp. 93--117, Masson,
Issues of Representation in
Neural Networks
Elie Bienenstock and René Doursat, In: Representations of Vision: Trends and
Tacit Assumptions in Vision Research, A. Gorea, ed., pp. 47--67,
Spatio-Temporal Coding and the
Compositionality of Cognition
Elie Bienenstock and René 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 Frégnac, Elie Bienenstock and Daniel Shulz, Proceedings of AFCET
International Conference on Neural Networks,
A Cursory Introduction to the
Physicists' Neural Networks
Léon Personnaz, Gérard Dreyfus and Elie Bienenstock, Journal de
Physique, Paris, 50 C3: 207--208 (1989).
Elastic Matching and Pattern
Recognition in Neural Networks
Elie Bienenstock and René 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 Frégnac, 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,
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,
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,
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 Frégnac, 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
Specific Functional Modifications
of Individual Cortical Neurons triggered by Vision and Passive Eye Movement in
Immobilized Kittens
Yves Frégnac 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 Frégnac, 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
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.