|Math Model Describes Collaboration of Individual Neurons|
|SciMed - Neuroscience|
|TS-Si News Service|
|Monday, 12 March 2012 02:00|
Wako-Shi, Japan; Jülich, Germany; Boston, MA, USA. A new mathematical model describes the collaboration of individual neurons and answer the question of how neurons in the brain communicate with each other?
One common theory suggests that individual cells do not exchange signals among each other, but rather that exchange takes place between groups of cells. The model can be used to test this assumption.
Scientists at the RIKEN Brain Science Institute (RIKEN BSI) (Japan) collaborated with researchers at the Forschungszentrum Jülich (Germany) and the Massachusetts Institute of Technology (MIT) (Boston, USA). A neuron in the neocortex, the part of the brain that deals with higher brain functions, contacts thousands of other neurons and receives as many inputs from other neurons. Previously, it has been very difficult to use measured signals to interpret the way the cells work together. The results of the current study appear in the journal PLoS Computational Biology. Refer to the sidebar for information on the general process for Neuronal Transmission of Information.
"From the many signals measured in parallel, the novel method filters the information on whether the neurons communicate individually or as a group", explains Dr. Hideaki Shimazaki from BSI. "Furthermore it takes into account that these groups of cells are not fixed but, instead, can organize themselves flexibly within milliseconds into groups of different composition, depending on the current requirements of the brain."
Prof. Sonja Grün from Forschungszentrum Jülich hopes that the method can help researchers to prove the existence of dynamic cell assemblies and clearly assign their activities to certain behaviors. The scientists already demonstrated that neurons work together when an animal anticipates a signal, which may allow it to have a more rapid or more sensitive response.
In the future, scientists hope to learn how to use their methods on the signals recorded from hundreds of neurons simultaneously. This would raise the probability of observing cell assemblies involved in planning and controlling behavior.
FundingThis work was supported in part by JSPS Research Fellowships for Young Scientists (HS) and RIKEN Strategic Programs for R&D (SG).
CitationState-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data. Hideaki Shimazaki, Shun-ichi Amari, Emery N. Brown, Sonja Grün. PLoS Computational Biology 2012; 8(3): e1002385. doi:10.1371/journal.pcbi.1002385
Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods to neural spike data simultaneously recorded from the motor cortex of an awake monkey and demonstrate that the higher-order spike correlation organizes dynamically in relation to a behavioral demand.
Nearly half a century ago, the Canadian psychologist D. O. Hebb postulated the formation of assemblies of tightly connected cells in cortical recurrent networks because of changes in synaptic weight (Hebb's learning rule) by repetitive sensory stimulation of the network. Consequently, the activation of such an assembly for processing sensory or behavioral information is likely to be expressed by precisely coordinated spiking activities of the participating neurons. However, the available analysis techniques for multiple parallel neural spike data do not allow us to reveal the detailed structure of transiently active assemblies as indicated by their dynamical pairwise and higher-order spike correlations. Here, we construct a state-space model of dynamic spike interactions, and present a recursive Bayesian method that makes it possible to trace multiple neurons exhibiting such precisely coordinated spiking activities in a time-varying manner. We also formulate a hypothesis test of the underlying dynamic spike correlation, which enables us to detect the assemblies activated in association with behavioral events. Therefore, the proposed method can serve as a useful tool to test Hebb's cell assembly hypothesis.
|Last Updated on Sunday, 11 March 2012 21:39|