18592302
A Winner-Take-All Circuit with Controllable Soft Max Property
I describe a silicon network consisting of a group of excitatory neurons and a global inhibitory neuron. The output of the inhibitory neuron is normalized with respect to the input strengths. This output models the normalization property of the wide-field direction-selective cells in the fly visual system. This normalizing property is also useful in any system where we wish the output signal to code only the strength of the inputs, and not be dependent on the number of inputs. The circuitry in each neuron is equivalent to that in Lazzaro's winner-take-all (WTA) circuit with one additional transistor and a voltage reference. Just as in Lazzaro's circuit, the outputs of the excitatory neurons code the neuron with the largest input. The difference here is that multiple winners can be chosen. By varying the voltage reference of the neuron, the network can transition between a soft-max behavior and a hard WTA behavior. I show results from a fabricated chip of 20 neurons in a 1.2 µm CMOS technology.
8818712
A silicon retina with controllable winner-take-all properties
The winner-take-all (WTA) circuit is a useful computational circuit for signal processing and learning tasks. By adding spatial coupling between pixels, local regions of competition can be delineated. Recently, we described a normalising circuit which enhances Lazzaro's WTA circuit (1989) only with the addition of a transistor and a global bias to each pixel. This new circuit allows the network to transition between a soft-max and a WTA function through the global bias. The WTA network of Lazzaro together with spatial coupling forms the current-mode silicon retina of Boahen and Andreou (1992). This retina models the center surround processing performed in biological retinas to enhance responses to spatial contrasts. Here, we show how our normalising network together with spatial coupling performs as a silicon retina. Results are presented from a fabricated circuit in a 2 /spl mu/m CMOS process.
1845062
Adaptive Retina with Center-Surround Receptive Field
Both vertebrate and invertebrate retinas are highly efficient in extracting contrast independent of the background intensity over five or more decades. This efficiency has been rendered possible by the adaptation of the DC operating point to the background intensity while maintaining high gain transient responses. The center-surround properties of the retina allows the system to extract information at the edges in the image. This silicon retina models the adaptation properties of the receptors and the antagonistic center-surround properties of the laminar cells of the invertebrate retina and the outer-plexiform layer of the vertebrate retina. We also illustrate the spatio-temporal responses of the silicon retina on moving bars. The chip has 59×64 pixels on a 6.9×6.8mm2 die and it is fabricated in 2 µm n-well technology.
1715678
Circuit Model of Short-Term Synaptic Dynamics
We describe a model of short-term synaptic depression that is derived from a silicon circuit implementation. The dynamics of this circuit model are similar to the dynamics of some present theoretical models of short-term depression except that the recovery dynamics of the variable describing the depression is nonlinear and it also depends on the presynaptic frequency. The equations describing the steady-state and transient responses of this synaptic model fit the experimental results obtained from a fabricated silicon network consisting of leaky integrate-and-fire neurons and different types of synapses. We also show experimental data demonstrating the possible computational roles of depression. One possible role of a depressing synapse is that the input can quickly bring the neuron up to threshold when the membrane potential is close to the resting potential.
6161480
Homeostasis in a Silicon Integrate and Fire Neuron
In this work, we explore homeostasis in a silicon integrate-and-fire neuron. The neuron adapts its firing rate over long time periods on the order of seconds or minutes so that it returns to its spontaneous firing rate after a lasting perturbation. Homeostasis is implemented via two schemes. One scheme looks at the presynaptic activity and adapts the synaptic weight depending on the presynaptic spiking rate. The second scheme adapts the synaptic "threshold" depending on the neuron's activity. The threshold is lowered if the neuron's activity decreases over a long time and is increased for prolonged increase in postsynaptic activity. Both these mechanisms for adaptation use floating-gate technology. The results shown here are measured from a chip fabricated in a 2-µm CMOS process.
34951314
Spike synchronization in a network of silicon integrate-and-fire neurons
Spike-event based computational processing by neuronal networks depends on a variety of spatio-temporal mechanisms that are distributed across neuronal synapses, dendrites, and somata. The overall processing of spatio-temporal patterns of spike requires global temporal coherence between all these processes both within a neuron, and across all neurons of the network. We are evaluating the use of networks of hybrid analog-digital integrate-and-fire neurons, fabricated using VLSI, for studying real-time event-based processing. These hardware networks have the advantages of real-time operation, and inherent global synchronization by their analog implementation. However, one of the technical challenges in the operation of such networks is the unavoidable fabrication related variance between the different neurons of the network. Here we show that despite this variance, an important behavior of neuronal networks, spike synchronization, can be obtained from a simple network of VLSI neurons interconnected by lateral excitation and global inhibition.
11533048
RFID Data Processing with a Data Stream Query Language
RFID technology provides significant advantages over traditional object-tracking technologies and is increasingly adopted and deployed in real applications. RFID applications generate large volume of streaming data, which have to be automatically filtered, processed, and transformed into semantic data, and integrated into business applications. Indeed, RFID data are highly temporal, and RFID observations form complex temporal event patterns which can be very different for various RFID applications. Thus, it is desirable to have a general RFID data processing framework with a powerful language, for the end users to express a variety of queries on RFID data streams, as well as detecting complex events patterns. While data stream management systems (DSMSs) are emerging for optimized stream data processing, they usually lack the language construct support for temporal event detection. In this paper, we discuss a stream query language to provide comprehensive temporal event detection, through temporal operators and extension of sliding-window constructs. With the integration of temporal event detection, a DSMS has the capability to serve as a powerful system for RFID data processing.
15677254
Inference Queues for Communicating and Monitoring Declarative Information between Web Services
We introduce the inference queue as a mechanism for communicating and transforming data in Web services choreography. The insert operation provides definite clauses to the inference queue, and the remove operation generates output that is sound, complete, fair, and irredundant. Both operations are thread safe and responsive. The inference queue can form part of a highly configurable data transformation system. Rules can also monitor for events of interest based on the occurrence of certain conditions. Our suggestion of system wide monitoring of communication is complementary to existing Web services proposals.
14954145
Interacting frameworks in Catalysis
In current OO design, most of the existing (semi-formal) methods use classes or objects as the basic unit of design. However it is increasingly recognised that classes are not the best focus for design. Typical design artefacts are about groups of objects and the way they interact In the Catalysis project, we use the term frameworks for descriptions of groups of objects, their relationships, division of responsibilities, and interactions. The power of frameworks lies in the fact that interactions can also occur at the level of frameworks. In a previous paper we defined a framework formally as a (first-order) many-sorted theory with a model theoretic semantics and showed how it can be represented using a pictorial notation for practical system development. However we considered only frameworks that do not interact with one another In this paper we discuss frameworks that interact with one another and show that such frameworks are very powerful tools for formal OO design.
1673600
Inferring Data Transformation Rules to Integrate Semantic Web Services
OWL-S allows selecting, composing and invoking Web Services at different levels of abstraction: selection uses high level abstract descriptions, invocation uses low level grounding ones, while composition needs to consider both high and low level descriptions. In our setting, two Web Services are to be composed so that output from the upstream one is used to create input for the downstream one. These Web Services may have different data models but are related to each other through high and low level descriptions. 
 
Correspondences must be found between components of the upstream data type and the downstream ones. Low level data transformation functions may be required (e.g. unit conversions, data type conversions). The components may be arranged in different XML tree structures. Thus, multiple data transformations are necessary: reshaping the message tree, matching leaves by corresponding types, translating through ontologies, and calling conversion functions. Our prototype compiles these transformations into a set of data transformation rules, using our tableau-based ALC Description Logic reasoner to reason over the given OWL-S and WSDL descriptions, as well as the related ontologies. A resolution-based inference mechanism for running these rules is embedded in an inference queue that conducts data from the upstream to the downstream service, running the rules to perform the data transformation in the process.
18135743
A clustering entropy-driven approach for exploring and exploiting noisy functions
Linear, Gaussian, fitness proportional, clustering, and Rosca entropies are succinct measures of diversity that have been applied to balance exploration and exploitation in evolutionary algorithms. In previous studies, an entropy-driven approach using linear entropy explicitly balances and/or searches optimal solutions for the selected unimodal and multimodal functions excluding noisy functions. This paper investigates the reasons for such an exception and introduces a clustering entropy-driven approach to solve the problem. Such an approach provides a coarse-grained diversity measure that filters the noise of functions, varies cluster size and categorizes individuals at the genotype level. The experimental results show that the clustering entropy-driven approach further improves the searching results of noisy functions by one more degree.