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Circuit topology for synchronizing neurons in spontaneously active networks.

ABSTRACT: Spike synchronization underlies information processing and storage in the brain. But how can neurons synchronize in a noisy network? By exploiting a high-speed (500-2,000 fps) multineuron imaging technique and a large-scale synapse mapping method, we directly compared spontaneous activity patterns and anatomical connectivity in hippocampal CA3 networks ex vivo. As compared to unconnected pairs, synaptically coupled neurons shared more common presynaptic neurons, received more correlated excitatory synaptic inputs, and emitted synchronized spikes with approximately 10(7) times higher probability. Importantly, common presynaptic parents per se synchronized more than unshared upstream neurons. Consistent with this, dynamic-clamp stimulation revealed that common inputs alone could not account for the realistic degree of synchronization unless presynaptic spikes synchronized among common parents. On a macroscopic scale, network activity was coordinated by a power-law scaling of synchronization, which engaged varying sets of densely interwired (thus highly synchronized) neuron groups. Thus, locally coherent activity converges on specific cell assemblies, thereby yielding complex ensemble dynamics. These segmentally synchronized pulse packets may serve as information modules that flow in associatively parallel network channels.

SUBMITTER: Takahashi N

PROVIDER: S-EPMC2890467 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature

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Circuit topology for synchronizing neurons in spontaneously active networks.

Takahashi Naoya N Sasaki Takuya T Matsumoto Wataru W Matsuki Norio N Ikegaya Yuji Y

Proceedings of the National Academy of Sciences of the United States of America 20100517 22

Spike synchronization underlies information processing and storage in the brain. But how can neurons synchronize in a noisy network? By exploiting a high-speed (500-2,000 fps) multineuron imaging technique and a large-scale synapse mapping method, we directly compared spontaneous activity patterns and anatomical connectivity in hippocampal CA3 networks ex vivo. As compared to unconnected pairs, synaptically coupled neurons shared more common presynaptic neurons, received more correlated excitato ...[more]

PMID: 20479225

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Project description:The external globus pallidus (GPe) is a critical node within the basal ganglia circuit. Phasic changes in the activity of GPe neurons during movement and their alterations in Parkinson's disease (PD) argue that the GPe is important in motor control. Parvalbumin-positive (PV+) neurons and Npas1+ neurons are the two principal neuron classes in the GPe. The distinct electrophysiological properties and axonal projection patterns argue that these two neuron classes serve different roles in regulating motor output. However, the causal relationship between GPe neuron classes and movement remains to be established. Here, by using optogenetic approaches in mice (both males and females), we showed that PV+ neurons and Npas1+ neurons promoted and suppressed locomotion, respectively. Moreover, PV+ neurons and Npas1+ neurons are under different synaptic influences from the subthalamic nucleus (STN). Additionally, we found a selective weakening of STN inputs to PV+ neurons in the chronic 6-hydroxydopamine lesion model of PD. This finding reinforces the idea that the reciprocally connected GPe-STN network plays a key role in disease symptomatology and thus provides the basis for future circuit-based therapies.SIGNIFICANCE STATEMENT The external pallidum is a key, yet an understudied component of the basal ganglia. Neural activity in the pallidum goes awry in neurologic diseases, such as Parkinson's disease. While this strongly argues that the pallidum plays a critical role in motor control, it has been difficult to establish the causal relationship between pallidal activity and motor function/dysfunction. This was in part because of the cellular complexity of the pallidum. Here, we showed that the two principal neuron types in the pallidum have opposing roles in motor control. In addition, we described the differences in their synaptic influence. Importantly, our research provides new insights into the cellular and circuit mechanisms that explain the hypokinetic features of Parkinson's disease.

Project description:BackgroundMetabolic Flux Analysis (MFA) based on isotope labeling experiments (ILEs) is a widely established tool for determining fluxes in metabolic pathways. Isotope labeling networks (ILNs) contain all essential information required to describe the flow of labeled material in an ILE. Whereas recent experimental progress paves the way for high-throughput MFA, large network investigations and exact statistical methods, these developments are still limited by the poor performance of computational routines used for the evaluation and design of ILEs. In this context, the global analysis of ILN topology turns out to be a clue for realizing large speedup factors in all required computational procedures.ResultsWith a strong focus on the speedup of algorithms the topology of ILNs is investigated using graph theoretic concepts and algorithms. A rigorous determination of all cyclic and isomorphic subnetworks, accompanied by the global analysis of ILN connectivity is performed. Particularly, it is proven that ILNs always brake up into a large number of small strongly connected components (SCCs) and, moreover, there are natural isomorphisms between many of these SCCs. All presented techniques are universal, i.e. they do not require special assumptions on the network structure, bidirectionality of fluxes, measurement configuration, or label input. The general results are exemplified with a practically relevant metabolic network which describes the central metabolism of E. coli comprising 10390 isotopomer pools.ConclusionExploiting the topological features of ILNs leads to a significant speedup of all universal algorithms for ILE evaluation. It is proven in theory and exemplified with the E. coli example that a speedup factor of about 1000 compared to standard algorithms is achieved. This widely opens the door for new high performance algorithms suitable for high throughput applications and large ILNs. Moreover, for the first time the global topological analysis of ILNs allows to comprehensively describe and understand the general patterns of label flow in complex networks. This is an invaluable tool for the structural design of new experiments and the interpretation of measured data.

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Circuit topology for synchronizing neurons in spontaneously active networks.

Publications

Circuit topology for synchronizing neurons in spontaneously active networks.

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