Events

Dissertation Defense

Presenter:

Ali Hummos

Date:

12-05-2017

Time:

2:00PM-3:00PM

Location:

2206C Student Center

MODELING THE HIPPOCAMUS: FINELY CONTROLLED MEMORY STORAGE USING SPIKING NEURONS

The hippocampus, an area in the temporal lobe of the mammalian brain, participates in the storage of personal memories and life events, including traumatic memories and the consequent symptoms of post-traumatic stress, giving importance to the study of the machinery of hippocampal memory storage and retrieval. The circuit is known to be controlled by the neuromodulator Acetylcholine, which switches the circuit between the memory storage state and the memory retrieval state.

We built a computational model of the hippocampus with the ability to perform both memory storage and retrieval functions, controlled by the level of Acetylcholine. This functional separation decrease interference between the two circuit functions while sharing the same physical implementation of a network of spiking neurons.

We discovered three important differences between the storage and retrieval circuits. First, they had difference in how they produced runaway excitation, an aberrant spread of brain activity leading to seizures. Second, the two circuits had distinct mechanisms to maintain control over runaway excitation spread. These two findings provided the first classification of seizures based on the functional state of the brain, and suggested the need for specific treatments for each type.

Third, we found the two circuits also had unique ways of generating theta rhythmic activity, which is theorized to have a fundamental role in memory storage and retrieval. Our model uncovered an unexpected complexity in theta rhythm generation across functional states of the circuit. These findings can allow for deciphering the computations carried out by the circuit, based on the engaged mechanisms of rhythm generation.

Seminar Series

Presenter:

Pericles Giannaris

Date:

12-04-2017

Time:

11:00am-12:00pm

Location:

2206A Student Center

Use of the N-ary Relational Schema to Atomize Compound Relational Triples

Electronic medical records document health information in structured format and in unstructured free text format.  Health information in structured format contains laboratory results, vital signs, patient demographics etc.  The unstructured free text is the prime source of healthcare information documenting providers’ interpretations of health conditions, diagnoses, medical interventions, impressions, etc.  In order to uncover unknown information and search for patterns in health data with computational methods, we need to structure the unstructured free text data.  For that, we use information extraction, a computational technique for analyzing free text and deriving structured information.  Extracted information from free text can be represented in the form of relational triples.  Relational triples are statements of a single fact composed of subject-relation-object.  These triple statements allow the development of knowledge bases, knowledge graphs or the application of inference rules.  In our research, we employ Stanford’s CoreNLP engine for information extraction in triple format.  This format helps us to develop Resource Description Framework (RDF) networks where each subject and object become nodes and the edges represent the relations between the nodes. However, most of the triples produced by CoreNLP convey multiple facts (compound triple), instead of a single fact (atomic triple). Compound triples produce networks with nodes representing multiple entities instead of a single entity causing issues of network representation of our data. Here, we extend the use of CoreNLP to atomize compound triples.  Our approach is based on the N-ary relational schema that links an individual to multiple individuals or values. Our approach includes triple decomposition and ontological modeling.

Seminar Series

Presenter:

Hung Nguyen

Date:

11-06-2017

Time:

11:00AM-12:00PM

Location:

2206A Student Center

Investigating genome composition in multiple bee species

The honey bee Apis mellifera was the first eusocial animal to have its genome assembled. Analysis of the complete draft sequence of the honey bee genome revealed several interesting features compared with the other metazoan genomes: a low but heterogeneous GC content, an overabundance of CpG dinucleotides and a lack of repetitive elements. The average GC content of the honey bee genome is only 33%, but GC content is highly heterogeneous, ranging from 11% to 67%, with a bimodal distribution. Furthermore, unlike genes in most other metazoans, honey bee genes are overly abundant in regions of low GC content (<30%). Some studies have suggested that the high GC-content regions of the honey bee genome are associated with areas of high meiotic recombination rates; indeed the honey bee exhibits the highest known recombination rate among eukaryotes. Other studies have suggested that honey bee genome nucleotide composition is associated with DNA methylation, which occurs at a low frequency at CpG sites within exons. However, reasons for the highly heterogeneous base composition are not well understood, and whether any of the unusual genome features are related to the emergence of eusociality in bees is not known. Since the publication of the honey bee genome, genomes of several other bee species have become available. I am investigating the composition and organization of genomes of multiple bee species with different levels of social complexity to identify features that are unique to eusocial bees. Results of this exploratory analysis will allow me to develop a hypothesis about the relationship of genome composition to the evolution of eusociality.

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