930 research outputs found
The Local Field Potential Reflects Surplus Spike Synchrony
The oscillatory nature of the cortical local field potential (LFP) is
commonly interpreted as a reflection of synchronized network activity, but its
relationship to observed transient coincident firing of neurons on the
millisecond time-scale remains unclear. Here we present experimental evidence
to reconcile the notions of synchrony at the level of neuronal spiking and at
the mesoscopic scale. We demonstrate that only in time intervals of excess
spike synchrony, coincident spikes are better entrained to the LFP than
predicted by the locking of the individual spikes. This effect is enhanced in
periods of large LFP amplitudes. A quantitative model explains the LFP dynamics
by the orchestrated spiking activity in neuronal groups that contribute the
observed surplus synchrony. From the correlation analysis, we infer that
neurons participate in different constellations but contribute only a fraction
of their spikes to temporally precise spike configurations, suggesting a dual
coding scheme of rate and synchrony. This finding provides direct evidence for
the hypothesized relation that precise spike synchrony constitutes a major
temporally and spatially organized component of the LFP. Revealing that
transient spike synchronization correlates not only with behavior, but with a
mesoscopic brain signal corroborates its relevance in cortical processing.Comment: 45 pages, 8 figures, 3 supplemental figure
Feed-Forward Propagation of Temporal and Rate Information between Cortical Populations during Coherent Activation in Engineered In Vitro Networks.
Transient propagation of information across neuronal assembles is thought to underlie many cognitive processes. However, the nature of the neural code that is embedded within these transmissions remains uncertain. Much of our understanding of how information is transmitted among these assemblies has been derived from computational models. While these models have been instrumental in understanding these processes they often make simplifying assumptions about the biophysical properties of neurons that may influence the nature and properties expressed. To address this issue we created an in vitro analog of a feed-forward network composed of two small populations (also referred to as assemblies or layers) of living dissociated rat cortical neurons. The populations were separated by, and communicated through, a microelectromechanical systems (MEMS) device containing a strip of microscale tunnels. Delayed culturing of one population in the first layer followed by the second a few days later induced the unidirectional growth of axons through the microtunnels resulting in a primarily feed-forward communication between these two small neural populations. In this study we systematically manipulated the number of tunnels that connected each layer and hence, the number of axons providing communication between those populations. We then assess the effect of reducing the number of tunnels has upon the properties of between-layer communication capacity and fidelity of neural transmission among spike trains transmitted across and within layers. We show evidence based on Victor-Purpura's and van Rossum's spike train similarity metrics supporting the presence of both rate and temporal information embedded within these transmissions whose fidelity increased during communication both between and within layers when the number of tunnels are increased. We also provide evidence reinforcing the role of synchronized activity upon transmission fidelity during the spontaneous synchronized network burst events that propagated between layers and highlight the potential applications of these MEMs devices as a tool for further investigation of structure and functional dynamics among neural populations
Replay as wavefronts and theta sequences as bump oscillations in a grid cell attractor network.
Grid cells fire in sequences that represent rapid trajectories in space. During locomotion, theta sequences encode sweeps in position starting slightly behind the animal and ending ahead of it. During quiescence and slow wave sleep, bouts of synchronized activity represent long trajectories called replays, which are well-established in place cells and have been recently reported in grid cells. Theta sequences and replay are hypothesized to facilitate many cognitive functions, but their underlying mechanisms are unknown. One mechanism proposed for grid cell formation is the continuous attractor network. We demonstrate that this established architecture naturally produces theta sequences and replay as distinct consequences of modulating external input. Driving inhibitory interneurons at the theta frequency causes attractor bumps to oscillate in speed and size, which gives rise to theta sequences and phase precession, respectively. Decreasing input drive to all neurons produces traveling wavefronts of activity that are decoded as replays
CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains
Recent developments in electrophysiological and optical recording techniques enable the simultaneous observation of large numbers of neurons. A meaningful interpretation of the resulting multivariate data, however, presents a serious challenge. In particular, the estimation of higher-order correlations that characterize the cooperative dynamics of groups of neurons is impeded by the combinatorial explosion of the parameter space. The resulting requirements with respect to sample size and recording time has rendered the detection of coordinated neuronal groups exceedingly difficult. Here we describe a novel approach to infer higher-order correlations in massively parallel spike trains that is less susceptible to these problems. Based on the superimposed activity of all recorded neurons, the cumulant-based inference of higher-order correlations (CuBIC) presented here exploits the fact that the absence of higher-order correlations imposes also strong constraints on correlations of lower order. Thus, estimates of only few lower-order cumulants suffice to infer higher-order correlations in the population. As a consequence, CuBIC is much better compatible with the constraints of in vivo recordings than previous approaches, which is shown by a systematic analysis of its parameter dependence
A Semiparametric Bayesian Model for Detecting Synchrony Among Multiple Neurons
We propose a scalable semiparametric Bayesian model to capture dependencies
among multiple neurons by detecting their co-firing (possibly with some lag
time) patterns over time. After discretizing time so there is at most one spike
at each interval, the resulting sequence of 1's (spike) and 0's (silence) for
each neuron is modeled using the logistic function of a continuous latent
variable with a Gaussian process prior. For multiple neurons, the corresponding
marginal distributions are coupled to their joint probability distribution
using a parametric copula model. The advantages of our approach are as follows:
the nonparametric component (i.e., the Gaussian process model) provides a
flexible framework for modeling the underlying firing rates; the parametric
component (i.e., the copula model) allows us to make inference regarding both
contemporaneous and lagged relationships among neurons; using the copula model,
we construct multivariate probabilistic models by separating the modeling of
univariate marginal distributions from the modeling of dependence structure
among variables; our method is easy to implement using a computationally
efficient sampling algorithm that can be easily extended to high dimensional
problems. Using simulated data, we show that our approach could correctly
capture temporal dependencies in firing rates and identify synchronous neurons.
We also apply our model to spike train data obtained from prefrontal cortical
areas in rat's brain
VIOLA - A multi-purpose and web-based visualization tool for neuronal-network simulation output
Neuronal network models and corresponding computer simulations are invaluable
tools to aid the interpretation of the relationship between neuron properties,
connectivity and measured activity in cortical tissue. Spatiotemporal patterns
of activity propagating across the cortical surface as observed experimentally
can for example be described by neuronal network models with layered geometry
and distance-dependent connectivity. The interpretation of the resulting stream
of multi-modal and multi-dimensional simulation data calls for integrating
interactive visualization steps into existing simulation-analysis workflows.
Here, we present a set of interactive visualization concepts called views for
the visual analysis of activity data in topological network models, and a
corresponding reference implementation VIOLA (VIsualization Of Layer Activity).
The software is a lightweight, open-source, web-based and platform-independent
application combining and adapting modern interactive visualization paradigms,
such as coordinated multiple views, for massively parallel neurophysiological
data. For a use-case demonstration we consider spiking activity data of a
two-population, layered point-neuron network model subject to a spatially
confined excitation originating from an external population. With the multiple
coordinated views, an explorative and qualitative assessment of the
spatiotemporal features of neuronal activity can be performed upfront of a
detailed quantitative data analysis of specific aspects of the data.
Furthermore, ongoing efforts including the European Human Brain Project aim at
providing online user portals for integrated model development, simulation,
analysis and provenance tracking, wherein interactive visual analysis tools are
one component. Browser-compatible, web-technology based solutions are therefore
required. Within this scope, with VIOLA we provide a first prototype.Comment: 38 pages, 10 figures, 3 table
A Neuromorphic Prosthesis to Restore Communication in Neuronal Networks
Recent advances in bioelectronics and neural engineering allowed the development of brain machine interfaces and neuroprostheses, capable of facilitating or recovering functionality in people with neurological disability. To realize energy-efficient and real-time capable devices, neuromorphic computing systems are envisaged as the core of next-generation systems for brain repair. We demonstrate here a real-time hardware neuromorphic prosthesis to restore bidirectional interactions between two neuronal populations, even when one is damaged or missing. We used in vitro modular cell cultures to mimic the mutual interaction between neuronal assemblies and created a focal lesion to functionally disconnect the two populations. Then, we employed our neuromorphic prosthesis for bidirectional bridging to artificially reconnect two disconnected neuronal modules and for hybrid bidirectional bridging to replace the activity of one module with a real-time hardware neuromorphic Spiking Neural Network. Our neuroprosthetic system opens avenues for the exploitation of neuromorphic-based devices in bioelectrical therapeutics for health care
- …