Multi-electrode array recording and data analysis methods for molluscan central nervous systems

In this work the use of the central nervous system (CNS) of the aquatic snail Lymnaea stagnalis on planar multi-electrode arrays (MEAs) was developed and analysis methods for the data generated were created. A variety of different combinations of configurations of tissue from the Lymnaea CNS were explored to determine the signal characteristics that could be recorded by sixty channel MEAs. In particular, the suitability of the semi-intact system consisting of the lips, oesophagus, CNS, and associated nerve connectives was developed for use on the planar MEA. The recording target area of the dorsal surface of the buccal ganglia was selected as being the most promising for study and recordings of its component cells during fictive feeding behaviour stimulated by sucrose were made. The data produced by this type of experimentation is very high volume and so its analysis required the development of a custom set of software tools. The goal of this tool set is to find the signal from individual neurons in the data streams of the electrodes of a planar MEA, to estimate their position, and then to predict their causal connectivity. To produce such an analysis techniques for noise filtration, neural spike detection, and group detection of bursts of spikes were created to pre-process electrode data streams. The Kohonen self-organising map (SOM) algorithm was adapted for the purpose of separating detected spikes into data streams representing the spike output of individual cells found in the target system. A significant addition to SOM algorithm was developed by the concurrent use of triangulation methods based on current source density analysis to predict the position of individual cells based on their spike output on more than one electrode. The likely functional connectivity of individual neurons identified by the SOM technique were analysed through the use of a statistical causality method known as Granger causality/causal connectivity. This technique was used to produce a map of the likely connectivity between neural sources

Comparison of burst detectors for spike trains

Accurate identification of bursting activity is an essential element in the characterization of neuronal network activity. Despite this, no one technique for identifying bursts in spike trains has been widely adopted. Instead, many methods have been developed for the analysis of bursting activity, often on an ad hoc basis. Here we provide an unbiased assessment of the effectiveness of eight of these methods at detecting bursts in a range of spike trains. We suggest a list of features that an ideal burst detection technique should possess and use synthetic data to assess each method in regard to these properties. We further employ each of the methods to reanalyze microelectrode array (MEA) recordings from mouse retinal ganglion cells and examine their coherence with bursts detected by a human observer. We show that several common burst detection techniques perform poorly at analyzing spike trains with a variety of properties. We identify four promising burst detection techniques, which are then applied to MEA recordings of networks of human induced pluripotent stem cell-derived neurons and used to describe the ontogeny of bursting activity in these networks over several months of development. We conclude that no current method can provide "perfect" burst detection results across a range of spike trains; however, two burst detection techniques, the MaxInterval and logISI methods, outperform compared with others. We provide recommendations for the robust analysis of bursting activity in experimental recordings using current techniques.Experimental data collection was supported by the BBSRC (PC, OP, grant number BB/H008608/1). EC was supported by a Wellcome Trust PhD Studentship and NIHR Cambridge Biomedical Research Centre Studentship. CWT was supported by a bursary from the Bridgwater Summer Undergraduate Research programme.This is the final version of the article. It first appeared from the American Physiological Society via https://doi.org/10.1152/jn.00093.201

Investigating epileptiform activity associated with slow wave sleep

PhD ThesisThe characteristic EEG trait of patients with nocturnal idiopathic epilepsies during childhood is the spike and wave discharge. Cognitive dysfunction is prevalent among these patients and is thought to be linked to disturbances in memory consolidation processes that normally occur during slow wave sleep. Several genetic mutations of nicotinic receptor subunits have been linked to these disorders. However, there is little known about the underlying mechanisms or the spatiotemporal characteristics of this epileptiform activity within the neocortex. This thesis presents a rat in vitro model of the epileptiform activity synonymous with nocturnal childhood epilepsies, that allows for pharmacological manipulation of receptor subunits linked to these disorders. The application of DTC [10 M], a non-selective, competitive nicotinic acetylcholine receptor antagonist, to an in vitro model of the cortical delta rhythm induced two individual forms of paroxysm events - wave discharges and the conventional spike and wave discharges. Pharmacological manipulation of this model suggest that the epileptiform activity is mediated by excitatory currents which is consistent with the use of glutamate antagonists as anticonvulsants. A blanket blockade of inhibition by a GABAA antagonist resulted in severe discharges, hence hugely increasing excitatory response. Only partial disinhibition is suggested to be required to generate epileptiform activity as nicotinic acetylcholine receptors and 5-HT3 receptors are located on dendrite targeting interneurons. Mapping of unit activity revealed the di erence between the two paroxysm events was recruitment of super cial layers with simultaneous paroxysm events in delta frequency-generating Layer V pyramidal cells. It is proposed that the hyperexcitability responsible for the generation of the spike component of a spike and wave discharge is mediated by the lack of excitatory tone in 5-HT3 and nicotinic acetylecholine receptor expressing inhibitory interneuron subtypes. The disinhibition, spike generation and disruption of interplay between deep and super cial layers of the neocortex is thought to be associated with synaptic plastic changes

Anti-correlations in the degree distribution increase stimulus detection performance in noisy spiking neural networks

Neuronal circuits in the rodent barrel cortex are characterized by stable low firing rates. However, recent experiments show that short spike trains elicited by electrical stimulation in single neurons can induce behavioral responses. Hence, the underlying neural networks provide stability against internal fluctuations in the firing rate, while simultaneously making the circuits sensitive to small external perturbations. Here we studied whether stability and sensitivity are affected by the connectivity structure in recurrently connected spiking networks. We found that anti-correlation between the number of afferent (in-degree) and efferent (out-degree) synaptic connections of neurons increases stability against pathological bursting, relative to networks where the degrees were either positively correlated or uncorrelated. In the stable network state, stimulation of a few cells could lead to a detectable change in the firing rate. To quantify the ability of networks to detect the stimulation, we used a receiver operating characteristic (ROC) analysis. For a given level of background noise, networks with anti-correlated degrees displayed the lowest false positive rates, and consequently had the highest stimulus detection performance. We propose that anti-correlation in the degree distribution may be a computational strategy employed by sensory cortices to increase the detectability of external stimuli. We show that networks with anti-correlated degrees can in principle be formed by applying learning rules comprised of a combination of spike-timing dependent plasticity, homeostatic plasticity and pruning to networks with uncorrelated degrees. To test our prediction we suggest a novel experimental method to estimate correlations in the degree distribution

A Neural Signal Processor for Low-Latency Spike Inference

This thesis describes the development of a system that can assign identities to a population of single-units, in multi-electrode recordings, at single-spike resolution with low-latency. The system has two parts. The first is a Field-Programmable Gate Array (FPGA)-based Neural Signal Processor (NSP) that receives raw input and generates labelled spikes as output, a process referred to as real-time spike inference. The second is a piece of software (Spiketag) that runs on a PC, communicates with the NSP, and generates a spike-sorted model to guide the real-time spike inference. The NSP provides clocks and control signals to five 32-channel INTAN RHD2132 chips to manage the acquisition of 160 channels of raw neural data. In parallel, the NSP further filters, detects and extracts extracellular spike waveforms from the raw neural data recorded by tetrodes or silicon probes and assigns single-unit identity to each detected spike. A set of Python application programming interfaces (APIs) was developed in Spiketag to enable the communication between the NSP and the PC. These APIs allow the NSP to obtain a model from the PC, which holds parameters such as reference channels, spike detection thresholds, spike feature transformation matrix and vector quantized clusters generated by spike sorting a short recording session. Using the spike-sorted model, the NSP performs data acquisition and real-time spike inference simultaneously. Algorithmic modules were implemented in the FPGA and pipelined to compute during 40 ms acquisition intervals. At the output end of the FPGA NSP, the real-time assigned single-unit identity (spike-id) is packaged with the timestamp, the electrode group, and the spike features as a spike-id packet. Spike-id packets are asynchronously transmitted through a low-latency Peripheral Component Interconnect Express (PCIe) interface to the PC, producing the real-time spike trains. The real-time spike trains can be used for further processing, such as real-time decoding. Several types of ground-truth data, including intracellular/extracellular paired recordings, synthesized tetrode extracellular waveforms with ground-truth spike timing and high-channel-count silicon probe recordings with ground-truth animal positions during navigation were used to validate the low-latency (1 ms) and high-accuracy (as high as state-of-the-art offline sorting and decoding algorithms) of the NSP’s real-time spike inference and the NSP-based real-time population decoding performance

The role of medial entorhinal cortex activity in hippocampal CA1 spatiotemporally correlated sequence generation and object selectivity for memory function

The hippocampus is crucial for episodic memory and certain forms of spatial navigation. Firing activity of hippocampal principal neurons contains environmental information, including the presence of specific objects, as well as the animal’s spatial and temporal position relative to environmental and behavioral cues. The organization of these firing correlates may allow the formation of memory traces through the integration of object and event information onto a spatiotemporal framework of cell assemblies. Characterizing how external inputs guide internal dynamics in the hippocampus to enable this process across different experiences is crucial to understanding hippocampal function. A body of literature implicates the medial entorhinal cortex (MEC) in supplying spatial and temporal information to the hippocampus. Here we develop a protocol utilizing bilaterally implanted custom designed triple fiber optic arrays and the red-shifted inhibitory opsin JAWS to transiently inactivate large volumes of MEC in freely behaving rats. This was coupled with extracellular tetrode recording of ensembles in CA1 of the hippocampus during a novel memory task involving temporal, spatial and object related epochs, in order to assess the importance of MEC activity for hippocampal feature selectivity during a rich and familiar experience. We report that inactivation of MEC during a mnemonic temporal delay disrupts the existing temporal firing field sequence in CA1 both during and following the inactivation period. Neurons with firing fields prior to the inactivation on each trial remained relatively stable. The disruption of CA1 temporal firing field sequences was accompanied by a behavioral deficit implicating MEC activity and hippocampal temporal field sequences in effective memory across time. Inactivating MEC during the object or spatial epochs of the task did not significantly alter CA1 object selective or spatial firing fields and behavioral performance remained stable. Our findings suggest that MEC is crucial specifically for temporal field organization and expression during a familiar and rich experience. These results support a role for MEC in guiding hippocampal cell assembly sequences in the absence of salient changing stimuli, which may extend to the navigation of cognitive organization in humans and support memory formation and retrieval

Investigating the role of fast-spiking interneurons in neocortical dynamics

PhD ThesisFast-spiking interneurons are the largest interneuronal population in neocortex. It is well documented that this population is crucial in many functions of the neocortex by subserving all aspects of neural computation, like gain control, and by enabling dynamic phenomena, like the generation of high frequency oscillations. Fast-spiking interneurons, which represent mainly the parvalbumin-expressing, soma-targeting basket cells, are also implicated in pathological dynamics, like the propagation of seizures or the impaired coordination of activity in schizophrenia. In the present thesis, I investigate the role of fast-spiking interneurons in such dynamic phenomena by using computational and experimental techniques. First, I introduce a neural mass model of the neocortical microcircuit featuring divisive inhibition, a gain control mechanism, which is thought to be delivered mainly by the soma-targeting interneurons. Its dynamics were analysed at the onset of chaos and during the phenomena of entrainment and long-range synchronization. It is demonstrated that the mechanism of divisive inhibition reduces the sensitivity of the network to parameter changes and enhances the stability and exibility of oscillations. Next, in vitro electrophysiology was used to investigate the propagation of activity in the network of electrically coupled fast-spiking interneurons. Experimental evidence suggests that these interneurons and their gap junctions are involved in the propagation of seizures. Using multi-electrode array recordings and optogenetics, I investigated the possibility of such propagating activity under the conditions of raised extracellular K+ concentration which applies during seizures. Propagated activity was recorded and the involvement of gap junctions was con rmed by pharmacological manipulations. Finally, the interaction between two oscillations was investigated. Two oscillations with di erent frequencies were induced in cortical slices by directly activating the pyramidal cells using optogenetics. Their interaction suggested the possibility of a coincidence detection mechanism at the circuit level. Pharmacological manipulations were used to explore the role of the inhibitory interneurons during this phenomenon. The results, however, showed that the observed phenomenon was not a result of synaptic activity. Nevertheless, the experiments provided some insights about the excitability of the tissue through scattered light while using optogenetics. This investigation provides new insights into the role of fast-spiking interneurons in the neocortex. In particular, it is suggested that the gain control mechanism is important for the physiological oscillatory dynamics of the network and that the gap junctions between these interneurons can potentially contribute to the inhibitory restraint during a seizure.Wellcome Trust

Studies on neuronal network activity of olfactory bulb, spinal cord and frontal cortex grown on microelectrode arrays in vitro : the role of gap junctions in network integration

This project focused on understanding the mechanisms involved in CNS integration. The anatomy and physiology of mammalian olfactory system was investigated in order to develop an organotypic in vitro sensory system to increase our understanding of sensory processing at a neural network level. The olfactory network cultures grown on multielectrode arrays (MEAs) were found to only rarely exhibit electrical activity and it was decided this was an unsuitable preparation for the purposes of this study. The spinal cord was chosen as a secondary sensory system, initially in co-culture with dorsal root ganglia and then alone, with special interest in gap junction function. Gap junctions have received increasing attention as contributors to pattern generation in neuronal ensembles, including the generation or modification of highly coordinated, intense bursting states. The main result section of this study explored the effects of four gap junction blockers (carbenoxolone (CBX), halothane, I-octanol and oleamide) on the spontaneous activity of mouse and rat frontal cortex and spinal cord cultures grown on microelectrode arrays (MEAs). It was our hypothesis that the characteristic coordinated bursting seen in most frontal cortex and in some higher density spinal cord cultures would be influenced via gap junction communication. The four compounds tested generated interesting, and in one case paradoxical effects. Frontal cortex cultures were all inhibited in a dose-dependent manner, which included total cessation of activity by halothane, CBX, I-octanol, or oleamide (at concentrations 250 muM, 100 muM, 20 muM, 20 muM, respectively). All cultures showed spontaneous recovery at lower concentrations and reversibility after culture medium changes at higher concentrations. In addition, measurements of network burst rates and coefficients of variation of burst period indicate that burst coordination among channels was reduced by these compounds. These responses were generally mirrored in the spinal cord, except for CBX, which produced a paradoxical transient intense increase in network spike and burst production. The results of this study show the effect of the gap junction blockers to be not only tissue specific, but also to differ from species to species. It is still unclear whether these differences seen really are through the blockade of gap junctions, or due to the secondary effects of the blockers used. Further studies showed that strychnine (1 muM) prevented this transient activity increase in spinal cord networks, implying that CBX may temporarily block glycine inhibition. Blocking intracellular calcium mobility with thapsigargin (up to 5 muM) did not affect the effects of gap junction blockers used