Neural coding is essential for understanding how the activity of individual neurons or ensembles of neurons relates to cognitive processing of the world. Neurons can code for multiple variables simultaneously and neuroscientists are interested in classifying neurons based on the variables they represent. Building a model identification paradigm to identify neurons in terms of their coding properties is essential to understanding how the brain processes information. Statistical paradigms are capable of methodologically determining the factors influencing neural observations and assessing the quality of the resulting models to characterize and classify individual neurons. However, as neural recording technologies develop to produce data from massive populations, classical statistical methods often lack the computational efficiency required to handle such data. Machine learning (ML) approaches are known for enabling efficient large scale data analysis; however, they require huge training data sets, and model assessment and interpretation are more challenging than for classical statistical methods. To address these challenges, we develop an integrated framework, combining statistical modeling and machine learning approaches to identify the coding properties of neurons from large populations. In order to evaluate our approaches, we apply them to data from a population of neurons in rat hippocampus and prefrontal cortex (PFC), to characterize how spatial learning and memory processes are represented in these areas. The data consist of local field potentials (LFP) and spiking data simultaneously recorded from the CA1 region of hippocampus and the PFC of a male Long Evans rat performing a spatial alternation task on a W-shaped track. We have examined this data in three separate but related projects. In one project, we build an improved class of statistical models for neural activity by expanding a common set of basis functions to increase the statistical power of the resulting models. In the second project, we identify the individual neurons in hippocampus and PFC and classify them based on their coding properties by using statistical model identification methods. We found that a substantial proportion of hippocampus and PFC cells are spatially selective, with position and velocity coding, and rhythmic firing properties. These methods identified clear differences between hippocampal and prefrontal populations, and allowed us to classify the coding properties of the full population of neurons in these two regions. For the third project, we develop a supervised machine learning classifier based on convolutional neural networks (CNNs), which use classification results from statistical models and additional simulated data as ground truth signals for training. This integration of statistical and ML approaches allows for statistically principled and computationally efficient classification of the coding properties of general neural populations.

Complex spatial relationships and patterns in multivariate data are relatively simple to identify visually but di cult to detect computation- ally. For this reason, Anivis, an interactive tool for visual exploration of multivariate quantitative pure serial periodic data was developed. The data has four dimensions depth, laterality, frequency and time. The data was visualized as an animated heatmap, by mapping depth and laterality to coordinates in a pixel grid and frequency to color. Transfer functions were devised to map a single variable to color through parametric curves. Anivis implemented heatmap generation through both weighted sum and deconvolution for comparison reasons. Deconvolution exhibited a to have better theoretical and practical performance. In addition to the heatmap visualization a scatter-plot was added in order to visualize the causal relationships between data points and high value areas in the heatmap visualization. Performance and applicability of the tool were tested and verified on experimental data from the Karolinksa Institute’s Department of Neuroscience. / Komplexa spatiala mo ̈nster och fo ̈rh ̊allanden i multivariat data a ̈r rel- ativt sv ̊ara att identifiera via bera ̈kningar men simpla att identifiera vi- suellt. Att visualisera data fo ̈r denna typ av data-analys anva ̈nds ofta i m ̊anga olika typer av fa ̈lt. Detta motiverade utvecklingen av Anivis; ett interaktivt verktyg fo ̈r visuell utforskning av multivariat kvantitativ data av neural aktivitet. Anivis anva ̈nder sig av dataset baserade p ̊a experi- mentell data fr ̊an en forskningsgrupp p ̊a Karolinska Institutets Institution fo ̈r Neurovetenskap. Dessa fyrdimensionella dataset best ̊ar av ma ̈tningar fr ̊an neuroner i form av deras position, aktivitet i form av frekvens och tidpunkt. Denna data anva ̈nds fo ̈r att generera en animerad heatmap, da ̈r neuroners frekvensva ̈rden visas i form av f ̈arg. Frekvensva ̈rdena om- vandlades till fa ̈rgva ̈rden via ̈overg ̊angsfunktioner som kopplar numeriska va ̈rden till fa ̈rgva ̈rden via parametriserade kurvor. Anivis lyckades imple- mentera tv ̊a olika metoder f ̈or att generera heatmap, viktade summor och dekonvolution. Dessa tv ̊a metoder ja ̈mfo ̈rdes med varandra, varav dekon- volution visade sig vara den teoretiskt och praktiskt e↵ektivaste meto- den. Utvecklingen av Anivis visade a ̈ven behovet fo ̈r ett punktdiagram fo ̈r att visualisera f ̈orh ̊allandet mellan ma ̈tta frekvensv ̈arden och spatial frekvensfo ̈rdelning i heatmappen.

Este trabalho descreve as pesquisas e desenvolvimentos em instrumentação eletrônica computacional, realizados para viabilizar experiências na área de neurobiofísica, tendo como objetivos principais a geração de estímulos visuais para invertebrados e a captação de sinais eletrofisiológicos gerados por sistemas biológicos sensoriais submetidos a estímulos. Trata-se de um conjunto de equipamentos que, operando de maneira integrada, são capazes de fornecer e sincronizar estímulos, realizar a aquisição dos dados de sinais neurais a serem utilizados para controle e análise em experiências in vivo\" nos estudos da visão de invertebrados no Laboratório de Neurobiofísica - DipteraLab do IFSC. A integração desta instrumentação eletrônica visa facilitar a sua utilização durante os experimentos, permitindo o acompanhamento das aquisições de dados neurais, viabilizando a realização de experimentos com alterações dos estímulos através de realimentação em tempo real. / This work describes the research and development of computational instrumentation to be used in experimental neurobiophysics. The developed electronic modules operate in an integrated manner and are used to generate visual stimuli for invertebrates and capture electrophysiological signals generated by biological systems subjected to sensory stimuli. They are able to provide synchronized stimuli and perform data acquisition of neural signals events to be used for control and analysis of vision experiments with invertebrates at the Laboratory of Neurobiophysics Dipteralab Laboratory, at the IFSC. The integration of electronic instrumentation facilitate its use during experiments allowing, through its monitoring capabilities of the neural data acquisition, the realization of experiments with real time stimuli changes through feedback. The possibility to perform pre-analyses of neural responses in behavioral closed loop experiments is also implemented.

Este trabalho descreve as pesquisas e desenvolvimentos em instrumentação eletrônica computacional, realizados para viabilizar experiências na área de neurobiofísica, tendo como objetivos principais a geração de estímulos visuais para invertebrados e a captação de sinais eletrofisiológicos gerados por sistemas biológicos sensoriais submetidos a estímulos. Trata-se de um conjunto de equipamentos que, operando de maneira integrada, são capazes de fornecer e sincronizar estímulos, realizar a aquisição dos dados de sinais neurais a serem utilizados para controle e análise em experiências in vivo\" nos estudos da visão de invertebrados no Laboratório de Neurobiofísica - DipteraLab do IFSC. A integração desta instrumentação eletrônica visa facilitar a sua utilização durante os experimentos, permitindo o acompanhamento das aquisições de dados neurais, viabilizando a realização de experimentos com alterações dos estímulos através de realimentação em tempo real. / This work describes the research and development of computational instrumentation to be used in experimental neurobiophysics. The developed electronic modules operate in an integrated manner and are used to generate visual stimuli for invertebrates and capture electrophysiological signals generated by biological systems subjected to sensory stimuli. They are able to provide synchronized stimuli and perform data acquisition of neural signals events to be used for control and analysis of vision experiments with invertebrates at the Laboratory of Neurobiophysics Dipteralab Laboratory, at the IFSC. The integration of electronic instrumentation facilitate its use during experiments allowing, through its monitoring capabilities of the neural data acquisition, the realization of experiments with real time stimuli changes through feedback. The possibility to perform pre-analyses of neural responses in behavioral closed loop experiments is also implemented.