13,167 research outputs found
Collective stability of networks of winner-take-all circuits
The neocortex has a remarkably uniform neuronal organization, suggesting that
common principles of processing are employed throughout its extent. In
particular, the patterns of connectivity observed in the superficial layers of
the visual cortex are consistent with the recurrent excitation and inhibitory
feedback required for cooperative-competitive circuits such as the soft
winner-take-all (WTA). WTA circuits offer interesting computational properties
such as selective amplification, signal restoration, and decision making. But,
these properties depend on the signal gain derived from positive feedback, and
so there is a critical trade-off between providing feedback strong enough to
support the sophisticated computations, while maintaining overall circuit
stability. We consider the question of how to reason about stability in very
large distributed networks of such circuits. We approach this problem by
approximating the regular cortical architecture as many interconnected
cooperative-competitive modules. We demonstrate that by properly understanding
the behavior of this small computational module, one can reason over the
stability and convergence of very large networks composed of these modules. We
obtain parameter ranges in which the WTA circuit operates in a high-gain
regime, is stable, and can be aggregated arbitrarily to form large stable
networks. We use nonlinear Contraction Theory to establish conditions for
stability in the fully nonlinear case, and verify these solutions using
numerical simulations. The derived bounds allow modes of operation in which the
WTA network is multi-stable and exhibits state-dependent persistent activities.
Our approach is sufficiently general to reason systematically about the
stability of any network, biological or technological, composed of networks of
small modules that express competition through shared inhibition.Comment: 7 Figure
Competition through selective inhibitory synchrony
Models of cortical neuronal circuits commonly depend on inhibitory feedback
to control gain, provide signal normalization, and to selectively amplify
signals using winner-take-all (WTA) dynamics. Such models generally assume that
excitatory and inhibitory neurons are able to interact easily, because their
axons and dendrites are co-localized in the same small volume. However,
quantitative neuroanatomical studies of the dimensions of axonal and dendritic
trees of neurons in the neocortex show that this co-localization assumption is
not valid. In this paper we describe a simple modification to the WTA circuit
design that permits the effects of distributed inhibitory neurons to be coupled
through synchronization, and so allows a single WTA to be distributed widely in
cortical space, well beyond the arborization of any single inhibitory neuron,
and even across different cortical areas. We prove by non-linear contraction
analysis, and demonstrate by simulation that distributed WTA sub-systems
combined by such inhibitory synchrony are inherently stable. We show
analytically that synchronization is substantially faster than winner
selection. This circuit mechanism allows networks of independent WTAs to fully
or partially compete with each other.Comment: in press at Neural computation; 4 figure
A Neural Model for Self Organizing Feature Detectors and Classifiers in a Network Hierarchy
Many models of early cortical processing have shown how local learning rules can produce efficient, sparse-distributed codes in which nodes have responses that are statistically independent and low probability. However, it is not known how to develop a useful hierarchical representation, containing sparse-distributed codes at each level of the hierarchy, that incorporates predictive feedback from the environment. We take a step in that direction by proposing a biologically plausible neural network model that develops receptive fields, and learns to make class predictions, with or without the help of environmental feedback. The model is a new type of predictive adaptive resonance theory network called Receptive Field ARTMAP, or RAM. RAM self organizes internal category nodes that are tuned to activity distributions in topographic input maps. Each receptive field is composed of multiple weight fields that are adapted via local, on-line learning, to form smooth receptive ftelds that reflect; the statistics of the activity distributions in the input maps. When RAM generates incorrect predictions, its vigilance is raised, amplifying subtractive inhibition and sharpening receptive fields until the error is corrected. Evaluation on several classification benchmarks shows that RAM outperforms a related (but neurally implausible) model called Gaussian ARTMAP, as well as several standard neural network and statistical classifters. A topographic version of RAM is proposed, which is capable of self organizing hierarchical representations. Topographic RAM is a model for receptive field development at any level of the cortical hierarchy, and provides explanations for a variety of perceptual learning data.Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409
Experience-driven formation of parts-based representations in a model of layered visual memory
Growing neuropsychological and neurophysiological evidence suggests that the
visual cortex uses parts-based representations to encode, store and retrieve
relevant objects. In such a scheme, objects are represented as a set of
spatially distributed local features, or parts, arranged in stereotypical
fashion. To encode the local appearance and to represent the relations between
the constituent parts, there has to be an appropriate memory structure formed
by previous experience with visual objects. Here, we propose a model how a
hierarchical memory structure supporting efficient storage and rapid recall of
parts-based representations can be established by an experience-driven process
of self-organization. The process is based on the collaboration of slow
bidirectional synaptic plasticity and homeostatic unit activity regulation,
both running at the top of fast activity dynamics with winner-take-all
character modulated by an oscillatory rhythm. These neural mechanisms lay down
the basis for cooperation and competition between the distributed units and
their synaptic connections. Choosing human face recognition as a test task, we
show that, under the condition of open-ended, unsupervised incremental
learning, the system is able to form memory traces for individual faces in a
parts-based fashion. On a lower memory layer the synaptic structure is
developed to represent local facial features and their interrelations, while
the identities of different persons are captured explicitly on a higher layer.
An additional property of the resulting representations is the sparseness of
both the activity during the recall and the synaptic patterns comprising the
memory traces.Comment: 34 pages, 12 Figures, 1 Table, published in Frontiers in
Computational Neuroscience (Special Issue on Complex Systems Science and
Brain Dynamics),
http://www.frontiersin.org/neuroscience/computationalneuroscience/paper/10.3389/neuro.10/015.2009
Combining Distributed and Localist Computations in Real-Time Networks
In order to benefit from the advantages of localist coding, neural models that feature winner-take-all representations at the top level of a network hierarchy must still solve the computational problems inherent in distributed representations at the lower levels
Distributed ARTMAP
Distributed coding at the hidden layer of a multi-layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically requires slow off-line learning to avoid catastrophic forgetting in an open input environment. An adaptive resonance theory (ART) model is designed to guarantee stable memories even with fast on-line learning. However, ART stability typically requires winner-take-all coding, which may cause category proliferation in a noisy input environment. Distributed ARTMAP (dARTMAP) seeks to combine the computational advantages of MLP and ART systems in a real-time neural network for supervised learning. This system incorporates elements of the unsupervised dART model as well as new features, including a content-addressable memory (CAM) rule. Simulations show that dARTMAP retains fuzzy ARTMAP accuracy while significantly improving memory compression. The model's computational learning rules correspond to paradoxical cortical data.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657
Distributed Hypothesis Testing, Attention Shifts and Transmitter Dynatmics During the Self-Organization of Brain Recognition Codes
BP (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI-90-00530); Air Force Office of Scientific Research (90-0175, 90-0128); Army Research Office (DAAL-03-88-K0088
Hierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence
No abstract availabl
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