Quantifying activity in nascent neuronal networks derived from embryonic stem cells

PhD ThesisThe relationship between spatiotemporal patterns of spontaneous activity and functional specialisation in developing neuronal networks is complex and its study is crucial to our understanding of how network communication is initiated. This project quantifies transitions between structural and functional states in embryonic stem cell cultures during differentiation. The work also focussed on the role of γ-aminobutyric acid (GABA), known to be vital for neuronal network development. The work used many techniques, including carbon nanotube (CNT) -patterned substrates to manipulate network architecture, multi-electrode arrays (MEAs) and calcium imaging to quantify function. An embryonic stem cell line (CC9) was used to generate ‘de novo’ neuronal networks and these were monitored over 13 – 22 days in vitro (DIV), while network structure forms and stabilizes. On CNT-patterned arrays, differentiating CC9s migrated and sub-clustered on CNT islands showing that network structure could be manipulated. No spontaneous electrophysiological (unit) activity was found in these cultures. However, intracellular calcium responses were readily induced and seen spontaneously at 13-20 DIV. Activity rate, kinetics and number of active cells increased between 16-18 DIV, correlating with changes in network clustering. Post 17 DIV, activity transformed from near-random to periodic and synchronous. Many events were initiated by ‘hubs’ and degrees of critical behaviour were observed, moving towards more efficient information processing states with development. Blockade of GABAA receptors lead to elevated spontaneous activity and supercritical behaviour, depending on developmental stage. Application of exogenous GABA induced large, slow calcium transients in a developmental stage-dependent manner, suggestive of a mixed excitatory/inhibitory role. These findings begin to show how activity develops as stem cells differentiate to form neuronal networks. GABA’s role in controlling patterns of activity was more complex that previously reported for neuronal networks in situ, but GABA clearly played a vital role in shaping population behaviour to optimise information processing properties in early, developing networks

Avalanches in a Stochastic Model of Spiking Neurons

Neuronal avalanches are a form of spontaneous activity widely observed in cortical slices and other types of nervous tissue, both in vivo and in vitro. They are characterized by irregular, isolated population bursts when many neurons fire together, where the number of spikes per burst obeys a power law distribution. We simulate, using the Gillespie algorithm, a model of neuronal avalanches based on stochastic single neurons. The network consists of excitatory and inhibitory neurons, first with all-to-all connectivity and later with random sparse connectivity. Analyzing our model using the system size expansion, we show that the model obeys the standard Wilson-Cowan equations for large network sizes ( neurons). When excitation and inhibition are closely balanced, networks of thousands of neurons exhibit irregular synchronous activity, including the characteristic power law distribution of avalanche size. We show that these avalanches are due to the balanced network having weakly stable functionally feedforward dynamics, which amplifies some small fluctuations into the large population bursts. Balanced networks are thought to underlie a variety of observed network behaviours and have useful computational properties, such as responding quickly to changes in input. Thus, the appearance of avalanches in such functionally feedforward networks indicates that avalanches may be a simple consequence of a widely present network structure, when neuron dynamics are noisy. An important implication is that a network need not be “critical” for the production of avalanches, so experimentally observed power laws in burst size may be a signature of noisy functionally feedforward structure rather than of, for example, self-organized criticality

Avalanches in a Stochastic Model of Spiking Neurons

Neuronal avalanches are a form of spontaneous activity widely observed in cortical slices and other types of nervous tissue, both in vivo and in vitro. They are characterized by irregular, isolated population bursts when many neurons fire together, where the number of spikes per burst obeys a power law distribution. We simulate, using the Gillespie algorithm, a model of neuronal avalanches based on stochastic single neurons. The network consists of excitatory and inhibitory neurons, first with all-to-all connectivity and later with random sparse connectivity. Analyzing our model using the system size expansion, we show that the model obeys the standard Wilson-Cowan equations for large network sizes ( neurons). When excitation and inhibition are closely balanced, networks of thousands of neurons exhibit irregular synchronous activity, including the characteristic power law distribution of avalanche size. We show that these avalanches are due to the balanced network having weakly stable functionally feedforward dynamics, which amplifies some small fluctuations into the large population bursts. Balanced networks are thought to underlie a variety of observed network behaviours and have useful computational properties, such as responding quickly to changes in input. Thus, the appearance of avalanches in such functionally feedforward networks indicates that avalanches may be a simple consequence of a widely present network structure, when neuron dynamics are noisy. An important implication is that a network need not be “critical” for the production of avalanches, so experimentally observed power laws in burst size may be a signature of noisy functionally feedforward structure rather than of, for example, self-organized criticality.</p

Dynamics and critical behaviour of neuronal cultures grown on topographical patterns with fractal structure

Treballs Finals de Màster en Física dels Sistemes Complexos i Biofísica, Facultat de Física, Universitat de Barcelona. Curs: 2022-2023. Tutor: Jordi Soriano FraderaNeuronal cultures are an excellent experimental tool to study the collective behaviour of neuronal ensembles, providing information on the principles of synaptic functioning and propagation. However, neurons cultured on flat surfaces present limitations in terms of their functionality, as they exhibit a synchronous dynamic behaviour that differs from the much richer repertoire of activity of the brain. In order to address this limitation and help developing better in vitro tools to model the brain, here we studied the capacity to break off synchrony by modulating the spatial arrangement of neurons in the substrate they grow. For that, we designed polydimethylsiloxane (PDMS) topographical patterns with fractal geometry and used them as the substrate to grow neurons, with the goal to break the isotropy in connectivity and enrich dynamics. Neuronal activity was recorded with calcium fluorescence imaging and data analysed in the context of criticality, which was inspired by recent findings suggesting that a rich structural connectivity in the brain is behind its functioning at the edge of criticality. We observed that, first, neurons cultured on fractal patterns exhibited richer and more complex dynamics as compared to standard cultures; and, second, that an analysis of the data using the renormalisation group approach, revealed the presence of scale invariance and typical features of systems poised at criticality. Our study is a multidisciplinary endeavour that combined experimental, theoretical and data analysis aspects to validate the hypothesis of the existence of a self-organised criticality in living neuronal networks, from cultures up to the brain

Functional Role of Critical Dynamics in Flexible Visual Information Processing

Recent experimental and theoretical work has established the hypothesis that cortical neurons operate close to a critical state which signifies a phase transition from chaotic to ordered dynamics. Critical dynamics are suggested to optimize several aspects of neuronal information processing. However, although signatures of critical dynamics have been demonstrated in recordings of spontaneously active cortical neurons, little is known about how these dynamics are affected by task-dependent changes in neuronal activity when the cortex is engaged in stimulus processing. In fact, some in vivo investigations of the awake and active cortex report either an absence of signatures of criticality or relatively weak ones. In addition, the functional role of criticality in optimizing computation is often reported in abstract theoretical studies, adopting minimalistic models with homogeneous topology and slowly-driven networks. Consequently, there is a lack of concrete links between information theoretical benefits of the critical state and neuronal networks performing a behaviourally relevant task. In this thesis we explore such concrete links by focusing on the visual system, which needs to meet major computational challenges on a daily basis. Among others, the visual system is responsible for the rapid integration of relevant information from a large number of single channels, and in a flexible manner depending on the behavioral and environmental contexts. We postulate that critical neuronal dynamics in the form of cascades of activity spanning large populations of neurons may support such quick and complex computations. Specifically, we consider two notable examples of well-known phenomena in visual information processing: First the enhancement of object discriminability under selective attention, and second, a feature integration and figure-ground segregation scenario. In the first example, we model the top-down modulation of the activity of visuocortical neurons in order to selectively improve the processing of an attended region in a visual scene. In the second example, we model how neuronal activity may be modulated in a bottom-up fashion by the properties of the visual stimulus itself, which makes it possible to perceive different shapes and objects. We find in both scenarios that the task performance may be improved by employing critical networks. In addition, we suggest that the specific task- or stimulus-dependent modulations of information processing may be optimally supported by the tuning of relevant local neuronal networks towards or away from the critical point. Thus, the relevance of this dissertation is summarized by the following points: We formally extend the existing models of criticality to inhomogeneous systems subject to a strong external drive. We present concrete functional benefits for networks operating near the critical point in well-known experimental paradigms. Importantly, we find emergent critical dynamics only in the parts of the network which are processing the behaviourally relevant information. We suggest that the implied locality of critical dynamics in space and time may help explain why some studies report no signatures of criticality in the active cortex

Investigating Information Flows in Spiking Neural Networks With High Fidelity

The brains of many organisms are capable of a wide variety of complex computations. This capability must be undergirded by a more general purpose computational capacity. The exact nature of this capacity, how it is distributed across the brains of organisms and how it arises throughout the course of development is an open topic of scientific investigation. Individual neurons are widely considered to be the fundamental computational units of brains. Moreover, the finest scale at which large scale recordings of brain activity can be performed is the spiking activity of neurons and our ability to perform these recordings over large numbers of neurons and with fine spatial resolution is increasing rapidly. This makes the spiking activity of individual neurons a highly attractive data modality on which to study neural computation. The framework of information dynamics has proven to be a successful approach towards interrogating the capacity for general purpose computation. It does this by revealing the atomic information processing operations of information storage, transfer and modification. Unfortunately, the study of information flows and other information processing operations from the spiking activity of neurons has been severely hindered by the lack of effective tools for estimating these quantities on this data modality. This thesis remedies this situation by presenting an estimator for information flows, as measured by Transfer Entropy (TE), that operates in continuous time on event-based data such as spike trains. Unlike the previous approach to the estimation of this quantity, which discretised the process into time bins, this estimator operates on the raw inter-spike intervals. It is demonstrated to be far superior to the previous discrete-time approach in terms of consistency, rate of convergence and bias. Most importantly, unlike the discrete-time approach, which requires a hard tradeoff between capturing fine temporal precision or history effects occurring over reasonable time intervals, this estimator can capture history effects occurring over relatively large intervals without any loss of temporal precision. This estimator is applied to developing dissociated cultures of cortical rat neurons, therefore providing the first high-fidelity study of information flows on spiking data. It is found that the spatial structure of the flows locks in to a significant extent. at the point of their emergence and that certain nodes occupy specialised computational roles as either transmitters, receivers or mediators of information flow. Moreover, these roles are also found to lock in early. In order to fully understand the structure of neural information flows, however, we are required to go beyond pairwise interactions, and indeed multivariate information flows have become an important tool in the inference of effective networks from neuroscience data. These are directed networks where each node is connected to a minimal set of sources which maximally reduce the uncertainty in its present state. However, the application of multivariate information flows to the inference of effective networks from spiking data has been hampered by the above-mentioned issues with preexisting estimation techniques. Here, a greedy algorithm which iteratively builds a set of parents for each target node using multivariate transfer entropies, and which has already been well validated in the context of traditional discretely sampled time series, is adapted to use in conjunction with the newly developed estimator for event-based data. The combination of the greedy algorithm and continuous-time estimator is then validated on simulated examples for which the ground truth is known. The new capabilities in the estimation of information flows and the inference of effective networks on event-based data presented in this work represent a very substantial step forward in our ability to perform these analyses on the ever growing set of high resolution, large scale recordings of interacting neurons. As such, this work promises to enable substantial quantitative insights in the future regarding how neurons interact, how they process information, and how this changes under different conditions such as disease

Lycium barbarum (wolfberry) polysaccharide facilitates ejaculatory behaviour in male rats

Poster Session AOBJECTIVE: Lycium barbarum (wolfberry) is a traditional Chinese medicine, which has been considered to have therapeutic effect on male infertility. However, there is a lack of studies support the claims. We thus investigated the effect of Lycium barbarum polysaccharide (LBP), a major component of wolfberry, on male rat copulatory behavior. METHOD: Sprague-Dawley rats were divided into two groups (n=8 for each group). The first group received oral feeding of LBP at dosage of 1mg/kg daily. The control group received vehicle (0.01M phosphate-buffered saline, served as control) feeding daily for 21 days. Copulatory tests were conducted at 7, 14 and 21 days after initiation of treatment. RESULTS: Compared to control animals, animals fed with 1mg/kg LBP showed improved copulatory behavior in terms of: 1. Higher copulatory efficiency (i.e. higher frequency to show intromission rather than mounting during the test), 2. higher ejaculation frequency and 3. Shorter ejaculation latency. The differences were found at all time points (Analyzed with two-tailed student’s t-test, p<0.05). There is no significant difference found between the two groups in terms of mount/intromission latency, which indicates no difference in time required for initiation of sexual activity. Additionally, no difference in mount frequency and intromission frequency was found. CONCLUSION: The present study provides scientific evidence for the traditional use of Lycium barbarum on male sexual behavior. The result provides basis for further study of wolfberry on sexual functioning and its use as an alternative treatment in reproductive medicine.postprintThe 30th Annual Meeting of the Australian Neuroscience Society, in conjunction with the 50th Anniversary Meeting of the Australian Physiological Society (ANS/AuPS 2010), Sydney, Australia, 31 January-3 February 2010. In Abstract Book of ANS/AuPS, 2010, p. 177, abstract no. POS-TUE-19

Increased and synchronous recruitment of release sites underlies hippocampal mossy fiber presynaptic potentiation

Synaptic plasticity is a cellular model for learning and memory. However, the expression mechanisms underlying presynaptic forms of plasticity are not well understood. Here, we investigate functional and structural correlates of long-term potentiation at large hippocampal mossy fiber boutons induced by the adenylyl cyclase activator forskolin. We performed two-photon imaging of the genetically encoded glutamate sensor iGlu(u) that revealed an increase in the surface area used for glutamate release at potentiated terminals. Moreover, time-gated stimulated emission depletion microscopy revealed no change in the coupling distance between immunofluorescence signals from calcium channels and release sites. Finally, by high-pressure freezing and transmission electron microscopy analysis, we found a fast remodeling of synaptic ultrastructure at potentiated boutons: synaptic vesicles dispersed in the terminal and accumulated at the active zones, while active zone density and synaptic complexity increased. We suggest that these rapid and early structural rearrangements likely enable long-term increase in synaptic strength