Learning is central to infusing intelligence to any biologically inspired system. This study introduces a novel Cross-Correlated Delay Shift (CCDS) learning method for spiking neurons with the ability to learn and reproduce arbitrary spike patterns in a supervised fashion with applicability tospatiotemporalinformation encoded at the precise timing of spikes. By integrating the cross-correlated term,axonaland synapse delays, the CCDS rule is proven to be both biologically plausible and computationally efficient. The proposed learning algorithm is evaluated in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. The results indicate that the proposed CCDS learning rule greatly improves classification accuracy when compared to the standards reached with the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. Network structureis the crucial partforany application domain of Artificial Spiking Neural Network (ASNN). Thus, temporal learning rules in multilayer spiking neural networks are investigated. As extensions of single-layer learning rules, the multilayer CCDS (MutCCDS) is also developed. Correlated neurons are connected through fine-tuned weights and delays. In contrast to the multilayer Remote Supervised Method (MutReSuMe) and multilayertempotronrule (MutTmptr), the newly developed MutCCDS shows better generalization ability and faster convergence. The proposed multilayer rules provide an efficient and biologically plausible mechanism, describing how delays and synapses in the multilayer networks are adjusted to facilitate learning. Interictalspikes (IS) aremorphologicallydefined brief events observed in electroencephalography (EEG) records from patients with epilepsy. The detection of IS remains an essential task for 3D source localization as well as in developing algorithms for seizure prediction and guided therapy. In this work, we present a new IS detection method using the Wavelet Encoding Device (WED) method together with CCDS learning rule and a specially designed Spiking Neural Network (SNN) structure. The results confirm the ability of such SNN to achieve good performance for automatically detecting such events from multichannel EEG records.

Assessing complex brain activity as a function of the type of epilepsy and in the context of the 3D source of seizure onset remains a critical and challenging endeavor. In this dissertation, we tried to extract the attributes of the epileptic brain by looking at the modular interactions from scalp electroencephalography (EEG). A classification algorithm is proposed for the connectivity-based separation of interictal epileptic EEG from normal. Connectivity patterns of interictal epileptic discharges were investigated in different types of epilepsy, and the relation between patterns and the epileptogenic zone are also explored in focal epilepsy. A nonlinear recurrence-based method is applied to scalp EEG recordings to obtain connectivity maps using phase synchronization attributes. The pairwise connectivity measure is obtained from time domain data without any conversion to the frequency domain. The phase coupling value, which indicates the broadband interdependence of input data, is utilized for the graph theory interpretation of local and global assessment of connectivity activities. The method is applied to the population of pediatric individuals to delineate the epileptic cases from normal controls. A probabilistic approach proved a significant difference between the two groups by successfully separating the individuals with an accuracy of 92.8%. The investigation of connectivity patterns of the interictal epileptic discharges (IED), which were originated from focal and generalized seizures, was resulted in a significant difference ( ) in connectivity matrices. It was observed that the functional connectivity maps of focal IED showed local activities while generalized cases showed global activated areas. The investigation of connectivity maps that resulted from temporal lobe epilepsy individuals has shown the temporal and frontal areas as the most affected regions. In general, functional connectivity measures are considered higher order attributes that helped the delineation of epileptic individuals in the classification process. The functional connectivity patterns of interictal activities can hence serve as indicators of the seizure type and also specify the irritated regions in focal epilepsy. These findings can indeed enhance the diagnosis process in context to the type of epilepsy and effects of relative location of the 3D source of seizure onset on other brain areas.

Made available in DSpace on 2014-12-17T15:28:53Z (GMT). No. of bitstreams: 1 AndersonBS_DISSERT.pdf: 4240084 bytes, checksum: 0331343a1aab5e54d0d9cb6baeccb72d (MD5) Previous issue date: 2013-12-13 / Epilepsies are neurological disorders characterized by recurrent and spontaneous seizures due to an abnormal electric activity in a brain network. The mesial temporal lobe epilepsy (MTLE) is the most prevalent type of epilepsy in adulthood, and it occurs frequently in association with hippocampal sclerosis. Unfortunately, not all patients benefit from pharmacological treatment (drug-resistant patients), and therefore become candidates for surgery, a procedure of high complexity and cost. Nowadays, the most common surgery is the anterior temporal lobectomy with selective amygdalohippocampectomy, a procedure standardized by anatomical markers. However, part of patients still present seizure after the procedure. Then, to increase the efficiency of this kind of procedure, it is fundamental to know the epileptic human brain in order to create new tools for auxiliary an individualized surgery procedure. The aim of this work was to identify and quantify the occurrence of epilepticform activity -such as interictal spikes (IS) and high frequency oscillations (HFO) - in electrocorticographic (ECoG) signals acutely recorded during the surgery procedure in drug-resistant patients with MTLE. The ECoG recording (32 channels at sample rate of 1 kHz) was performed in the surface of temporal lobe in three moments: without any cortical resection, after anterior temporal lobectomy and after amygdalohippocampectomy (mean duration of each record: 10 min; N = 17 patients; ethic approval #1038/03 in Research Ethic Committee of Federal University of S?o Paulo). The occurrence of IS and HFO was quantified automatically by MATLAB routines and validated manually. The events rate (number of events/channels) in each recording time was correlated with seizure control outcome. In 8 hours and 40 minutes of record, we identified 36,858 IS and 1.756 HFO. We observed that seizure-free outcome patients had more HFO rate before the resection than non-seizure free, however do not differentiate in relation of frequency, morphology and distribution of IS. The HFO rate in the first record was better than IS rate on prediction of seizure-free patients (IS: AUC = 57%, Sens = 70%, Spec = 71% vs HFO: AUC = 77%, Sens = 100%, Spec = 70%). We observed the same for the difference of the rate of pre and post-resection (IS: AUC = 54%, Sens = 60%, Spec = 71%; vs HFO: AUC = 84%, Sens = 100%, Spec = 80%). In this case, the algorithm identifies all seizure-free patients (N = 7) with two false positives. To conclude, we observed that the IS and HFO can be found in intra-operative ECoG record, despite the anesthesia and the short time of record. The possibility to classify the patients before any cortical resection suggest that ECoG can be important to decide the use of adjuvant pharmacological treatment or to change for tailored resection procedure. The mechanism responsible for this effect is still unknown, thus more studies are necessary to clarify the processes related to it / As epilepsias s?o dist?rbios neurol?gicos caracterizados por crises espont?neas e recorrentes, resultantes de uma atividade el?trica anormal de uma rede neural. Dentre os diferentes tipos de epilepsia, a epilepsia mesial do lobo temporal (EMLT) ? a mais observada em adultos, sendo frequentemente associada ? esclerose hipocampal. Infelizmente, nem todos os pacientes s?o beneficiados pelo tratamento farmacol?gico (pacientes f?rmaco-resistentes). Para estes sujeitos, uma alternativa ? a realiza??o de cirurgia, um procedimento de alta complexidade e elevado custo. Atualmente, o procedimento mais realizado ? a lobectomia temporal anterior com amigdalo-hipocampectomia seletiva, uma cirurgia padronizada por marcos anat?micos. Entretanto, uma parcela dos pacientes continua a apresentar crises incapacitantes ap?s o tratamento cir?rgico. Desta forma, para aumentar a efici?ncia deste tipo de tratamento, ? fundamental a compreens?o do enc?falo humano epil?ptico com vistas a se criar ferramentas que auxiliem na realiza??o de procedimentos individualizados. O objetivo do presente trabalho foi identificar e quantificar a ocorr?ncia de atividade epileptiforme - esp?culas interictais (EI) e oscila??es de alta frequ?ncia (OAF) - em registros eletrocorticogr?ficos (ECoG) realizados durante procedimento cir?rgico em pacientes com EMLT refrat?ria ao tratamento farmacol?gico. Registros ECoG (32 canais a uma taxa de amostragem de 1 kHz) foram realizados na superf?cie do lobo temporal em 3 momentos cir?rgicos: no c?rtex intacto, ap?s lobectomia temporal anterior e ap?s amigdalo-hipocampectomia (dura??o m?dia de cada um desses registros: 10 min; N=17 pacientes). A ocorr?ncia de EI e OAF foi quantificada automatica-mente, por meio de rotinas em MATLAB, e validadas manualmente. A taxa de ocorr?ncia em cada um dos tempos cir?rgicos foi correlacionada com o resultado cir?rgico quanto ao controle das crises, num seguimento de 2 anos. De um total de 8 h e 40 min de registro, identificamos 36.858 EI e 1.756 OAF. Observamos que os pacientes que ficaram livres de crises no p?s-operat?rio apresentaram maior quanti-dade de OAF antes da cirurgia do que aqueles que continuaram a ter crises; por?m, n?o diferiram quanto a frequ?ncia, morfologia e distribui??o de EI. A ocorr?ncia de OAF no registro basal apresentou melhor desempenho que as EI na previs?o do controle total das crises no p?s-operat?rio (EI: AUC = 57%, S = 71% , E = 70% vs OAF: AUC = 77%, S = 100%, E=70%). O mesmo foi observado com a varia??o da ocorr?ncia entre os momentos pr?- e p?s-ressec??o (EI: AUC = 54%, S = 71%, E = 60% vs OAF: AUC = 84%, S = 100%, E = 80%). Nesse caso, o classificador foi capaz de identificar todos os pacientes livres de crises (N = 7) , apresentando apenas dois falsos positivos. Desta forma, podemos concluir que as OAF, juntamente com as EI, podem ser encontradas no registro ECoG intra-operat?rio, mesmo na presen?a de anest?sicos e em uma curta sess?o de registro. Al?m disso, a observa??o de que a ocorr?ncia desses eventos no in?cio da cirurgia permite classificar o paciente quanto ao progn?stico cir?rgico abre caminho para aplicar o ECoG intra-operat?rio, por exemplo, na decis?o sobre o uso de tratamento farmacol?gico adjuvante ou da convers?o para ressec??es individualizadas. No entanto, o mecanismo respons?vel por esse efeito ainda ? desconhecido, logo novos estudos s?o necess?rios para melhor esclarec?-lo