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Activity and Memory in Biologically Motivated Neural Network.
Štroffek, Július ; Maršálek, Petr (advisor) ; Zápotocký, Martin (referee) ; Hozman, Jiří (referee)
This work presents biologically motivated neural network model which works as an auto-associative memory. Architecture of the presented model is similar to the architecture of the Hopfield network which might be similar to some parts of the hippocampal network area CA3 (Cornu Amonis). Patterns learned and retrieved are not static but they are periodically repeating sequences of sparse synchronous activities. Patterns were stored to the network using the modified Hebb rule adjusted to store cyclic sequences. Capacity of the model is analyzed together with the numerical simulations. The model is further extended with short term potentiation (STP), which is forming the essential part of the successful pattern recall process. The memory capacity of the extended version of the model is highly increased. The joint version of the model combining both approaches is discussed. The model might be able to retrieve the pattern in short time interval without STP (fast patterns) or in a longer time period utilizing STP (slow patterns). We know from our everyday life that some patterns could be recalled promptly and some may need much longer time to reveal. Keywords auto-associative neural network, Hebbian learning, neural coding, memory, pattern recognition, short-term potentiation 1
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Implementace paralelního zpracování dotazů v databázovém systému PostgreSQL
Vojtek, Daniel ; Štroffek, Julius (advisor) ; Bednárek, David (referee)
CONTENTS vi Title: Implementation of parallel query processing in PostgreSQL Author: Bc. Daniel Vojtek Department: Department of Software Engineering Supervisor: Mgr. Július Štroffek Supervisor's e-mail address: julo@stroffek.cz Abstract: Parallel query processing can help with processing of huge amounts of data stored in database systems. The aim of this diploma the- sis was to explore the possibilities, analyze, design and finally implement parallel query processing in open source database system PostgreSQL. I used a Master/Worker design pattern, in which standard PostgreSQL backend process is a master. As workers I used processes created from postmaster. In the thesis I focused on preparing an infrastructure nec- essary for parallel processing. I defined a new top level memory context over shared memory, which allows efficient and convenient memory al- locations. Then I implemented creation of new worker processes, based on master process requirements. To be able to control these workers I defined controlling structures using state machines. Then I implemented parallel sort operation and SQL operator UNION ALL using this infras- tructure. The result of this diploma thesis is not only implementation of infrastructure and some parallel operations, but also description of the problems encountered during the...
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