This paper presents some techniques for analogical mapping using associative-projective neural networks (APNNs). sparse binary distributed representations of constant high dimensionality are constructed on-the-fly for...
详细信息
This paper presents some techniques for analogical mapping using associative-projective neural networks (APNNs). sparse binary distributed representations of constant high dimensionality are constructed on-the-fly for hierarchical structures of various complexity. Such representations encode both surface and structural similarity of analogical episodes. The introduced mapping approaches are illustrated using test analogies.
Recent non-orthogonal models of simple cells (OF;Nature 381 (1996) 607) are based on the assumption that having desirable statistical properties (e.g., kurtosis) implies physiological plausibility. Orthogonal models w...
详细信息
Recent non-orthogonal models of simple cells (OF;Nature 381 (1996) 607) are based on the assumption that having desirable statistical properties (e.g., kurtosis) implies physiological plausibility. Orthogonal models which do not have certain statistical properties (e.g., equal variance distribution) have been dismissed without being tested under the same stimulus conditions as OF (localised segments of whitened natural scenes). In this paper, the statistical properties of OF are examined with respect to two alternatives: principal components analysis (PCA), which is the most parsimonious model, and independent components analysis (ICA), which directly optimises basis functions for kurtosis. After simulation on two different sets of whitened natural scenes (trees/plants with edges and short line segments and landscapes with less fine structure), it was found that the distribution of variance for trees/plants was similar for all three models. However, both ICA and PCA distributed variance more evenly than OF for landscapes, indicating an OF deficit in processing fine structure. Although OF was consistently more kurtotic than either ICA or PCA, OF must be substantially over-estimating the selectivity of its basis functions to process natural scenes, since ICA optimises this directly. These results demonstrate that (a) orthogonality is sometimes more appropriate for modelling neural responses than non-orthogonality;(b) OF and ICA are not formally equivalent;and (c) that desirable statistical distributions are highly sensitive to edges and short line segments. Are these distributions, therefore, the best criteria by which to judge the physiological plausibility of models of the visual cortex? Crown Copyright (C) 2003 Published by Elsevier B.V. All rights reserved.
Recently, different models of the statistical structure of natural images (and sequences) have been proposed. Maximizing sparseness, or alternatively temporal coherence of linear filter outputs leads to the emergence ...
详细信息
Recently, different models of the statistical structure of natural images (and sequences) have been proposed. Maximizing sparseness, or alternatively temporal coherence of linear filter outputs leads to the emergence of simple cell properties. Taking account of the basic dependencies of linear filter outputs enables modelling of complex cell and topographic properties as well. Here, we propose a unifying framework for all these statistical properties, based on the concept of spatiotemporal activity bubbles. (C) 2004 Elsevier B.V. All rights reserved.
This paper addresses two different coding strategies in the framework of linear generative models. One relies on statistical independence between encoding variables and the other imposes non-negativity constraints in ...
详细信息
This paper presents a new technique for achieving blind signal separation when given only a single channel recording. The main concept is based on exploiting a prior sets of time-domain basis functions learned by inde...
详细信息
This paper presents a new technique for achieving blind signal separation when given only a single channel recording. The main concept is based on exploiting a prior sets of time-domain basis functions learned by independent component analysis (ICA) to the separation of mixed source signals observed in a single channel. The inherent time structure of sound sources is reflected in the ICA basis functions, which encode the sources in a statistically efficient manner. We derive a learning algorithm using a maximum likelihood approach given the observed single channel data and sets of basis functions. For each time point we infer the source parameters and their contribution factors. This inference is possible due to prior knowledge of the basis functions and the associated coefficient densities. A flexible model for density estimation allows accurate modeling of the observation and our experimental results exhibit a high level of separation performance for simulated mixtures as well as real environment recordings employing mixtures of two different sources.
This paper focuses on the calculation of boundary values for the design parameters in the fan- out phase of the olfactory system of insects. Three main criteria are taken into account to determine the boundaries of th...
详细信息
This paper focuses on the calculation of boundary values for the design parameters in the fan- out phase of the olfactory system of insects. Three main criteria are taken into account to determine the boundaries of the parameters: (i) information conservation, (ii) low energy costs and (iii) full involvement of all the neurons. These criteria serve to determine the structural parameters that produce a sufficient minimal response. Analytical calculations lead to a few general expressions which show how the main internal parameters can be obtained for any system with similar characteristics. We calculate the optimal threshold for coincidence detection, connectivity and output activity values that verify criteria ( i), ( ii) and ( iii). The range of parameter values obtained by these calculations include those observed in the olfactory system of the locust.
Sensory information is represented in a spatio-temporal code in the antennal lobe, the first processing stage of the olfactory system of insects. We propose a novel mechanism for decoding this information in the next ...
详细信息
Sensory information is represented in a spatio-temporal code in the antennal lobe, the first processing stage of the olfactory system of insects. We propose a novel mechanism for decoding this information in the next processing stage, the mushroom body. The Kenyon cells in the mushroom body of insects exhibit lateral excitatory connections at their axons. We demonstrate that slow lateral excitation between Kenyon cells allows one to decode sequences of activity in the antennal lobe. We are thus able to clarify the role of the existing connections as well as to demonstrate a novel mechanism for decoding temporal information in neuronal systems. This mechanism complements the variety of existing temporal decoding schemes. It seems that neuronal systems not only have a rich variety of code types but also quite a diversity of algorithms for transforming different codes into each other.
Infomax means maximization of information flow in a neural system. A nonlinear version of infomax has been shown to be connected to independent component analysis and the receptive fields of neurons in the visual cort...
详细信息
Infomax means maximization of information flow in a neural system. A nonlinear version of infomax has been shown to be connected to independent component analysis and the receptive fields of neurons in the visual cortex. Here we show a problem of nonrobustness of nonlinear infomax: it is very sensitive to the choice the nonlinear neuronal transfer function. We consider an alternative approach in which the system is linear. but the noise level depends on the mean of the signal, as in a Poisson neuron model. This gives similar predictions as the nonlinear infomax, but seems to be more robust. (C) 2002 Published by Elsevier Science B.V.
Data implying that neurons can communicate with synchronous volleys are difficult to reconcile with the bulk of single unit recordings which do not show synchrony yet reveal substantial correlations with animal behavi...
详细信息
Data implying that neurons can communicate with synchronous volleys are difficult to reconcile with the bulk of single unit recordings which do not show synchrony yet reveal substantial correlations with animal behavior. Our simulations reconcile these two sets of results by sharing a synchronous signal among groups of neurons in a way that the average signal through any particular neuron exhibits conventional receptive field properties. The simulation models a subset of the connections between the LGN and VI and shows that synchronous computation at a high firing rate can appear at an individual cell as random spikes at a lower rate. (C) 2002 Elsevier Science B.V. All rights reserved.
暂无评论