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1.
. We present a class a neural networks algorithms based on simple hebbian learning which allow the finding of higher order
structure in data. The neural networks use negative feedback of activation to self-organise; such networks have previously
been shown to be capable of performing principal component analysis (PCA). In this paper, this is extended to exploratory
projection pursuit (EPP), which is a statistical method for investigating structure in high-dimensional data sets. As opposed
to previous proposals for networks which learn using hebbian learning, no explicit weight normalisation, decay or weight clipping
is required. The results are extended to multiple units and related to both the statistical literature on EPP and the neural
network literature on non-linear PCA.
Received: 30 May 1994/Accepted in revised form: 18 November 1994 相似文献
2.
We obtain bounds for the capacity of some multi-layer networks of linear threshold units. In the case of a network having n inputs, a single layer of h hidden units and an output layer of s units, where all the weights in the network are variable and shn, the capacity m satisfies 2nmnt logt, where t=1=h/s. We consider in more detail the case where there is a single output that is a fixed boolean function of the hidden units. In this case our upper bound is of order nh logh but the argument which provided the lower bound of 2n no longer applies. However, by explicit computation in low dimensional cases we show that the capacity exceeds 2n but is substantially less than the upper bound. Finally, we describe a learning algorithm for multi-layer networks with a single output unit. This greatly outperforms back propagation at the task of learning random vectors and provides further empirical evidence that the lower bound of 2n can be exceeded. 相似文献
3.
A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to alleviate the problem of catastrophic forgetting. 相似文献
4.
It has been shown that dynamic recurrent neural networks are successful in identifying the complex mapping relationship between
full-wave-rectified electromyographic (EMG) signals and limb trajectories during complex movements. These connectionist models
include two types of adaptive parameters: the interconnection weights between the units and the time constants associated
to each neuron-like unit; they are governed by continuous-time equations. Due to their internal structure, these models are
particularly appropriate to solve dynamical tasks (with time-varying input and output signals). We show in this paper that
the introduction of a modular organization dedicated to different aspects of the dynamical mapping including privileged communication
channels can refine the architecture of these recurrent networks. We first divide the initial individual network into two
communicating subnetworks. These two modules receive the same EMG signals as input but are involved in different identification
tasks related to position and acceleration. We then show that the introduction of an artificial distance in the model (using
a Gaussian modulation factor of weights) induces a reduced modular architecture based on a self-elimination of null synaptic
weights. Moreover, this self-selected reduced model based on two subnetworks performs the identification task better than
the original single network while using fewer free parameters (better learning curve and better identification quality). We
also show that this modular network exhibits several features that can be considered as biologically plausible after the learning
process: self-selection of a specific inhibitory communicating path between both subnetworks after the learning process, appearance
of tonic and phasic neurons, and coherent distribution of the values of the time constants within each subnetwork.
Received: 17 September 2001 / Accepted in revised form: 15 January 2002 相似文献
5.
Bauch CT 《Journal of mathematical biology》2002,45(5):375-395
We develop a moment closure approximation (MCA) to a network model of sexually transmitted disease (STD) spread through a
steady/casual partnership network. MCA has been used previously to approximate static, regular lattices, whereas application
to dynamic, irregular networks is a new endeavour, and application to sociologically-motivated network models has not been
attempted. Our goals are 1) to investigate issues relating to the application of moment closure approximations to dynamic
and irregular networks, and 2) to understand the impact of concurrent casual partnerships on STD transmission through a population
of predominantly steady monogamous partnerships. We are able to derive a moment closure approximation for a dynamic irregular
network representing sexual partnership dynamics, however, we are forced to use a triple approximation due to the large error
of the standard pair approximation. This example underscores the importance of doing error analysis for moment closure approximations.
We also find that a small number of casual partnerships drastically increases the prevalence and rate of spread of the epidemic.
Finally, although the approximation is derived for a specific network model, we can recover approximations to a broad range
of network models simply by varying model parameters which control the structure of the dynamic network. Thus our moment closure
approximation is very flexible in the kinds of network models it can approximate.
Received: 26 August 2001 / Revised version: 15 March 2002 / Published online: 23 August 2002
C.T.B. was supported by the NSF.
Key words or phrases: Moment closure approximation – Network model – Pair approximation – Sexually transmitted diseases – Steady/casual partnership
network 相似文献
6.
We describe and analyze a model for a stochastic pulse-coupled neuronal network with many sources of randomness: random external
input, potential synaptic failure, and random connectivity topologies. We show that different classes of network topologies
give rise to qualitatively different types of synchrony: uniform (Erdős–Rényi) and “small-world” networks give rise to synchronization
phenomena similar to that in “all-to-all” networks (in which there is a sharp onset of synchrony as coupling is increased);
in contrast, in “scale-free” networks the dependence of synchrony on coupling strength is smoother. Moreover, we show that
in the uniform and small-world cases, the fine details of the network are not important in determining the synchronization
properties; this depends only on the mean connectivity. In contrast, for scale-free networks, the dynamics are significantly
affected by the fine details of the network; in particular, they are significantly affected by the local neighborhoods of
the “hubs” in the network. 相似文献
7.
8.
Humans are able to form internal representations of the information they process—a capability which enables them to perform many different memory tasks. Therefore, the neural system has to learn somehow to represent aspects of the environmental situation; this process is assumed to be based on synaptic changes. The situations to be represented are various as for example different types of static patterns but also dynamic scenes. How are neural networks consisting of mutually connected neurons capable of performing such tasks? Here we propose a new neuronal structure for artificial neurons. This structure allows one to disentangle the dynamics of the recurrent connectivity from the dynamics induced by synaptic changes due to the learning processes. The error signal is computed locally within the individual neuron. Thus, online learning is possible without any additional structures. Recurrent neural networks equipped with these computational units cope with different memory tasks. Examples illustrate how information is extracted from environmental situations comprising fixed patterns to produce sustained activity and to deal with simple algebraic relations. 相似文献
9.
In this paper, we study the combined dynamics of the neural activity and the synaptic efficiency changes in a fully connected
network of biologically realistic neurons with simple synaptic plasticity dynamics including both potentiation and depression.
Using a mean-field of technique, we analyzed the equilibrium states of neural networks with dynamic synaptic connections and
found a class of bistable networks. For this class of networks, one of the stable equilibrium states shows strong connectivity
and coherent responses to external input. In the other stable equilibrium, the network is loosely connected and responds non
coherently to external input. Transitions between the two states can be achieved by positively or negatively correlated external
inputs. Such networks can therefore switch between their phases according to the statistical properties of the external input.
Non-coherent input can only “rcad” the state of the network, while a correlated one can change its state. We speculate that
this property, specific for plastic neural networks, can give a clue to understand fully unsupervised learning models.
Received: 8 August 1999 / Accepted in revised form: 16 March 2000 相似文献
10.
Temporal correlation of neuronal activity has been suggested as a criterion for multiple object recognition. In this work,
a two-dimensional network of simplified Wilson-Cowan oscillators is used to manage the binding and segmentation problem of
a visual scene according to the connectedness Gestalt criterion. Binding is achieved via original coupling terms that link
excitatory units to both excitatory and inhibitory units of adjacent neurons. These local coupling terms are time independent,
i.e., they do not require Hebbian learning during the simulations. Segmentation is realized by a two-layer processing of the
visual image. The first layer extracts all object contours from the image by means of “retinal cells” with an “on-center”
receptive field. Information on contour is used to selectively inhibit Wilson-Cowan oscillators in the second layer, thus
realizing a strong separation among neurons in different objects. Accidental synchronism between oscillations in different
objects is prevented with the use of a global inhibitor, i.e., a global neuron that computes the overall activity in the Wilson-Cowan
network and sends back an inhibitory signal.
Simulations performed in a 50×50 neural grid with 21 different visual scenes (containing up to eight objects + background)
with random initial conditions demonstrate that the network can correctly segment objects in almost 100% of cases using a
single set of parameters, i.e., without the need to adjust parameters from one visual scene to the next. The network is robust
with reference to dynamical noise superimposed on oscillatory neurons. Moreover, the network can segment both black objects
on white background and vice versa and is able to deal with the problem of “fragmentation.”
The main limitation of the network is its sensitivity to static noise superimposed on the objects. Overcoming this problem
requires implementation of more robust mechanisms for contour enhancement in the first layer in agreement with mechanisms
actually realized in the visual cortex.
Received: 25 October 2001 / Accepted: 26 February 2003 /
Published online: 20 May 2003
Correspondence to: Mauro Ursino (e-mail: mursino@deis.unibo.it, Tel.: +39-051-2093008, Fax: +39-051-2093073) 相似文献
11.
Man‐Sun Kim Jeong‐Rae Kim Kwang‐Hyun Cho 《BioEssays : news and reviews in molecular, cellular and developmental biology》2010,32(6):505-513
The identification of network motifs has been widely considered as a significant step towards uncovering the design principles of biomolecular regulatory networks. To date, time‐invariant networks have been considered. However, such approaches cannot be used to reveal time‐specific biological traits due to the dynamic nature of biological systems, and hence may not be applicable to development, where temporal regulation of gene expression is an indispensable characteristic. We propose a concept of a “temporal sequence of network motifs”, a sequence of network motifs in active sub‐networks constructed over time, and investigate significant network motifs in the active temporal sub‐networks of Drosophila melanogaster . Based on this concept, we find a temporal sequence of network motifs which changes according to developmental stages and thereby cannot be identified from the whole static network. Moreover, we show that the temporal sequence of network motifs corresponding to each developmental stage can be used to describe pivotal developmental events. 相似文献
12.
We introduce here the concept of Implicit networks which provide, like Bayesian networks, a graphical modelling framework that encodes the joint probability distribution for a set of random variables within a directed acyclic graph. We show that Implicit networks, when used in conjunction with appropriate statistical techniques, are very attractive for their ability to understand and analyze biological data. Particularly, we consider here the use of Implicit networks for causal inference in biomolecular pathways. In such pathways, an Implicit network encodes dependencies among variables (proteins, genes), can be trained to learn causal relationships (regulation, interaction) between them and then used to predict the biological response given the status of some key proteins or genes in the network. We show that Implicit networks offer efficient methodologies for learning from observations without prior knowledge and thus provide a good alternative to classical inference in Bayesian networks when priors are missing. We illustrate our approach by an application to simulated data for a simplified signal transduction pathway of the epidermal growth factor receptor (EGFR) protein. 相似文献
13.
José Antonio Villacorta-Atienza Manuel G. Velarde Valeri A. Makarov 《Biological cybernetics》2010,103(4):285-297
Animals for survival in complex, time-evolving environments can estimate in a “single parallel run” the fitness of different
alternatives. Understanding of how the brain makes an effective compact internal representation (CIR) of such dynamic situations
is a challenging problem. We propose an artificial neural network capable of creating CIRs of dynamic situations describing
the behavior of a mobile agent in an environment with moving obstacles. The network exploits in a mental world model the principle
of causality, which enables reduction of the time-dependent structure of real situations to compact static patterns. It is
achieved through two concurrent processes. First, a wavefront representing the agent’s virtual present interacts with mobile
and immobile obstacles forming static effective obstacles in the network space. The dynamics of the corresponding neurons
in the virtual past is frozen. Then the diffusion-like process relaxes the remaining neurons to a stable steady state, i.e.,
a CIR is given by a single point in the multidimensional phase space. Such CIRs can be unfolded into real space for execution
of motor actions, which allows a flexible task-dependent path planning in realistic time-evolving environments. Besides, the
proposed network can also work as a part of “autonomous thinking”, i.e., some mental situations can be supplied for evaluation
without direct motor execution. Finally we hypothesize the existence of a specific neuronal population responsible for detection
of possible time-space coincidences of the animal and moving obstacles. 相似文献
14.
Ann-Kristin Becker Marcus Drr Stephan B. Felix Fabian Frost Hans J. Grabe Markus M. Lerch Matthias Nauck Uwe Vlker Henry Vlzke Lars Kaderali 《PLoS computational biology》2021,17(2)
In this work, we introduce an entirely data-driven and automated approach to reveal disease-associated biomarker and risk factor networks from heterogeneous and high-dimensional healthcare data. Our workflow is based on Bayesian networks, which are a popular tool for analyzing the interplay of biomarkers. Usually, data require extensive manual preprocessing and dimension reduction to allow for effective learning of Bayesian networks. For heterogeneous data, this preprocessing is hard to automatize and typically requires domain-specific prior knowledge. We here combine Bayesian network learning with hierarchical variable clustering in order to detect groups of similar features and learn interactions between them entirely automated. We present an optimization algorithm for the adaptive refinement of such group Bayesian networks to account for a specific target variable, like a disease. The combination of Bayesian networks, clustering, and refinement yields low-dimensional but disease-specific interaction networks. These networks provide easily interpretable, yet accurate models of biomarker interdependencies. We test our method extensively on simulated data, as well as on data from the Study of Health in Pomerania (SHIP-TREND), and demonstrate its effectiveness using non-alcoholic fatty liver disease and hypertension as examples. We show that the group network models outperform available biomarker scores, while at the same time, they provide an easily interpretable interaction network. 相似文献
15.
We explore the behavior of richly connected inhibitory neural networks under parameter changes that correspond to weakening
of synaptic efficacies between network units, and show that transitions from irregular to periodic dynamics are common in
such systems. The weakening of these connections leads to a reduction in the number of units that effectively drive the dynamics
and thus to simpler behavior. We hypothesize that the multiple interconnecting loops of the brain’s motor circuitry, which
involve many inhibitory connections, exhibit such transitions. Normal physiological tremor is irregular while other forms
of tremor show more regular oscillations. Tremor in Parkinson’s disease, for example, stems from weakened synaptic efficacies
of dopaminergic neurons in the nigro-striatal pathway, as in our general model. The multiplicity of structures involved in
the production of symptoms in Parkinson’s disease and the reversibility of symptoms by pharmacological and surgical manipulation
of connection parameters suggest that such a neural network model is appropriate. Furthermore, fixed points that can occur
in the network models are suggestive of akinesia in Parkinson’s disease. This model is consistent with the view that normal
physiological systems can be regulated by robust and richly connected feedback networks with complex dynamics, and that loss
of complexity in the feedback structure due to disease leads to more orderly behavior. 相似文献
16.
Kendra S. Burbank 《PLoS computational biology》2015,11(12)
The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field’s Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks. 相似文献
17.
Organisms that can learn about their environment and modify their behaviour appropriately during their lifetime are more likely to survive and reproduce than organisms that do not. While associative learning – the ability to detect correlated features of the environment – has been studied extensively in nervous systems, where the underlying mechanisms are reasonably well understood, mechanisms within single cells that could allow associative learning have received little attention. Here, using in silico evolution of chemical networks, we show that there exists a diversity of remarkably simple and plausible chemical solutions to the associative learning problem, the simplest of which uses only one core chemical reaction. We then asked to what extent a linear combination of chemical concentrations in the network could approximate the ideal Bayesian posterior of an environment given the stimulus history so far? This Bayesian analysis revealed the ‘memory traces’ of the chemical network. The implication of this paper is that there is little reason to believe that a lack of suitable phenotypic variation would prevent associative learning from evolving in cell signalling, metabolic, gene regulatory, or a mixture of these networks in cells. 相似文献
18.
The ability of the human brain to carry out logical reasoning can be interpreted, in general, as a by-product of adaptive
capacities of complex neural networks. Thus, we seek to base abstract logical operations in the general properties of neural
networks designed as learning modules. We show that logical operations executable by McCulloch–Pitts binary networks can also
be programmed in analog neural networks built with associative memory modules that process inputs as logical gates. These
modules can interact among themselves to generate dynamical systems that extend the repertoire of logical operations. We demonstrate
how the operations of the exclusive-OR or the implication appear as outputs of these interacting modules. In particular, we provide a model of the exclusive-OR that succeeds in evaluating an odd number of options (the exclusive-OR of classical logic fails in his case), thus paving
the way for a more reasonable biological model of this important logical operator. We propose that a brain trained to compute
can associate a complex logical operation to an orderly structured but temporary contingent episode by establishing a codified
association among memory modules. This explanation offers an interpretation of complex logical processes (eventually learned)
as associations of contingent events in memorized episodes. We suggest, as an example, a cognitive model that describes these
“logical episodes”. 相似文献
19.
20.
Boris B. Vladimirskiy Eleni Vasilaki Robert Urbanczik Walter Senn 《Biological cybernetics》2009,100(4):319-330
Reinforcement learning in neural networks requires a mechanism for exploring new network states in response to a single, nonspecific
reward signal. Existing models have introduced synaptic or neuronal noise to drive this exploration. However, those types
of noise tend to almost average out—precluding or significantly hindering learning —when coding in neuronal populations or
by mean firing rates is considered. Furthermore, careful tuning is required to find the elusive balance between the often
conflicting demands of speed and reliability of learning. Here we show that there is in fact no need to rely on intrinsic
noise. Instead, ongoing synaptic plasticity triggered by the naturally occurring online sampling of a stimulus out of an entire
stimulus set produces enough fluctuations in the synaptic efficacies for successful learning. By combining stimulus sampling
with reward attenuation, we demonstrate that a simple Hebbian-like learning rule yields the performance that is very close
to that of primates on visuomotor association tasks. In contrast, learning rules based on intrinsic noise (node and weight
perturbation) are markedly slower. Furthermore, the performance advantage of our approach persists for more complex tasks
and network architectures. We suggest that stimulus sampling and reward attenuation are two key components of a framework
by which any single-cell supervised learning rule can be converted into a reinforcement learning rule for networks without
requiring any intrinsic noise source.
This work was supported by the Swiss National Science Foundation grant K-32K0-118084. 相似文献