The Experts below are selected from a list of 648 Experts worldwide ranked by ideXlab platform
Peter Dayan - One of the best experts on this subject based on the ideXlab platform.
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NIPS - Two is better than one: distinct roles for familiarity and recollection in retrieving palimpsest memories
2011Co-Authors: Cristina Savin, Peter Dayan, Mate LengyelAbstract:Storing a new pattern in a palimpsest Memory system comes at the cost of interfering with the Memory traces of previously stored items. Knowing the age of a pattern thus becomes critical for recalling it faithfully. This implies that there should be a tight coupling between estimates of age, as a form of familiarity, and the neural dynamics of recollection, something which current theories omit. Using a normative model of Autoassociative Memory, we show that a dual Memory system, consisting of two interacting modules for familiarity and recollection, has best performance for both recollection and recognition. This finding provides a new window onto actively contentious psychological and neural aspects of recognition Memory.
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Matching storage and recall: hippocampal spike timing–dependent plasticity and phase response curves
Nature Neuroscience, 2005Co-Authors: Mate Lengyel, Jeehyun Kwag, Ole Paulsen, Peter DayanAbstract:Hippocampal area CA3 is widely considered to function as an Autoassociative Memory. However, it is insufficiently understood how it does so. In particular, the extensive experimental evidence for the importance of carefully regulated spiking times poses the question as to how spike timing–based dynamics may support Memory functions. Here, we develop a normative theory of Autoassociative Memory encompassing such network dynamics. Our theory specifies the way that the synaptic plasticity rule of a Memory constrains the form of neuronal interactions that will retrieve memories optimally. If memories are stored by spike timing–dependent plasticity, neuronal interactions should be formalized in terms of a phase response curve, indicating the effect of presynaptic spikes on the timing of postsynaptic spikes. We show through simulation that such memories are competent analog autoassociators and demonstrate directly that the attributes of phase response curves of CA3 pyramidal cells recorded in vitro qualitatively conform with the theory.
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rate and phase coded Autoassociative Memory
Neural Information Processing Systems, 2004Co-Authors: Mate Lengyel, Peter DayanAbstract:Areas of the brain involved in various forms of Memory exhibit patterns of neural activity quite unlike those in canonical computational models. We show how to use well-founded Bayesian probabilistic Autoassociative recall to derive biologically reasonable neuronal dynamics in recurrently coupled models, together with appropriate values for parameters such as the membrane time constant and inhibition. We explicitly treat two cases. One arises from a standard Hebbian learning rule, and involves activity patterns that are coded by graded firing rates. The other arises from a spike timing dependent learning rule, and involves patterns coded by the phase of spike times relative to a coherent local field potential oscillation. Our model offers a new and more complete understanding of how neural dynamics may support autoassociation.
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NIPS - Rate- and Phase-coded Autoassociative Memory
2004Co-Authors: Mate Lengyel, Peter DayanAbstract:Areas of the brain involved in various forms of Memory exhibit patterns of neural activity quite unlike those in canonical computational models. We show how to use well-founded Bayesian probabilistic Autoassociative recall to derive biologically reasonable neuronal dynamics in recurrently coupled models, together with appropriate values for parameters such as the membrane time constant and inhibition. We explicitly treat two cases. One arises from a standard Hebbian learning rule, and involves activity patterns that are coded by graded firing rates. The other arises from a spike timing dependent learning rule, and involves patterns coded by the phase of spike times relative to a coherent local field potential oscillation. Our model offers a new and more complete understanding of how neural dynamics may support autoassociation.
Alessandro Treves - One of the best experts on this subject based on the ideXlab platform.
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the ca3 network as a Memory store for spatial representations
Learning & Memory, 2007Co-Authors: Gergely Papp, Menno P Witter, Alessandro TrevesAbstract:Comparative neuroanatomy suggests that the CA3 region of the mammalian hippocampus is directly homologous with the medio-dorsal pallium in birds and reptiles, with which it largely shares the basic organization of primitive cortex. Autoassociative Memory models, which are generically applicable to cortical networks, then help assess how well CA3 may process information and what the crucial hurdles are that it may face. The analysis of such models points at spatial memories as posing a special challenge, both in terms of the attractor dynamics they can induce and how they may be established. Addressing such a challenge may have favored the evolution of elements of hippocampal organization observed only in mammals.
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Autoassociative Memory retrieval and spontaneous activity bumps in small-world networks of integrate-and-fire neurons.
Journal of physiology Paris, 2006Co-Authors: Anastasia Anishchenko, Alessandro TrevesAbstract:The metric structure of synaptic connections is obviously an important factor in shaping the properties of neural networks, in particular the capacity to retrieve memories, with which are endowed Autoassociative nets operating via attractor dynamics. Qualitatively, some real networks in the brain could be characterized as 'small worlds', in the sense that the structure of their connections is intermediate between the extremes of an orderly geometric arrangement and of a geometry-independent random mesh. Small worlds can be defined more precisely in terms of their mean path length and clustering coefficient; but is such a precise description useful for a better understanding of how the type of connectivity affects Memory retrieval? We have simulated an Autoassociative Memory network of integrate-and-fire units, positioned on a ring, with the network connectivity varied parametrically between ordered and random. We find that the network retrieves previously stored Memory patterns when the connectivity is close to random, and displays the characteristic behavior of ordered nets (localized 'bumps' of activity) when the connectivity is close to ordered. Recent analytical work shows that these two behaviors can coexist in a network of simple threshold-linear units, leading to localized retrieval states. We find that they tend to be mutually exclusive behaviors, however, with our integrate-and-fire units. Moreover, the transition between the two occurs for values of the connectivity parameter which are not simply related to the notion of small worlds.
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Autoassociative Memory Retrieval and Spontaneous Activity Bumps in Small-World Networks of Integrate-and-Fire Neurons
arXiv: Neurons and Cognition, 2005Co-Authors: Anastasia Anishchenko, Elie Bienenstock, Alessandro TrevesAbstract:Qualitatively, some real networks in the brain could be characterized as 'small worlds', in the sense that the structure of their connections is intermediate between the extremes of an orderly geometric arrangement and of a geometry-independent random mesh. Small worlds can be defined more precisely in terms of their mean path length and clustering coefficient; but is such a precise description useful to better understand how the type of connectivity affects Memory retrieval? We have simulated an Autoassociative Memory network of integrate-and-fire units, positioned on a ring, with the network connectivity varied parametrically between ordered and random. We find that the network retrieves when the connectivity is close to random, and displays the characteristic behavior of ordered nets (localized 'bumps' of activity) when the connectivity is close to ordered. Recent analytical work shows that these two behaviours can coexist in a network of simple threshold-linear units, leading to localized retrieval states. We find that they tend to be mutually exclusive behaviours, however, with our integrate-and-fire units. Moreover, the transition between the two occurs for values of the connectivity parameter which are not simply related to the notion of small worlds.
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The Autoassociative Hypothesis Places Constraints on Hippocampal Organization
ICANN ’93, 1993Co-Authors: Alessandro Treves, Edmund T. RollsAbstract:We consider the theory that the hippocampus operates as an intermediate term buffer store during consolidation of long-term Memory in neocortical areas, and that the crucial role in such operation is played by the CA3 region, which acts as an Autoassociative Memory network. We extend here previous work, which suggested ways in which the theory placed constraints on the organization of the CA3 region, by indicating how it could also imply constraints informing the organization of other hippocampal regions, as well as of hippocampal return projections to the neocortex.
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Short- and long-range connections in Autoassociative Memory
Journal of Physics A: Mathematical and General, 1992Co-Authors: Dominic O'kane, Alessandro TrevesAbstract:The authors consider Memory retrieval in a network of M modules. A module consists of N neuronal units, each of which is connected to all N-1 other units within the same module, and to L units distributed randomly throughout all the other modules. Both short- and long-range connections are symmetric. The units are threshold-linear with a continuous positive output. Each module can retrieve one of D local activity patterns, or 'features', stored on the corresponding short-range connections. Furthermore, P global activity patterns, each consisting of combinations of M local features, are stored on the dilute long-range connections. When M>>1 the long-range connections endow the network with attractor states correlated with a single global pattern, and they study its storage capacity within a mean-field approach. If P=D, and each feature appears in only one pattern, their model reduces to an intermediate case between fully connected and highly dilute architectures, whose capacities they recover in the appropriate limits. As P/D takes larger (integer) values, the maximum P grows, but it remains asymptotically proportional to N rather than to L+N-1 (the total number of connections per unit). The maximum amount of retrievable information per synapse, on the other hand, decreases. Moreover, as P/D grows, retrieval attractors have to compete with a 'Memory glass' state, involving the retrieval of spurious combinations of features, whose existence and stability they describe analytically. They suggest implications for neocortical Memory functions.
Mate Lengyel - One of the best experts on this subject based on the ideXlab platform.
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NIPS - Two is better than one: distinct roles for familiarity and recollection in retrieving palimpsest memories
2011Co-Authors: Cristina Savin, Peter Dayan, Mate LengyelAbstract:Storing a new pattern in a palimpsest Memory system comes at the cost of interfering with the Memory traces of previously stored items. Knowing the age of a pattern thus becomes critical for recalling it faithfully. This implies that there should be a tight coupling between estimates of age, as a form of familiarity, and the neural dynamics of recollection, something which current theories omit. Using a normative model of Autoassociative Memory, we show that a dual Memory system, consisting of two interacting modules for familiarity and recollection, has best performance for both recollection and recognition. This finding provides a new window onto actively contentious psychological and neural aspects of recognition Memory.
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Matching storage and recall: hippocampal spike timing–dependent plasticity and phase response curves
Nature Neuroscience, 2005Co-Authors: Mate Lengyel, Jeehyun Kwag, Ole Paulsen, Peter DayanAbstract:Hippocampal area CA3 is widely considered to function as an Autoassociative Memory. However, it is insufficiently understood how it does so. In particular, the extensive experimental evidence for the importance of carefully regulated spiking times poses the question as to how spike timing–based dynamics may support Memory functions. Here, we develop a normative theory of Autoassociative Memory encompassing such network dynamics. Our theory specifies the way that the synaptic plasticity rule of a Memory constrains the form of neuronal interactions that will retrieve memories optimally. If memories are stored by spike timing–dependent plasticity, neuronal interactions should be formalized in terms of a phase response curve, indicating the effect of presynaptic spikes on the timing of postsynaptic spikes. We show through simulation that such memories are competent analog autoassociators and demonstrate directly that the attributes of phase response curves of CA3 pyramidal cells recorded in vitro qualitatively conform with the theory.
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rate and phase coded Autoassociative Memory
Neural Information Processing Systems, 2004Co-Authors: Mate Lengyel, Peter DayanAbstract:Areas of the brain involved in various forms of Memory exhibit patterns of neural activity quite unlike those in canonical computational models. We show how to use well-founded Bayesian probabilistic Autoassociative recall to derive biologically reasonable neuronal dynamics in recurrently coupled models, together with appropriate values for parameters such as the membrane time constant and inhibition. We explicitly treat two cases. One arises from a standard Hebbian learning rule, and involves activity patterns that are coded by graded firing rates. The other arises from a spike timing dependent learning rule, and involves patterns coded by the phase of spike times relative to a coherent local field potential oscillation. Our model offers a new and more complete understanding of how neural dynamics may support autoassociation.
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NIPS - Rate- and Phase-coded Autoassociative Memory
2004Co-Authors: Mate Lengyel, Peter DayanAbstract:Areas of the brain involved in various forms of Memory exhibit patterns of neural activity quite unlike those in canonical computational models. We show how to use well-founded Bayesian probabilistic Autoassociative recall to derive biologically reasonable neuronal dynamics in recurrently coupled models, together with appropriate values for parameters such as the membrane time constant and inhibition. We explicitly treat two cases. One arises from a standard Hebbian learning rule, and involves activity patterns that are coded by graded firing rates. The other arises from a spike timing dependent learning rule, and involves patterns coded by the phase of spike times relative to a coherent local field potential oscillation. Our model offers a new and more complete understanding of how neural dynamics may support autoassociation.
Marcos Eduardo Valle - One of the best experts on this subject based on the ideXlab platform.
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a fast and robust max c projection fuzzy Autoassociative Memory with application for face recognition
Brazilian Conference on Intelligent Systems, 2017Co-Authors: Alex Santana Dos Santos, Marcos Eduardo ValleAbstract:Max-C projection Autoassociative fuzzy memories (max-C PAFMs) are Memory models designed for the storage and recall of fuzzy sets. In few words, a max-C PAFM projects the input fuzzy set into the family of all max-C combinations of the stored items. In this paper, we focus on a particular max-C PAFM called Zadeh max-C PAFM. The Zadeh max-C PAFM is the most robust max-C PAFM with respect to dilative noise. Furthermore, by masking the noise contained in a corrupted input, it exhibits excellent tolerance to any kind of noise. Besides introducing the Zadeh max-C PAFM, in this paper we point out a potential application of the Zadeh max-C PAFM for face recognition.
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On subspace projection Autoassociative memories based on linear support vector regression
2015 Latin America Congress on Computational Intelligence (LA-CCI), 2015Co-Authors: Marcos Eduardo Valle, Emely Pujolli Da SilvaAbstract:Autossociative memories (AMs) are models inspired by the human brain ability to store and recall information. They should be able to retrieve a stored information upon presentation of a partial or corrupted item. An AM that projects the input onto a linear subspace is called subspace projection Autoassociative Memory (SPAM). The recall phase of a SPAM model is equivalent to a multi-linear regression problem. In particular, the optimal linear Autoassociative Memory (OLAM) corresponds to the SPAM model obtained by considering traditional least squares regression in the recall phase. In this paper, we present a novel class of SPAM models obtained by considering linear support vector regression (SVR). Precisely, we introduce three SPAM models based on primal, dual, and bi-level formulations of the linear e-support vector regression. A simple example is used throughout the paper to illustrate the noise tolerance of the proposed Memory models.
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A Robust Subspace Projection Autoassociative Memory Based on the M-Estimation Method
IEEE Transactions on Neural Networks and Learning Systems, 2014Co-Authors: Marcos Eduardo ValleAbstract:An Autoassociative Memory (AM) that projects an input pattern onto a linear subspace is referred to as a subspace projection AM (SPAM). The optimal linear AM (OLAM), which can be used for the storage and recall of real-valued patterns, is an example of SPAM. In this brief we introduce a novel SPAM model based on the robust M-estimation method. In contrast to the OLAM and many other associative Memory models, the robust SPAM represents a neural network in which the synaptic weights are iteratively adjusted during the retrieval phase. Computational experiments concerning the reconstruction of corrupted gray-scale images reveal that the novel memories exhibit an excellent tolerance with respect to salt and pepper noise as well as some tolerance with respect to Gaussian noise and blurred input images.
Ole Paulsen - One of the best experts on this subject based on the ideXlab platform.
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Matching storage and recall: hippocampal spike timing–dependent plasticity and phase response curves
Nature Neuroscience, 2005Co-Authors: Mate Lengyel, Jeehyun Kwag, Ole Paulsen, Peter DayanAbstract:Hippocampal area CA3 is widely considered to function as an Autoassociative Memory. However, it is insufficiently understood how it does so. In particular, the extensive experimental evidence for the importance of carefully regulated spiking times poses the question as to how spike timing–based dynamics may support Memory functions. Here, we develop a normative theory of Autoassociative Memory encompassing such network dynamics. Our theory specifies the way that the synaptic plasticity rule of a Memory constrains the form of neuronal interactions that will retrieve memories optimally. If memories are stored by spike timing–dependent plasticity, neuronal interactions should be formalized in terms of a phase response curve, indicating the effect of presynaptic spikes on the timing of postsynaptic spikes. We show through simulation that such memories are competent analog autoassociators and demonstrate directly that the attributes of phase response curves of CA3 pyramidal cells recorded in vitro qualitatively conform with the theory.
