Spike Rate and Spike Timing Contributions to Coding Taste Quality Information in Rat Periphery

There is emerging evidence that individual sensory neurons in the rodent brain rely on temporal features of the discharge pattern to code differences in taste quality information. In contrast, investigations of individual sensory neurons in the periphery have focused on analysis of spike rate and mostly disregarded spike timing as a taste quality coding mechanism. The purpose of this work was to determine the contribution of spike timing to taste quality coding by rat geniculate ganglion neurons using computational methods that have been applied successfully in other systems. We recorded the discharge patterns of narrowly tuned and broadly tuned neurons in the rat geniculate ganglion to representatives of the five basic taste qualities. We used mutual information to determine significant responses and the van Rossum metric to characterize their temporal features. While our findings show that spike timing contributes a significant part of the message, spike rate contributes the largest portion of the message relayed by afferent neurons from rat fungiform taste buds to the brain. Thus, spike rate and spike timing together are more effective than spike rate alone in coding stimulus quality information to a single basic taste in the periphery for both narrowly tuned specialist and broadly tuned generalist neurons

Physiological role of PRRT2 and its involvement in the pathogenesis of paroxysmal disorders

Mutations in the PRoline-Rich Transmembrane protein 2 gene (PRRT2) underlie a heterogeneous group of paroxysmal disorders encompassing infantile epilepsy, paroxysmal kinesigenic dyskinesia, a combination of these phenotypes and migraine. For the majority of the pathogenic PRRT2 variants, the mutant proteins are not expressed or not correctly targeted to the plasma membrane, resulting in a loss-of function mechanism for PRRT2-related diseases. PRRT2 is a neuron-specific, type II transmembrane protein of 340 amino acids with an important functional role in synapse formation and maintenance, as well as in the regulation of fast neurotransmitter release at both glutamatergic and GABAergic terminals. The PRRT2 knock-out (PRRT2-KO) mouse, in which PRRT2 has been constitutively inactivated, displays alterations in brain structure and a sharp paroxysmal phenotype, reminiscent of the most common clinical manifestations of the human PRRT2-linked diseases. To gain further insights on the pathogenic role of PRRT2 deficiency, I used Multi-Electrode Arrays (MEAs) to characterize neuronal activity generated by primary hippocampal cultures obtained from the PRRT2-KO mouse embryos and to assess the epileptic propensity of cortico-hippocampal slices obtained from the same animal model. This experimental approach revealed a state of heightened spontaneous activity, hyper-synchronization in population bursts of action potentials (APs) and enhanced responsiveness to external stimuli in mutant networks. A complex interplay between (i) a synaptic phenotype, with weakened spontaneous transmission and increased short-term facilitation, and (ii) a marked increase in intrinsic excitability of excitatory neurons as assessed by single-cell electrophysiology, upholds this network phenotype. Furthermore, our group has generated cortical neurons from induced pluripotent stem cells (iPSCs) derived from heterozygous and homozygous siblings carrying the most common C.649dupC mutation. Patch-clamp recordings in neurons from homozygous patients showed an increased Na+ current that was fully rescued by expression of exogenous wild-type PRRT2. A strikingly similar electrophysiological phenotype was observed in excitatory primary cortical neurons from the PRRT2-KO mouse, which was accompanied by an increased length of the axon initial segment (AIS). At the network level, mutant cortical neurons grown on MEAs also displayed a state of spontaneous and evoked hyper-excitability and elevated propensity to synchronize their activity in network bursting events

The paroxysmal disorder gene PRRT2 downregulates NaV channels and neuronal excitability in human neurons

Proline-Rich Transmembrane Protein 2 (PRRT2) has been identified as the single causative gene for a group of paroxysmal syndromes, including benign familial infantile seizures, paroxysmal kinesigenic dyskinesia and migraine. Most of the mutations of this gene lead to a premature stop codon, generating an unstable form of mRNA or a truncated protein that is degraded, pointing out the loss of the PRRT2 function as pathogenic mechanism of action. In this thesis, we have used different approaches to investigate the pathophysiological function of PRRT2. An important role for PRRT2 in the neurotransmitter release machinery, brain development and synapse formation has been uncovered by a previous work performed in our laboratory by acute silencing of PRRT2 expression. Here, we analyzed the phenotype of primary hippocampal neurons obtained from mouse PRRT2 knockout (KO) embryos. Analysis of synaptic function in primary neurons obtained from PRRT2-KO showed a largely similar, albeit attenuated, synaptic phenotype with respect to acute PRRT2 silencing characterized by weakened spontaneous/evoked synaptic transmission and increased facilitation at excitatory synapses. These effects were accompanied by a strengthened inhibitory transmission that, however, displayed faster synaptic depression. At the network level, these synaptic phenotypes, resulted in a state of increased spontaneous and evoked neurotransmitter release with increased excitability of excitatory neurons. To better dissect the physiological role of PRRT2, we characterized the phenotypes of neurons differentiated from Induced Pluripotent Stem Cells (iPSCs) from patients homozygous for the PRRT2 c.649dupC mutation. Hence, we observed an increased Na+ current and firing activity in iPSCs rescued with the re-expression of the human wild-type form of PRRT2. By use of heterologous expression system, we demonstrate that PRRT2 interacts with NaV1.2/NaV1.6, but not with NaV1.1 channels, modulating their membrane exposure and decreasing their conductances. In brief, our findings highlighted that PRRT2 mutations might be a negative modulator of NaV1.2/NaV1.6 channels and point out the critical role of this protein in the regulation of the neuronal network functionality

Contour Integration via Cortical Interactions in Visual Cortex

The visual system possesses a remarkable ability to group fragmented line segments into coherent contours and to segregate them from background. This process, known as contour integration, is critical to identifying object boundaries in complex visual scenes, and thus particularly important for performing shape discrimination, image segmentation and ultimately object recognition. Current evidence supports the idea that long-range horizontal connections in early visual cortex contribute to the process of contour integration, but the underling cortical circuitry, particularly the top-down feedback influence from higher visual areas, is not fully understood. Throughout the thesis, we took computational approaches to systematically examine how contour information is represented across the network of cortical areas and the circuitry by which this information is encoded. Three closely related projects, each having new methods development and hypothesis testing, were performed to analyze and interpret a very large set of neural data. The data set consists of recently acquired multi-electrode multi-unit spikes and local field potentials (LFPs) simultaneously recorded in visual areas V1 and V4 of monkeys performing a visual contour detection task. In the first project, well-established Granger causality measure was extended to the analysis of spiking trains data, which enabled us to quantify the causal interactions within and between areas V1 and V4. Our findings provided clear evidence that there is a top-down V4 feedback influence upon early visual area V1 during contour integration. In the second project, we investigated whether the contour signals in V1 are derived from feedback inputs alone, or whether they are mediated by an intimate interaction between feedback and horizontal connections within V1. Conditional causality measure was developed to dissect the respective contributions of V1 horizontal connections and V4 feedback to contour grouping. Our results suggest that feedback and lateral connections closely interact to mediate the contour integration process. In the third project, a novel Granger causality measure was proposed for the analysis of mixed neural data of spikes and LFP. Spikes and LFP are generated by separate sources with distinct signal characteristics. A joint analysis of spikes and LFP was performed to address the fundamental question about how contour regulates cortical communication between individual neurons and local network activity. The results conform to the general input-output relationship between LFP and spikes within an area. Importantly, we found that contour-related causality is only observed from spikes to LFP, but not in the opposite direction. These findings suggest that Granger causality from spikes to LFP, rather than that from LFP to spikes, carries contour-related information. Taken together, these results indicate that cortical interactions underlie contour integration, thus contribute to a better understanding of the cortical circuitry for parsing visual images and for sensory processing in general. Given the increasing use of multi-electrode recordings in multiple cortical areas, the methodology developed in this thesis should also have a broad impact.Ph.D., Biomedical Engineering -- Drexel University, 201

Encoding of Motor Behaviors by Cortical Neuronal Networks

Performance of motor behavior requires complex coordination of neural activity across diverse regions of cortex at multiples scales. At the level of coordination across large areas of cortex, this activity is thought to be related to similarly broad concepts of movement from goal identification to motor planning to generation of motor commands. At smaller scales on the level of local populations of individual neurons in motor and premotor cortex, we observe complex non-stationary firing patterns that appear to be related to the movement itself. Our understanding of the details of this relationship are incomplete, however. Earlier work by the community largely focused on the analysis of individual units in isolation. Technological advances and changes in experimental paradigms have led to the simultaneous recording of hundreds of neurons simultaneously. From an analysis standpoint, we are observing a similar shift in focus from the individual neuron to the population as a whole. This dissertation investigates encoding and decoding techniques that handle time-varying neuronal activity from within the context of a reach-to-grasp task. The first part of this work investigates the dynamics of neural coding of reach and grasp through a series of temporally localized classifiers. The second part of this thesis proposes a semi-supervised learning approach to identifying task relevant neurons for classification purposes and for identifying communities of neurons that co-modulate their activity in correlation to a common external variable. The third part of this work proposes and demonstrates an approach to modeling the firing of individual neurons as a weighted combination of other neurons with weighting dependent on the task being performed

Network Structure and Function in the Input Stage of the Cerebellar Cortex

It has long been recognised that neuronal networks are complex systems, whose dynamics depend on the properties of the individual synapses and neurons and the way in which they are interconnected. However, establishing clear links between network structure and function has proven difficult. To address this question I applied tools and techniques from computational neuroscience, neuroinformatics, information theory, machine learning, spatial point process theory and network theory, deploying them on a suitable HPC infrastructure where appropriate. Moreover, access to electrophysiological and anatomical data enabled me to develop biologically accurate models and to compare my theoretical predictions with analyses of raw data. In this work, I focused on the granule cell layer (GCL), the input stage of the cerebellar cortex. The GCL is particularly well suited to this type of analysis, as its structural characteristics are comparatively regular, well known and conserved across animal species, and several of its basic functions are relatively well understood. I showed that the synaptic connectivity in simple feed forward networks like the GCL governs the trade-off between information transmission and sparsification of incoming signals. This suggests a link between the functional requirements for the network and the strong evolutionary conservation of the anatomy of the cerebellar GCL. Furthermore, I investigated how the geometry of the GCL interacts with the spatial constraints of synaptic connectivity and gives rise to the statistical features of the chemically and electrically coupled networks formed by mossy fibres, granule cells and Golgi cells. Finally, I studied the influence of the spatial structure of the Golgi cell network on the robustness of the synchronous activity state it can support