Perturbing the impulse activity of a single identified neuron augments the formation of long-term memory in a molluscan semi-intact preparation /

The aim of this study was to investigate the neural correlates of operant conditioning in a semi-intact preparation of the pond snail, Lymnaea stagnalis. Lymnaea learns, via operant conditioning, to reduce its aerial respiratory behaviour in response to an aversive tactile stimulus to its open pneumostome. This thesis demonstrates the successful conditioning of na'ive semiintact preparations to show learning in the dish. Furthermore, these conditioned preparations show long-term memory that persists for at least 18 hours. As the neurons that generate this behaviour have been previously identified I can, for the first time, monitor neural activity during both learning and long-term memory consolidation in the same preparation. In particular, I record from the respiratory neuron Right Pedal Dorsal 1 (RPeD 1) which is part of the respiratory central pattern generator. In this study, I demonstrate that preventing RPeDl impulse activity between training sessions reduces the number of sessions needed to produce long-term memory in the present semi-intact preparation.

Teaching persons with intellectual disabilities and limited receptive and expressive communication how to recognize and assert their human rights

Persons with intellectual disabilities (ID) are far more likely to be abused than the general population, but there is little research on teaching people with ID about their rights. The goal of this study was to teach four participants with ID and limited communication abilities about their human rights by training them on specific rights topics. The training program included icebreaker activities, instruction on rights concepts, watching and answering questions about videotaped scenarios of rights restrictions, watching and answering questions about role pl ay scenarios of rights restrictions, and responding to brief, low risk in situ rights restrictions imposed by the researchers. Participant performance did not improve significantly or consistently from baseline to training on the questions asked about the videotaped or the role play scenarios, but two of three participants demonstrated defmite improvements in responding to in situ rights restrictions.

L-Fuzzy Relations in Coq

Heyting categories, a variant of Dedekind categories, and Arrow categories provide a convenient framework for expressing and reasoning about fuzzy relations and programs based on those methods. In this thesis we present an implementation of Heyting and arrow categories suitable for reasoning and program execution using Coq, an interactive theorem prover based on Higher-Order Logic (HOL) with dependent types. This implementation can be used to specify and develop correct software based on L-fuzzy relations such as fuzzy controllers. We give an overview of lattices, L-fuzzy relations, category theory and dependent type theory before describing our implementation. In addition, we provide examples of program executions based on our framework.

An Abstract Algebraic Theory of L-Fuzzy Relations for Relational Databases

Classical relational databases lack proper ways to manage certain real-world situations including imprecise or uncertain data. Fuzzy databases overcome this limitation by allowing each entry in the table to be a fuzzy set where each element of the corresponding domain is assigned a membership degree from the real interval [0…1]. But this fuzzy mechanism becomes inappropriate in modelling scenarios where data might be incomparable. Therefore, we become interested in further generalization of fuzzy database into L-fuzzy database. In such a database, the characteristic function for a fuzzy set maps to an arbitrary complete Brouwerian lattice L. From the query language perspectives, the language of fuzzy database, FSQL extends the regular Structured Query Language (SQL) by adding fuzzy specific constructions. In addition to that, L-fuzzy query language LFSQL introduces appropriate linguistic operations to define and manipulate inexact data in an L-fuzzy database. This research mainly focuses on defining the semantics of LFSQL. However, it requires an abstract algebraic theory which can be used to prove all the properties of, and operations on, L-fuzzy relations. In our study, we show that the theory of arrow categories forms a suitable framework for that. Therefore, we define the semantics of LFSQL in the abstract notion of an arrow category. In addition, we implement the operations of L-fuzzy relations in Haskell and develop a parser that translates algebraic expressions into our implementation.

L-Fuzzy Structured Query Language

Lattice valued fuzziness is more general than crispness or fuzziness based on the unit interval. In this work, we present a query language for a lattice based fuzzy database. We define a Lattice Fuzzy Structured Query Language (LFSQL) taking its membership values from an arbitrary lattice L. LFSQL can handle, manage and represent crisp values, linear ordered membership degrees and also allows membership degrees from lattices with non-comparable values. This gives richer membership degrees, and hence makes LFSQL more flexible than FSQL or SQL. In order to handle vagueness or imprecise information, every entry into an L-fuzzy database is an L-fuzzy set instead of crisp values. All of this makes LFSQL an ideal query language to handle imprecise data where some factors are non-comparable. After defining the syntax of the language formally, we provide its semantics using L-fuzzy sets and relations. The semantics can be used in future work to investigate concepts such as functional dependencies. Last but not least, we present a parser for LFSQL implemented in Haskell.