Formal Modeling of Connectionism using Concurrency Theory, an Approach Based on Automata and Model Checking

This paper illustrates a framework for applying formal methods techniques, which are symbolic in nature, to specifying and verifying neural networks, which are sub-symbolic in nature. The paper describes a communicating automata [Bowman & Gomez, 2006] model of neural networks. We also implement the model using timed automata [Alur & Dill, 1994] and then undertake a verification of these models using the model checker Uppaal [Pettersson, 2000] in order to evaluate the performance of learning algorithms. This paper also presents discussion of a number of broad issues concerning cognitive neuroscience and the debate as to whether symbolic processing or connectionism is a suitable representation of cognitive systems. Additionally, the issue of integrating symbolic techniques, such as formal methods, with complex neural networks is discussed. We then argue that symbolic verifications may give theoretically well-founded ways to evaluate and justify neural learning systems in the field of both theoretical research and real world applications

Continuous-time recurrent neural networks for quadratic programming: theory and engineering applications.

Liu Shubao.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 90-98).Abstracts in English and Chinese.Abstract --- p.i摘要 --- p.iiiAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Time-Varying Quadratic Optimization --- p.1Chapter 1.2 --- Recurrent Neural Networks --- p.3Chapter 1.2.1 --- From Feedforward to Recurrent Networks --- p.3Chapter 1.2.2 --- Computational Power and Complexity --- p.6Chapter 1.2.3 --- Implementation Issues --- p.7Chapter 1.3 --- Thesis Organization --- p.9Chapter I --- Theory and Models --- p.11Chapter 2 --- Linearly Constrained QP --- p.13Chapter 2.1 --- Model Description --- p.14Chapter 2.2 --- Convergence Analysis --- p.17Chapter 3 --- Quadratically Constrained QP --- p.26Chapter 3.1 --- Problem Formulation --- p.26Chapter 3.2 --- Model Description --- p.27Chapter 3.2.1 --- Model 1 (Dual Model) --- p.28Chapter 3.2.2 --- Model 2 (Improved Dual Model) --- p.28Chapter II --- Engineering Applications --- p.29Chapter 4 --- KWTA Network Circuit Design --- p.31Chapter 4.1 --- Introduction --- p.31Chapter 4.2 --- Equivalent Reformulation --- p.32Chapter 4.3 --- KWTA Network Model --- p.36Chapter 4.4 --- Simulation Results --- p.40Chapter 4.5 --- Conclusions --- p.40Chapter 5 --- Dynamic Control of Manipulators --- p.43Chapter 5.1 --- Introduction --- p.43Chapter 5.2 --- Problem Formulation --- p.44Chapter 5.3 --- Simplified Dual Neural Network --- p.47Chapter 5.4 --- Simulation Results --- p.51Chapter 5.5 --- Concluding Remarks --- p.55Chapter 6 --- Robot Arm Obstacle Avoidance --- p.56Chapter 6.1 --- Introduction --- p.56Chapter 6.2 --- Obstacle Avoidance Scheme --- p.58Chapter 6.2.1 --- Equality Constrained Formulation --- p.58Chapter 6.2.2 --- Inequality Constrained Formulation --- p.60Chapter 6.3 --- Simplified Dual Neural Network Model --- p.64Chapter 6.3.1 --- Existing Approaches --- p.64Chapter 6.3.2 --- Model Derivation --- p.65Chapter 6.3.3 --- Convergence Analysis --- p.67Chapter 6.3.4 --- Model Comparision --- p.69Chapter 6.4 --- Simulation Results --- p.70Chapter 6.5 --- Concluding Remarks --- p.71Chapter 7 --- Multiuser Detection --- p.77Chapter 7.1 --- Introduction --- p.77Chapter 7.2 --- Problem Formulation --- p.78Chapter 7.3 --- Neural Network Architecture --- p.82Chapter 7.4 --- Simulation Results --- p.84Chapter 8 --- Conclusions and Future Works --- p.88Chapter 8.1 --- Concluding Remarks --- p.88Chapter 8.2 --- Future Prospects --- p.88Bibliography --- p.8

Choosing our words: Neural mechanisms supporting cognitive control during language processing

When we speak, we must constantly retrieve and select words in the face of multiple competing alternatives. Previous research has left many questions unanswered about how we achieve these fundamental cognitive control processes. This dissertation contributes to answering these questions at three levels. First, using well-controlled tasks and measures, we ask what specific aspects of language production drive cognitive control demands, as indexed by slower RTs to produce a verbal response. Second, we apply these unconfounded measures to fMRI experiments, to ask what neural substrates support cognitive control during language production. Third, we ask how these brain areas support cognitive control processes, by first simulating possible mechanisms in a neural network model and then empirically testing model predictions using pharmacological and clinical methods. In sum, the dissertation research suggests that cognitive control is needed during language production when responses compete with alternative task-relevant response options (underdetermined selection), compete with prepotent responses (prepotent selection), or are difficult to retrieve from semantic memory (controlled retrieval), and these demands interact both behaviorally and neurally. Shared neural substrates in left ventrolateral prefrontal cortex (VLPFC) support both underdetermined selection and controlled retrieval, while left VLPFC is not activated by prepotent selection demands. In contrast, an area of left dorsolateral prefrontal cortex (DLPFC) is sensitive to both underdetermined and prepotent competition. Neural network simulations suggest that competitive lateral inhibition in VLPFC is key for underdetermined selection, while other mechanisms subserved by VLPFC support controlled retrieval, and top-down biasing from DLPFC is critical for prepotent selection. As predicted by the model, increased inhibition under the GABA agonist midazolam improved selection, while anxiety (linked to reduced GABAergic function) was associated with impaired selection and reduced engagement of left VLPFC during selection. These findings enable a synthesis and reinterpretation of prior evidence, and suggest that language production is affected by both selection and retrieval mechanisms subserved by left VLPFC and DLPFC, and these processes interact in meaningful ways. Better understanding these fundamental aspects of language production may ultimately have implications for better understanding and treating impairments associated with prefrontal damage, as well as anxiety and depression

A Neurocomputational Model of the Functional Role of Dopamine in Stimulus-Response Task Learning and Performance

Thesis (Ph.D.) - Indiana University, Psychology, 2009The neuromodulatory neurotransmitter dopamine (DA) plays a complex, but central role in the learning and performance of stimulus-response (S-R) behaviors. Studies have implicated DA's role in reward-driven learning and also its role in setting the overall level of vigor or frequency of response. Here, a neurocomputational model is developed which models DA's influence on a set of brain regions believed to be involved in the learning and execution of S-R tasks, including frontal cortex, basal ganglia, and cingulate cortex. An `actor' component of the model is trained, using `babble' (random behavior selection) and `critic' (rewarding and punishing) components of the model, to perform acceptance/rejection responses upon presentation of color stimuli in the context of recently presented auditory tones. The model behaves like an autonomous organism learning (and relearning) through `trial-and-error'. The focus of the study, the impact of hypo- and hyper-normal DA activity on this model, is investigated by three different dopaminergic pathways--two striatal and one prefrontal cortical--being manipulated independently during the learning and performance of the color response task. Hypo-DA conditions, analogous to Parkinsonism, cause slowing and reduction of frequency of learned responses, and, at extremes, degrade the learning (either initial or reversal) of the task. Hyper-DA conditions, analogous to psychostimulant effects, cause more rapid response times, but also can lead to perseveration of incorrect learning of response on the task. The presence of these effects often depends on which DA-ergic pathway is manipulated, however, which has implications for interpretation of the pharmacological experimental data. The proposed model embodies an integrative theory of dopamine function which suggests that the base rate of DA cell activity encodes the overall `activity-oriented motivation' of the organism, with hunger and/or expectation of reward driving both response vigor and tendency to generate an explorative `babble' response. This more `tonic' feature of DA functionality coexists naturally with the more extensively-studied `phasic' reward-learning features. The model may provide better insights on the role of DA system dysfunction in the cognitive and motivational symptoms of disorders such as Parkinsonism, psychostimulant abuse, ADHD, OCD, and schizophrenia, accounting for deficits in both learning and performance of tasks

A Review of Findings from Neuroscience and Cognitive Psychology as Possible Inspiration for the Path to Artificial General Intelligence

This review aims to contribute to the quest for artificial general intelligence by examining neuroscience and cognitive psychology methods for potential inspiration. Despite the impressive advancements achieved by deep learning models in various domains, they still have shortcomings in abstract reasoning and causal understanding. Such capabilities should be ultimately integrated into artificial intelligence systems in order to surpass data-driven limitations and support decision making in a way more similar to human intelligence. This work is a vertical review that attempts a wide-ranging exploration of brain function, spanning from lower-level biological neurons, spiking neural networks, and neuronal ensembles to higher-level concepts such as brain anatomy, vector symbolic architectures, cognitive and categorization models, and cognitive architectures. The hope is that these concepts may offer insights for solutions in artificial general intelligence.Comment: 143 pages, 49 figures, 244 reference

High Efficiency Real-Time Sensor and Actuator Control and Data Processing

The advances in sensor and actuator technology foster the use of large multitransducer networks in many different fields. The increasing complexity of such networks poses problems in data processing, especially when high-efficiency is required for real-time applications. In fact, multi-transducer data processing usually consists of interconnection and co-operation of several modules devoted to process different tasks. Multi-transducer network modules often include tasks such as control, data acquisition, data filtering interfaces, feature selection and pattern analysis. Heterogeneous techniques derived from chemometrics, neural networks, fuzzy-rules used to implement such tasks may introduce module interconnection and co-operation issues. To help dealing with these problems the author here presents a software library architecture for a dynamic and efficient management of multi-transducer data processing and control techniques. The framework’s base architecture and the implementation details of several extensions are described. Starting from the base models available in the framework core dedicated models for control processes and neural network tools have been derived. The Facial Automaton for Conveying Emotion (FACE) has been used as a test field for the control architecture

Towards Debugging and Improving Adversarial Robustness Evaluations ​

Despite exhibiting unprecedented success in many application domains, machine‐learning models have been shown to be vulnerable to adversarial examples, i.e., maliciously perturbed inputs that are able to subvert their predictions at test time. Rigorous testing against such perturbations requires enumerating all possible outputs for all possible inputs, and despite impressive results in this field, these methods remain still difficult to scale to modern deep learning systems. For these reasons, empirical methods are often used. These adversarial perturbations are optimized via gradient descent, minimizing a loss function that aims to increase the probability of misleading the model’s predictions. To understand the sensitivity of the model to such attacks, and to counter the effects, machine-learning model designers craft worst-case adversarial perturbations and test them against the model they are evaluating. However, many of the proposed defenses have been shown to provide a false sense of security due to failures of the attacks, rather than actual improvements in the machine‐learning models’ robustness. They have been broken indeed under more rigorous evaluations. Although guidelines and best practices have been suggested to improve current adversarial robustness evaluations, the lack of automatic testing and debugging tools makes it difficult to apply these recommendations in a systematic and automated manner. To this end, we tackle three different challenges: (1) we investigate how adversarial robustness evaluations can be performed efficiently, by proposing a novel attack that can be used to find minimum-norm adversarial perturbations; (2) we propose a framework for debugging adversarial robustness evaluations, by defining metrics that reveal faulty evaluations as well as mitigations to patch the detected problems; and (3) we show how to employ a surrogate model for improving the success of transfer-based attacks, that are useful when gradient-based attacks are failing due to problems in the gradient information. To improve the quality of robustness evaluations, we propose a novel attack, referred to as Fast Minimum‐Norm (FMN) attack, which competes with state‐of‐the‐art attacks in terms of quality of the solution while outperforming them in terms of computational complexity and robustness to sub‐optimal configurations of the attack hyperparameters. These are all desirable characteristics of attacks used in robustness evaluations, as the aforementioned problems often arise from the use of sub‐optimal attack hyperparameters, including, e.g., the number of attack iterations, the step size, and the use of an inappropriate loss function. The correct refinement of these variables is often neglected, hence we designed a novel framework that helps debug the optimization process of adversarial examples, by means of quantitative indicators that unveil common problems and failures during the attack optimization process, e.g., in the configuration of the hyperparameters. Commonly accepted best practices suggest further validating the target model with alternative strategies, among which is the usage of a surrogate model to craft the adversarial examples to transfer to the model being evaluated is useful to check for gradient obfuscation. However, how to effectively create transferable adversarial examples is not an easy process, as many factors influence the success of this strategy. In the context of this research, we utilize a first-order model to show what are the main underlying phenomena that affect transferability and suggest best practices to create adversarial examples that transfer well to the target models.