LEADER 03637nam 2200457z- 450 001 9910227345903321 005 20210211 035 $a(CKB)4100000000883874 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/50002 035 $a(oapen)doab50002 035 $a(EXLCZ)994100000000883874 100 $a20202102d2017 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aImpact of Diet on Learning, Memory and Cognition 210 $cFrontiers Media SA$d2017 215 $a1 online resource (117 p.) 225 1 $aFrontiers Research Topics 311 08$a2-88945-228-X 330 $aChanges in food composition and availability have contributed to the dramatic increase in obesity over the past 30-40 years in developed and, increasingly, in developing countries. The modern diet now contains many foods that are rich in saturated fat and refined sugar. People who eat excessive amounts of this diet are not only likely to become overweight, even obese, develop metabolic and cardiovascular diseases, some forms of cancer, but also undergo a more rapid rate of normal age-related cognitive decline and more rapid progression of neurological diseases such as dementia. A central problem is why people persist in consuming this diet in spite of its adverse health effects and when alternative food choices are available. As high fat / high sugar foods are inherently rewarding, eating for pleasure, like taking psychoactive drugs, can modulate reward neurocircuitry, causing changes in responsiveness to reward-predicting stimuli and incentive motivation. Indeed, the excessive ingestion in modern societies and the resulting obesity epidemic may be viewed as a form of food addiction. Thus, a diet high in palatable foods is proposed to impact upon reward systems in the brain, modulating appetitive learning and altering reward thresholds. Impairments in other forms of cognition have been associated with obesity, and these have a rapid onset. The hippocampus appears to be particularly vulnerable to the detrimental effects of high fat and high sugar diets. Recent research has shown that as little as one week of exposure to a high fat, high sugar diet leads to impairments in place but not object recognition memory in the rat. Excess sugar alone had similar effects, and the detrimental effects of diet consumption was linked to increased inflammatory markers in the hippocampus, a critical region involved in memory. Furthermore, obesity-related inflammatory changes have also been described in the human brain that may lead to memory impairments. These memory deficits may contribute to pathological eating behaviour through changes in the amount consumed and timing of eating. The aim of this eBook is to present up-to-date information about the impact of diet and diet-induced obesity on reward driven learning, memory and cognition, encompassing both animal and human literature, and also potential therapeutic targets to attenuate such deficits. 606 $aNeurosciences$2bicssc 610 $aBehavior 610 $aCognition 610 $aDiet 610 $aFamine 610 $aFat 610 $aMemory 610 $aNeurodevelopment 610 $aObesity 610 $aSugar 615 7$aNeurosciences 700 $aAmy Claire Reichelt$4auth$01301442 702 $aR. Fred Westbrook$4auth 702 $aMargaret J. Morris$4auth 906 $aBOOK 912 $a9910227345903321 996 $aImpact of Diet on Learning, Memory and Cognition$93025854 997 $aUNINA LEADER 04979nam 22008535 450 001 9910767575603321 005 20251226195902.0 010 $a3-540-78469-1 024 7 $a10.1007/978-3-540-78469-2 035 $a(CKB)1000000000490667 035 $a(SSID)ssj0000318231 035 $a(PQKBManifestationID)11226176 035 $a(PQKBTitleCode)TC0000318231 035 $a(PQKBWorkID)10308176 035 $a(PQKB)11615035 035 $a(DE-He213)978-3-540-78469-2 035 $a(MiAaPQ)EBC3068708 035 $a(PPN)123743796 035 $a(Au-PeEL)EBL3068708 035 $a(CaPaEBR)ebr10533868 035 $a(CaONFJC)MIL134312 035 $a(OCoLC)233973960 035 $a(EXLCZ)991000000000490667 100 $a20100301d2008 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aInductive Logic Programming $e17th International Conference, ILP 2007, Corvallis, OR, USA, June 19-21, 2007, Revised Selected Papers /$fedited by Hendrik Blockeel, Jan Ramon, Jude Shavlik, Prasad Tadepalli 205 $a1st ed. 2008. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2008. 215 $a1 online resource (XI, 307 p.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v4894 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a3-540-78468-3 320 $aIncludes bibliographical references and index. 327 $aInvited Talks -- Learning with Kernels and Logical Representations -- Beyond Prediction: Directions for Probabilistic and Relational Learning -- Extended Abstracts -- Learning Probabilistic Logic Models from Probabilistic Examples (Extended Abstract) -- Learning Directed Probabilistic Logical Models Using Ordering-Search -- Learning to Assign Degrees of Belief in Relational Domains -- Bias/Variance Analysis for Relational Domains -- Full Papers -- Induction of Optimal Semantic Semi-distances for Clausal Knowledge Bases -- Clustering Relational Data Based on Randomized Propositionalization -- Structural Statistical Software Testing with Active Learning in a Graph -- Learning Declarative Bias -- ILP :- Just Trie It -- Learning Relational Options for Inductive Transfer in Relational Reinforcement Learning -- Empirical Comparison of ?Hard? and ?Soft? Label Propagation for Relational Classification -- A Phase Transition-Based Perspective on Multiple Instance Kernels -- Combining Clauses with Various Precisions and Recalls to Produce Accurate Probabilistic Estimates -- Applying Inductive Logic Programming to Process Mining -- A Refinement Operator Based Learning Algorithm for the Description Logic -- Foundations of Refinement Operators for Description Logics -- A Relational Hierarchical Model for Decision-Theoretic Assistance -- Using Bayesian Networks to Direct Stochastic Search in Inductive Logic Programming -- Revising First-Order Logic Theories from Examples Through Stochastic Local Search -- Using ILP to Construct Features for Information Extraction from Semi-structured Text -- Mode-Directed Inverse Entailment for Full Clausal Theories -- Mining of Frequent Block Preserving Outerplanar Graph Structured Patterns -- Relational Macros for Transfer in Reinforcement Learning -- Seeing theForest Through the Trees -- Building Relational World Models for Reinforcement Learning -- An Inductive Learning System for XML Documents. 330 $aThis book contains the post-conference proceedings of the 17th International Conference on Inductive Logic Programming. It covers current topics in inductive logic programming, from theoretical and methodological issues to advanced applications. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v4894 606 $aArtificial intelligence 606 $aSoftware engineering 606 $aComputer programming 606 $aMachine theory 606 $aAlgorithms 606 $aData mining 606 $aArtificial Intelligence 606 $aSoftware Engineering 606 $aProgramming Techniques 606 $aFormal Languages and Automata Theory 606 $aAlgorithms 606 $aData Mining and Knowledge Discovery 615 0$aArtificial intelligence. 615 0$aSoftware engineering. 615 0$aComputer programming. 615 0$aMachine theory. 615 0$aAlgorithms. 615 0$aData mining. 615 14$aArtificial Intelligence. 615 24$aSoftware Engineering. 615 24$aProgramming Techniques. 615 24$aFormal Languages and Automata Theory. 615 24$aAlgorithms. 615 24$aData Mining and Knowledge Discovery. 676 $a005.1/5 701 $aBlockeel$b Hendrik$01759891 712 12$aILP 2007 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910767575603321 996 $aInductive logic programming$94198563 997 $aUNINA