Reliable Sequential Activation of Neural Assemblies by Single Pyramidal Cells in a Three-Layered Cortex

Recent studies reveal the occasional impact of single neurons on surround firing statistics and even simple behaviors. Exploiting the advantages of a simple cortex, we examined the influence of single pyramidal neurons on surrounding cortical circuits. Brief activation of single neurons triggered reliable sequences of firing in tens of other excitatory and inhibitory cortical neurons, reflecting cascading activity through local networks, as indicated by delayed yet precisely timed polysynaptic subthreshold potentials. The evoked patterns were specific to the pyramidal cell of origin, extended over hundreds of micrometers from their source, and unfolded over up to 200 ms. Simultaneous activation of pyramidal cell pairs indicated balanced control of population activity, preventing paroxysmal amplification. Single cortical pyramidal neurons can thus trigger reliable postsynaptic activity that can propagate in a reliable fashion through cortex, generating rapidly evolving and non-random firing sequences reminiscent of those observed in mammalian hippocampus during "replay" and in avian song circuits

Consensus-Based Sorting of Neuronal Spike Waveforms

Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained “ground truth” data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data

Innate Synchronous Oscillations in Freely-Organized Small Neuronal Circuits

BACKGROUND: Information processing in neuronal networks relies on the network's ability to generate temporal patterns of action potentials. Although the nature of neuronal network activity has been intensively investigated in the past several decades at the individual neuron level, the underlying principles of the collective network activity, such as the synchronization and coordination between neurons, are largely unknown. Here we focus on isolated neuronal clusters in culture and address the following simple, yet fundamental questions: What is the minimal number of cells needed to exhibit collective dynamics? What are the internal temporal characteristics of such dynamics and how do the temporal features of network activity alternate upon crossover from minimal networks to large networks? METHODOLOGY/PRINCIPAL FINDINGS: We used network engineering techniques to induce self-organization of cultured networks into neuronal clusters of different sizes. We found that small clusters made of as few as 40 cells already exhibit spontaneous collective events characterized by innate synchronous network oscillations in the range of 25 to 100 Hz. The oscillation frequency of each network appeared to be independent of cluster size. The duration and rate of the network events scale with cluster size but converge to that of large uniform networks. Finally, the investigation of two coupled clusters revealed clear activity propagation with master/slave asymmetry. CONCLUSIONS/SIGNIFICANCE: The nature of the activity patterns observed in small networks, namely the consistent emergence of similar activity across networks of different size and morphology, suggests that neuronal clusters self-regulate their activity to sustain network bursts with internal oscillatory features. We therefore suggest that clusters of as few as tens of cells can serve as a minimal but sufficient functional network, capable of sustaining oscillatory activity. Interestingly, the frequencies of these oscillations are similar those observed in vivo

Emergent Functional Properties of Neuronal Networks with Controlled Topology

The interplay between anatomical connectivity and dynamics in neural networks plays a key role in the functional properties of the brain and in the associated connectivity changes induced by neural diseases. However, a detailed experimental investigation of this interplay at both cellular and population scales in the living brain is limited by accessibility. Alternatively, to investigate the basic operational principles with morphological, electrophysiological and computational methods, the activity emerging from large in vitro networks of primary neurons organized with imposed topologies can be studied. Here, we validated the use of a new bio-printing approach, which effectively maintains the topology of hippocampal cultures in vitro and investigated, by patch-clamp and MEA electrophysiology, the emerging functional properties of these grid-confined networks. In spite of differences in the organization of physical connectivity, our bio-patterned grid networks retained the key properties of synaptic transmission, short-term plasticity and overall network activity with respect to random networks. Interestingly, the imposed grid topology resulted in a reinforcement of functional connections along orthogonal directions, shorter connectivity links and a greatly increased spiking probability in response to focal stimulation. These results clearly demonstrate that reliable functional studies can nowadays be performed on large neuronal networks in the presence of sustained changes in the physical network connectivity

Brief wide-field photostimuli evoke and modulate oscillatory reverberating activity in cortical networks

Cell assemblies manipulation by optogenetics is pivotal to advance neuroscience and neuroengineering. In in vivo applications, photostimulation often broadly addresses a population of cells simultaneously, leading to feed-forward and to reverberating responses in recurrent microcircuits. The former arise from direct activation of targets downstream, and are straightforward to interpret. The latter are consequence of feedback connectivity and may reflect a variety of time-scales and complex dynamical properties. We investigated wide-field photostimulation in cortical networks in vitro, employing substrate-integrated microelectrode arrays and long-term cultured neuronal networks. We characterized the effect of brief light pulses, while restricting the expression of channelrhodopsin to principal neurons. We evoked robust reverberating responses, oscillating in the physiological gamma frequency range, and found that such a frequency could be reliably manipulated varying the light pulse duration, not its intensity. By pharmacology, mathematical modelling, and intracellular recordings, we conclude that gamma oscillations likely emerge as in vivo from the excitatory-inhibitory interplay and that, unexpectedly, the light stimuli transiently facilitate excitatory synaptic transmission. Of relevance for in vitro models of (dys)functional cortical microcircuitry and in vivo manipulations of cell assemblies, we give for the first time evidence of network-level consequences of the alteration of synaptic physiology by optogenetics

Self-Organizing Circuit Assembly through Spatiotemporally Coordinated Neuronal Migration within Geometric Constraints

Neurons are dynamically coupled with each other through neurite-mediated adhesion during development. Understanding the collective behavior of neurons in circuits is important for understanding neural development. While a number of genetic and activity-dependent factors regulating neuronal migration have been discovered on single cell level, systematic study of collective neuronal migration has been lacking. Various biological systems are shown to be self-organized, and it is not known if neural circuit assembly is self-organized. Besides, many of the molecular factors take effect through spatial patterns, and coupled biological systems exhibit emergent property in response to geometric constraints. How geometric constraints of the patterns regulate neuronal migration and circuit assembly of neurons within the patterns remains unexplored.We established a two-dimensional model for studying collective neuronal migration of a circuit, with hippocampal neurons from embryonic rats on Matrigel-coated self-assembled monolayers (SAMs). When the neural circuit is subject to geometric constraints of a critical scale, we found that the collective behavior of neuronal migration is spatiotemporally coordinated. Neuronal somata that are evenly distributed upon adhesion tend to aggregate at the geometric center of the circuit, forming mono-clusters. Clustering formation is geometry-dependent, within a critical scale from 200 µm to approximately 500 µm. Finally, somata clustering is neuron-type specific, and glutamatergic and GABAergic neurons tend to aggregate homo-philically.We demonstrate self-organization of neural circuits in response to geometric constraints through spatiotemporally coordinated neuronal migration, possibly via mechanical coupling. We found that such collective neuronal migration leads to somata clustering, and mono-cluster appears when the geometric constraints fall within a critical scale. The discovery of geometry-dependent collective neuronal migration and the formation of somata clustering in vitro shed light on neural development in vivo

Circuit-based interrogation of sleep control.

Sleep is a fundamental biological process observed widely in the animal kingdom, but the neural circuits generating sleep remain poorly understood. Understanding the brain mechanisms controlling sleep requires the identification of key neurons in the control circuits and mapping of their synaptic connections. Technical innovations over the past decade have greatly facilitated dissection of the sleep circuits. This has set the stage for understanding how a variety of environmental and physiological factors influence sleep. The ability to initiate and terminate sleep on command will also help us to elucidate its functions within and beyond the brain

Large-scale mapping of cortical synaptic projections with extracellular electrode arrays

Understanding curcuit computation in the nervous system requires sampling activity over large neural populations and maximizing the number of features that can be extracted . By combining planar arrays of extracullular electrodes with the three-layered cortex of turtles, we show that synaptic signals induced along individual axons as well as action poteintials can be easily captured. Two types of information can be extracted from these signals, the neuronal subtype (inhibitory or excitatory)-whose identification is more reliable than with traditional measures such as action potential width - and a (partial) spatial map of functional axonal projections from individual neurons. Because our approach is algorhytmic, it can be carried out in parellel on hundreds of simultaneoulsy recoreded neurons. Combining our approach with soma triangulation, we reveal an axonal projectionbias among a population of pyramidal neurons in turtle cortex and confirm this bias through anatomical reconstructions