Karel Jezek

Place cells in the hippocampus: Eleven maps for eleven rooms

Significance The hippocampus is thought to store a large number of experiences that, despite their similarity, can be individually retrieved with minimal interference. Studies have shown that place cells in hippocampal area CA3 form statistically independent representations of pairs of environments. It has remained unclear, however, whether CA3 place cells maintain this independence when the number of environments is increased. We recorded activity from CA3 in 11 environments with nearly identical geometric features. Spatial firing patterns remained uncorrelated across all 55 pairs of environments, with minimal overlap in the populations of active cells. The data suggest that the capacity of the CA3 network is large and speak against extensive recurrence of spatial motifs across experiences. The contribution of hippocampal circuits to high-capacity episodic memory is often attributed to the large number of orthogonal activity patterns that may be stored in these networks. Evidence for high-capacity storage in the hippocampus is missing, however. When animals are tested in pairs of environments, different combinations of place cells are recruited, consistent with the notion of independent representations. However, the extent to which representations remain independent across larger numbers of environments has not been determined. To investigate whether spatial firing patterns recur when animals are exposed to multiple environments, we tested rats in 11 recording boxes, each in a different room, allowing for 55 comparisons of place maps in each animal. In each environment, activity was recorded from neuronal ensembles in hippocampal area CA3, with an average of 30 active cells per animal. Representations were highly correlated between repeated tests in the same room but remained orthogonal across all combinations of different rooms, with minimal overlap in the active cell samples from each environment. A low proportion of cells had activity in many rooms but the firing locations of these cells were completely uncorrelated. Taken together, the results suggest that the number of independent spatial representations stored in hippocampal area CA3 is large, with minimal recurrence of spatial firing patterns across environments.

Multilingual Plagiarism Detection

Multilingual text processing has been gaining more and more attention in recent years. This trend has been accentuated by the global integration of European states and the vanishing cultural and social boundaries. Multilingual text processing has become an important field bringing a lot of new and interesting problems. This paper describes a novel approach to multilingual plagiarism detection. We propose a new method called MLPlag for plagiarism detection in multilingual environment. This method is based on analysis of word positions. It utilizes the EuroWordNet thesaurus which transforms words into language independent form. This allows to identify documents plagiarized from sources written in other languages. Special techniques, such as semantic-based word normalization, were incorporated to refine our method. It identifies the replacement of synonyms used by plagiarists to hide the document match. We performed and evaluated our experiments on monolingual and multilingual corpora and results are presented in this paper.

Extending the single words-based document model: a comparison of bigrams and 2-itemsets

The basic approach in text categorization is to represent documents by single words. However, often other features are utilized to achieve better classification results. In this paper, our attention is focused on bigrams and 2-itemsets. We compare the performance improvement in terms of classification accuracy when these features are used to extend the single words-based document representation on two standard text corpora: Reuters-21578 and 20 Newsgroups. For this comparison we use the multinomial Naive Bayes classifier and five different feature selection approaches. Algorithms for bigrams and 2-itemsets discovery are presented as well. Our results show a statistically significant improvement when bigrams and also 2-itemsets are incorporated. However, in the case of 2-itemsets it is important to use an appropriate feature selection method. On the other hand, even when a simple feature selection approach is applied to discover bigrams the classification accuracy improves. The conclusion is that, in our case, it is not very effective to extend document representation with 2-itemsets because bigrams achieve better results and discovering them is less resource-consuming.

Theta-paced flickering between place-cell maps in the hippocampus: A model based on short-term synaptic plasticity

Hippocampal place cells represent different environments with distinct neural activity patterns. Following an abrupt switch between two familiar configurations of visual cues defining two environments, the hippocampal neural activity pattern switches almost immediately to the corresponding representation. Surprisingly, during a transient period following the switch to the new environment, occasional fast transitions between the two activity patterns (flickering) were observed (Jezek, Henriksen, Treves, Moser, & Moser, ). Here we show that an attractor neural network model of place cells with connections endowed with short‐term synaptic plasticity can account for this phenomenon. A memory trace of the recent history of network activity is maintained in the state of the synapses, allowing the network to temporarily reactivate the representation of the previous environment in the absence of the corresponding sensory cues. The model predicts that the number of flickering events depends on the amplitude of the ongoing theta rhythm and the distance between the current position of the animal and its position at the time of cue switching. We test these predictions with new analysis of experimental data. These results suggest a potential role of short‐term synaptic plasticity in recruiting the activity of different cell assemblies and in shaping hippocampal activity of behaving animals.

Automatic tagging based on linked data: Unsupervised methods for the extraction of hidden information

We have created a web agent for collecting Call for Papers (CFP) announcements. Our web agent obtains CFP announcements from websites or from mailbox. The most important information is extracted and published on our own special website in a user and machine readable way. One of the most important problems is event classification, categorization and clustering. In this paper we describe unsupervised methods for automatic tagging based on information extraction from Linked data. These methods are usable in situations where we have to tag unknown data and we have no corpus for learning methods. Tagged data can have the form of short messages from RSS, short blog posts or emails. The automatic tags can be used for classifying the conferences. Users can use our web service to search for interesting events and sort them by their own preferences. We obtain tags with their relationship parameters and we can use them for automatic clustering of collected events.

Pairwise graphical model for brain-state identification: application to decoding of spatial representations from hippocampal CA1 and CA3 recordings

Hippocampus can store spatial representations, or maps, which are recalled each time a subject is placed in the corresponding environment. We consider the problem of decoding the recalled maps as a function of time from multi-cellular recordings in CA1. Maps corresponding to different environments in CA1 mostly differ by changes in firing rates rather than firing fields, and are harder to identify than in CA3, in which maps are essentially orthogonal. We introduce a functional-connectivity-based decoder, which accounts for the pairwise correlations between the spiking activities of neurons in each map and does not require any positional information, i.e. any knowledge about place fields. We first show, on recordings of hippocampal activity in constant environmental conditions, that our decoder outperforms existing decoding methods in CA1. Our decoder is then applied to data from teleportation experiments, in which instantaneous switches between environmental conditions trigger the recall of the corresponding maps. We test the sensitivity of our approach on the transition dynamics between the respective memory states (maps). We find that the rate of spontaneous state shifts (flickering) after a teleportation event is increased not only within the first few seconds as already reported, but the network also shows a higher instability level on much longer (> 1 min) intervals.Hippocampus can store spatial representations, or maps, which are recalled each time a subject is placed in the corresponding environment. We consider the problem of decoding the recalled maps as a function of time from multi-cellular recordings. We introduce a graphical model-based decoder, which accounts for the pairwise correlations between the spiking activities of neurons and does not require any positional information, i.e. any knowledge about place fields. We first show, on recordings of hippocampal activity in constant environmental conditions, that our decoder efficiently decodes maps in CA3 and outperfoms existing methods in CA1, where maps are much less orthogonal. Our decoder is then applied to data from teleportation experiments, in which instantaneous switches between environmental conditions trigger the recall of the corresponding maps. We test the sensitivity of our approach on the transition dynamics between the respective memory states (maps). We find that the rate of spontaneous state shifts (flickering) after a teleportation event is increased not only within the first few seconds as already reported, but the network also shows a higher instability level for on much longer (> 1 min) intervals, both in CA3 and in CA1. In addition, we introduce an efficient Bayesian decoder of the rat full trajectory over time, and find that the animal location can be accurately predicted at all times, even during flickering events. Precise information about the animal position is thus always present in the neural activity, irrespectively of the dynamical shifts in the recalled maps.

Magic Sets Method with Fuzzy Logic

This paper describes a method of the efficient query evaluation when uncertainty is involved in a deductive database system. A deductive system enriched with fuzzy logic is able to serve better as a knowledge system. Speeding up its execution makes this system practically useful.