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This paper presents a methodology for fast time-domain simulation of analog systems with nonlinear parameters. Specifically, the paper focuses on @[email protected] analog-to-digital converters (ADC). The method creates compiled-code simulators based on symbolic analysis. Code is optimized using loop invariant elimination and constant folding, well-known compiler optimization methods. Circuits are described as structural macromodels. Nonlinear parameters are expressed using piecewise linear (PWL) models. The paper presents a technique for automatically creating PWL models through model extraction from trained neural networks. As compared to existing behavioral simulation methods for @[email protected] ADC, this technique is more systematic and accurate. In our experiments, compiled-code simulation was significantly faster than numerical simulation. Hence, the methodology is very useful in analog and mixed-signal system synthesis, which is known to require a large number of simulation steps.

In this article we implement a stochastic modeling technique for simulating the communication between processors and arbitration among buses for an embedded SoC. The stochastic models implemented with queues have been used to estimate, through simulation of different arbitration policies, the power consumption and delays, as well as estimate average or worst case scenarios that could occur with different architectures and arbitration policies . This idea could then be extended to writing probabilistic test benches to analyze the performance of different architectures as well as device and test arbitration policies which would attempt to optimize the power consumption and buffer lengths with constraints on the average delay.

This paper presents a simulation model for observing the dynamics of a research community in engineering. The objective is to study how parameters, like group expertise and resources, influence the effectiveness of the group and community as a whole. The model implements a game-theoretic approach in which every group maximizes the difference between its rewards and costs (e.g., time and resources). Experiments studied the total reward and the number of problems solved over time by a community made of twenty groups for different conditions, i.e. allocated resources and group characteristics.

Place cell activity in the hippocampus depends in a complex way on the information from two major sensory sources: External (e.g., visual) cues and internal cues (self-motion, vestibular cues). Dissociation of the two inputs in the presence of the animal modifies the place fields in ways that depend on the magnitude of the difference: If the mismatch is small, place fields typically follow the external cues, but if the mismatch is large, their activity is determined mainly by the internal cues. The dynamics underlying the interaction between the external and internal cues in the hippocampus is not completely known, but is an important issue in understanding the hippocampus’ role in spatial learning and navigation. In this paper, we propose an attractor model of the hippocampus which captures the interaction between external and internal sensory inputs to the hippocampus. The proposed mechanism accounts for the difference seen in the experimental results in the case of a small versus a large discrepancy between the visual and idiothetic information.

Attractor networks have been one of the most successful paradigms in neural computation, and have been used as models of computation in the nervous system. Recently, we proposed a paradigm called ‘latent attractors’ where attractors embedded in a recurrent network via Hebbian learning are used to channel network response to external input rather than becoming manifest themselves. This allows the network to generate context-sensitive internal codes in complex situations. Latent attractors are particularly helpful in explaining computations within the hippocampus—a brain region of fundamental significance for memory and spatial learning.Latent attractor networks are a special case of associative memory networks. The model studied here consists of a two-layer recurrent network with attractors stored in the recurrent connections using a clipped Hebbian learning rule. The firing in both layers is competitive—K winners take all firing. The number of neurons allowed to fire, K, is smaller than the size of the ac...

It is well-known that identical sensory input under different perceptual, behavioral or contextual conditions can produce distinct patterns of activity in the place cells of the rodent hippocampus. However, the mechanisms underlying this have not been completely clarified. A recent experiment has shown that place cell activity on a 3-arm maze exhibits hysteresis as the maze is rotated with respect to distal cues. The apparent angular extent of a place field is greater when a maze arm rotates out of it than when it rotates back into the field. In this report, we present a simple attractor-based model of the hippocampus that reproduces this hysteresis phenomenon. The model allows us to make predictions about changes in the hysteresis effect as the animal becomes more familiar with the maze in several orientations. It also has implications for the place field remapping phenomenon seen in many hippocampal experiments.

The paper presents a development method for fuzzy controllers, derived from a stability analysis method based on a linearized model of the controlled plant. The stability conditions are expressed by a set of indices that verify the character of the equilibrium point and its uniqueness, and also measure the relative degree of stability. The proposed development method is applicable to the control of a class of nonlinear plants of arbitrary order, with the right-hand term of the state equation restricted to being a continuous and partially differentiable function. For the sake of simplicity, the presentation is mainly dedicated to second-order systems. The application examples presented here deal with the control of two real-world processes, namely the development of fuzzy control systems intended for the position control of an electro-hydraulic servosystem, and for the speed control of an electrical driving system with a variable moment of inertia.

Recently, it has been suggested that attractor networks may provide a mechanism for context-dependence in hippocampal place codes. We have proposed that context may be coded by “latent attractors” — mutually competitive and internally cooperative cell groups which channel the system's response to afferent stimuli. We have also argued that it is the disynaptically recurrent dentate gyrus–hilus (DGH) system which embodies these latent attractors in the hippocampus. Others have suggested the CA3 network — with its monosynaptic recurrence — is the site. While latent attractors (and, thus, context-dependent coding) can be implemented by a 1-layer or 2-layer recurrent network, we show that a 2-layer recurrent network can implement a sparse context-dependent place-code using latent attractors much more flexibly than a 1-layer network.

Primary neurons in all parts of the rodent hippocampus show location-specific patterns of activity called place fields, thus forming a distributed representation of place. Paradoxically, environments with very similar sensory cues can produce distinct place representations. The origin of this context dependence is not known. In this paper, we speculate that the dentate gyrus–hilus (DGH) system, with its disynaptic recurrent connectivity plays a central role in creating context-dependent place representations. We show that a simple system architecture can support distinct and consistent place representations for similar environments.

The paper presents a design method of fuzzy controllers (FCs) derived from a stability analysis method (Aracil, et al., 1989). The stability conditions are expressed by a number of indices which verifies the character of the equilibrium point, its uniqueness and also measures the relative degree of stability. The method is applied to the development of a fuzzy control system meant for the position control of an electrohydraulic servosystem.

In this paper we study the effect of external ideas on brainstorming by means of two computational models: a transient emergent attractors model (TEAM) and a probabilistic associative model (PAM). New behavioral experimental results show that hints or others' ideas can either hinder or enhance ideas generated during exposure period, while they consistently enhance the quantity of ideas produced after exposure. The TEAM model consists of a neural network of concept nodes connected by means of category membership and relatedness. Ideas emerge dynamically from the activity of the network by temporarily strengthening the connections between co-active nodes. Local inhibition inactivates current idea nodes and allows another idea to form. Active nodes prime connected inactive nodes depending on the recent activity of the node. The model shows that hindering of the number of ideas during hint presentation depends on the strength of hints and that the speed and duration of priming is essential for the long-term priming effect of hints observed in experiments. For comparison purposes, a PAM model originally proposed by Brown and Paulus (1998) is used to explain the same experimental data.

It is generally believed that cognition involves the self-organization of coherent dy- namic functional networks across several brain regions in response to incoming stimulus and internal modulation. These context-dependent networks arise continually from the spatiotemporally multi-scale structural substrate of the brain configured by evolution, development and previous experience, persisting for 100–200 ms and generating re- sponses such as imagery, recall and motor action. In the current paper, we show that a system of interacting modular attractor networks can use a selective mechanism for assembling functional networks from the modular substrate. We use the approach to develop a model of idea-generation in the brain. Ideas are modeled as combinations of concepts organized in a recurrent network that reflects previous associations between them. The dynamics of this network, resulting in the transient co-activation of concept groups, is seen as a search through the space of ideas, and attractor dynamics is used to “shape” this search. The process is required to encompass both rapid retrieval of old ideas in familiar contexts and efficient search for novel ones in unfamiliar situations (or during brainstorming). The inclusion of an adaptive modulatory mechanism allows the network to balance the competing requirements of exploiting previous learning and exploring new possibilities as needed in different contexts.

It is clear that much of the world's technological innovation originates from the domain of the startup, an arena in which the United States has historically played a major role. However, while business and other schools have traditionally offered courses in entrepreneurship, only about one third of all new businesses ever reach profitability and less than half of all new startups survive more than five years. Much of the attrition of small businesses is a result of poor preparation of the founders and the failure of academia to treat entrepreneurship as something more than a purely academic subject. A new program at Hofstra University focuses on a fresh approach to preparing Computer Science and Engineering students in entrepreneurship by providing a curriculum specifically designed to meet the myriad challenges encountered by entrepreneurs in the “real” world.

While major research universities enjoy a large pool of cutting-edge research projects for technology transfer, small liberal art institutions have to adopt a different model for promoting entrepreneurship due to resource limits. We propose a sandbox model for teaching entrepreneurship in a software engineering class. Student activities are closely monitored and coordinated by the instructor. Introduction of entrepreneurial thinking is tightly integrated with the milestone projects in the class. Students are allowed to fail, but they can always resume from the next milestone. We admit that the sandbox approach is highly debatable. Some faculty members believe that a fixed project topic may limit the creativity of students. On the other hand, we argue for the effectiveness of the approach. A good coverage of entrepreneurship topics can be achieved without requiring extra room in the curriculum. We report our preliminary experiences of implementing the sandbox model at Hofstra University.

Creativity, innovation and leadership are among the essential attributes of future engineers as identified in the Engineer 2020 report by the National Academy of Engineering. It is critical for students to have the ability to identify new needs and opportunities for technological innovation in highly complex and interdisciplinary domains. Historically, entrepreneurship has fueled innovation and the advent of many technological advances. Therefore, education in entrepreneurship for engineering or computing disciplines that complement strong analytical and technical skills, is likely to significantly enhance students' lifelong ability and desire to innovate. The goal of this panel is to discuss a number of education models in entrepreneurship, innovation, and leadership at different types of computing and engineering schools across the country and internationally, in order to identify common themes and effective components for such programs.

Creativity and innovativeness are among the most essential attributes of engineering graduates and also of successful entrepreneurs. Entrepreneurship, or the process of starting a new venture, is one of the main roads to new technological innovations. This paper presents two novel models of entrepreneurship education integrated in computer science and computer engineering curricula and geared towards computing students with entrepreneurial intentions. To expose all computing students to entrepreneurial ideas and to spark their entrepreneurial spirit, we also developed several entrepreneurship add-on modules for existing CS and CE disciplines. All these programs have been developed and implemented at Hofstra University, with modules implemented also at Qatar University. Preliminary evaluation results are presented and discussed.

Behavioral research on brainstorming discovered many cognitive, social and motivational factors that affect the idea generation process in individuals or in groups. There are still aspects not completely understood, especially the effects of sharing ideas or providing hints. In this paper we propose a conceptual dynamic model of the idea generation process to investigate the dynamics of idea generation process. The model consists of a network of fixed concept units with inter-connections determined by category membership and typicality of the unit. The novel feature of the model consists in the way ideas, represented as conceptual combinations, emerge in the conceptual network through a transient learning process. Simulation results show the effect of model parameters on idea generation process, and on convergent and divergent thinking. The model replicates results on the effect of hints presented sequentially versus simultaneously.

Randomly deployed wireless sensor networks are becoming increasingly viable for many applications. Such networks can comprise anywhere from a few hundred to thousands of sensor nodes, and these sizes are likely to grow with advancing technology, making scalability a primary concern. Each node in these sensor networks is a small unit with limited resources and localized sensing and communication. Thus, all global tasks must be accomplished through self-organized distributed algorithms, which also leads to improved scalability, robustness and flexibility. In this paper, we examine the use of distributed algorithms to infer the spatial structure of an extended environment monitored by a self-organizing sensor network. Based on its sensing, the network segments the environment into regions with distinct characteristics, thereby inferring a cognitive map of the environment. This, in turn, can be used to answer global queries about the environment efficiently and accurately. The main challenge to the network arises from the necessarily irregular spatial sampling and the need for totally distributed computation. We consider distributed machine learning techniques for segmentation and study the variation of segmentation quality with reconstruction at different node densities and in environments of varying complexity.

This paper discusses our ongoing efforts on developing, and conducting, senior design projects that involve undergraduate students working together on entrepreneurial, innovative projects. The students are from multiple universities, some of which are abroad. The goal is to emulate the main elements of global engineering teams that develop complex, multidisciplinary designs while targeting market-related constraints, such as price, reliability, size and ease-of-use. This paper discusses the goals and organization of the global student teams, and presents our experience in working with such teams.

Understanding the neural mechanisms of the idea generation process has implications for research in brainstorming, creativity and innovation. In this paper we present a conceptual neural model for generating ideas. The model extends the associative memory model of Brown et al. (1998) by explicitly representing categories as networks of concepts and ideas as conceptual combinations. Simulation results are compared with experimental results on effects of priming on low, versus high accessibility categories.

Brainstorming is the process of generating ideas in a specific task or problem context.We have previously presented a connectionist framework to study the dynamics of idea generation in individuals. In this paper, we develop this model further, and apply it to studying qualitatively the effects of priming on the process of ideation. Motivated by experimental data from a previous study, we explore the differential effects of relevant and irrelevant primes on productivity of idea generation in specific problem/task contexts. Simulations using our model suggest that even irrelevant primes can provide a modest productivity boost in contexts that are familiar or are similar to familiar contexts, but no benefit when the context is unfamiliar. We propose possible explanations for these results and make predictions for future experiments.

Many ECE graduates have only limited experience with design and integration of mixed-signal, embedded systems. This is in part due to the multitude of topics that must be taught to students during a very limited period of time. Fortunately, recent advances in reconfigurable hardware/software platforms and Visual Programming tools have the potential to dramatically address this deficiency, and enable a paradigm shift in ECE undergraduate education towards complex systems and networks of systems. This paper presents new educational material based on Visual Programming and reconfigurable mixed-signal systems on a chip to teach mixed-domain co-design and integration, including analog, digital, and software. The paper presents a new introductory course for freshmen ECE students, which is currently being developed at Stony Brook University, and a senior design project that was successfully completed at Hofstra University.

The recent changes in the world and engineering present both challenges and opportunities to the engineering education. Engineering education is changing to meet these challenges. More and more engineering programs strive to include entrepreneurship and innovation, traditionally American values, in the engineering curriculum. In this paper, we present our view on teaching entrepreneurship to future engineers and describe our experience in introducing entrepreneurship in engineering education through an NSF-sponsored pilot program based on collaboration between Stony Brook University and three other major higher education institutions on Long Island.

This paper presents a novel methodology, which extracts an optimal set of design (operation) points for an adaptable embedded system that functions under continuously varying throughput constraints. The methodology involves characterizing the throughput constraint variations with a new kind of mathematical modeling expressions called performance bands, and a technique for optimal selection of the design points needed for adaptation. Experiments compare the obtained over-design reduction as the system adapts to varying throughput constraints by switching to the selected design points.

Cognitive control -the ability to produce appropriate behavior in complex situations -is a fundamental aspect of intelligence. It is increasingly evident that this control arises from the interaction of dynamics in several brain regions, and depends significantly on processes of modulation and dynamical biasing. While most research has focused on explanations of behavioral responses seen in experiments and pathologies, it is reasonable to expect that internal functions such as planning and thinking would also use similar control mechanisms. In this paper, we present a connectionist model for an idea generation process that can rapidly retrieve old ideas in familiar contexts and search for novel ideas in unfamiliar ones. Based on a simple reinforcement signal, the system learns context-dependent biases that represent effective internal "response systems" for generating ideas from conceptual elements. A broad goal of the research is to show that preconfigured structural modularity, limited real-time selectivity, and adaptive modulation can interact to produce the flexible functionality necessary for cognition and intelligent behavior.

Context is an essential part of all cognitive function. However, neural network models have only considered this issue in limited ways, focusing primarily on the conditioning of a system’s response by its recent history. This type of context, which we term Type I, is clearly relevant in many situations, but in other cases, the system’s response for an extended period must be conditioned by stimuli encountered at a specific earlier time. For example, the decision to turn left or right at an intersection point in a navigation task depends on the goal set at the beginning of the task. We term this type of context, which sets the “frame of reference” for an entire episode, Type II context. The prefrontal cortex in mammals has been hypothesized to perform this function, but it has been difficult to incorporate this into neural network models. In the present chapter, we describe an approach called latent attractors that allows self-organizing neural systems to simultaneously incorporate both Type I and Type II context dependency. We demonstrate this by applying the approach to a series of problems requiring one or both types of context. We also argue that the latent attractor approach is a general and flexible method for incorporating multi-scale temporal dependence into neural systems, and possibly other self-organized

This paper presents a new technique for automatically creating analog circuit models. The method extracts piecewise linear models from trained neural networks. A model is a set of linear dependencies between circuit performance and design parameters. The paper illustrates the technique for an OTA circuit for which models for gain and bandwidth are generated. As experiments show, the obtained models have simple form that accurately fits the sampled points. These models are useful for fast simulation of systems with nonlinear behavior and performance.

The paper presents a neural model for learning sequences of relevant patterns embedded in distractors. A contextual episode is a sequence of relevant patterns - always in the same order - intermixed with distractors. By repeated presentations of all contextual episodes, the model discovers for each episode the set of relevant patterns and their order. The problem is solved in two stages: (a) by eliminating distractors, and (b) by learning the order between relevant patterns. The model uses the concept of latent attractors - essential in creating different neural representations for same patterns in distinct episodes. No external teacher and only Hebbian type learning rules are used.

This paper presents a new technique for automatically creating analog circuit models. The method extracts piecewise linear models from trained neural networks. A model is a set of linear dependencies between circuit performances and design parameters. The paper illustrates the technique for an OTA circuit - an amplifier circuit widely used in filters and A/D converters for which models for gain and bandwidth were automatically generated. As experiments show, the obtained models have simple form that accurately fits the sampled points and the behavior of the trained neural networks. These models are useful for fast simulation of systems with non-linear behavior and performances.

Latent attractor networks have been proposed as a possible mechanism for representing episodic context in the hippocampus, and as general purpose models of episodic context-dependent encoding in neural networks. These are recurrent neural networks with attractors that never fully manifest themselves, but bias the network's response to external stimuli. While each attractor in the original latent attractor model was triggered by unique context patterns specific to the context, this model was later extended to the case where contexts were triggered progressively by the sequential presentation of several stimulus patterns without regard to order, simulating the more realistic situation where a context is identified by a sequentially scanned combination of landmarks. In this paper, we describe a network model that can select among contexts identified by overlapping sequences of different lengths, even if the relevant stimulus patterns are interspersed among patterns irrelevant to context selection.

Latent attractor networks are recurrent neural networks with weak attractors that bias the network's response to external stimuli but never fully manifest themselves. Such networks have been used to model context-dependent place representations in the hippocampus, and to encode context-dependent stimuli in neural networks. In the original latent attractor model, each attractor was triggered by a unique context pattern representing a stimulus that uniquely identified the context of the subsequent episode. This model was later extended to the case where contexts were triggered progressively by the sequential presentation of several stimulus patterns without regard to order. In this paper, we describe a network model that can select contexts even if the triggering stimulus patterns are interspersed among patterns irrelevant to context selection. This is closer to the way such a process would occur cognitively, where contexts are typically recognized based on a subset of sequentially perceived identifiers or cues among a larger set of perceived items.

Attractor networks have been one of the most successful paradigms in neural computation, and have been used as models of computation in the nervous system. Recently, we proposed a paradigm called 'latent attractors' where attractors embedded in a recurrent network via Hebbian learning are used to channel network response to external input rather than becoming manifest themselves. This allows the network to generate context-sensitive internal codes in complex situations. Latent attractors are particularly helpful in explaining computations within the hippocampus--a brain region of fundamental significance for memory and spatial learning. Latent attractor networks are a special case of associative memory networks. The model studied here consists of a two-layer recurrent network with attractors stored in the recurrent connections using a clipped Hebbian learning rule. The firing in both layers is competitive--K winners take all firing. The number of neurons allowed to fire, K, is smaller than the size of the active set of the stored attractors. The performance of latent attractor networks depends on the number of such attractors that a network can sustain. In this paper, we use signal-to-noise methods developed for standard associative memory networks to do a theoretical and computational analysis of the capacity and dynamics of latent attractor networks. This is an important first step in making latent attractors a viable tool in the repertoire of neural computation. The method developed here leads to numerical estimates of capacity limits and dynamics of latent attractor networks. The technique represents a general approach to analyse standard associative memory networks with competitive firing. The theoretical analysis is based on estimates of the dendritic sum distributions using Gaussian approximation. Because of the competitive firing property, the capacity results are estimated only numerically by iteratively computing the probability of erroneous firings. The analysis contains two cases: the simple case analysis which accounts for the correlations between weights due to shared patterns and the detailed case analysis which includes also the temporal correlations between the network's present and previous state. The latter case predicts better the dynamics of the network state for non-zero initial spurious firing. The theoretical analysis also shows the influence of the main parameters of the model on the storage capacity.

In many cognitive situations, context is specified globally-encompassing the entire duration of an episode, but explicitly identified only at its initiation. Examples of such contexts are social situations, spatial environments, task specifications, etc. In many such cases, a neural system needs to respond differently to the same immediate stimulus depending on the global context. Clearly, the system must be able to switch into different response regimes and remain there while the context remains fixed. Simultaneously, it must remain sensitive to external stimuli, and respond to them in a reliable and informative manner. We propose a neurally plausible approach for handling these requirements.

Latent attractor networks are recurrent neural networks with weak embedded attractors. The attractors bias the network's response to external inputs without becoming fully manifested themselves. Latent attractor networks have been used to model context-dependent spatial representations in the hippocampus, and to encode context-dependent stimuli in neural networks. In the current model, the selection of the biasing attractor occurred in response to an initial triggering stimulus indicating the context signal. For example, the sign on a door may set the context for the representation of a room. However, in many realistic situations, context is set by a set of cues rather than a single cue, and these cues are typically seen sequentially, though not in a particular order. The problem addressed here is: how can a latent attractor network progressively select an attractor in response to a sequence of context patterns?.

If a place field of a hippocampal cell is established in a specific environment, then what happens to that cell’s activity when the animal finds itself in a very different environment? There are a number of possible answers to this question: The cell could have a place field in the new environment that is topologically related to the field in the first environment. The cell could have a place field in the new environment that is related in some non-topological but otherwise discernible logical way to its field in the first environment. The cell could have a field that is not discernibly related to its field in the first environment. The cell could have no field, even though it shows some activity in the new environment. The cell could be essentially silent in the new environment. This chapter reviews the research that has addressed this question.