5,313 research outputs found
Estimating the Amount of Information Carried by a Neuronal Population
Although all brain functions require coordinated activity of many neurons, it has been difficult to estimate the amount of information carried by a population of spiking neurons. We present here a Fourier-based method for estimating the information delivery rate from a population of neurons, which allows us to measure the redundancy of information within and between functional neuronal classes. We illustrate the use of the method on some artificial spike trains and on simultaneous recordings from a small population of neurons from the lateral geniculate nucleus of an anesthetized macaque monkey
Estimating the Amount of Information Conveyed by a Population of Neurons
Recent advances in electrophysiological recording technology have allowed for the collection of data from large populations of neurons simultaneously. Yet despite these advances, methods for the estimation of the amount of information conveyed by multiple neurons have been stymied by the “curse of dimensionality”–as the number of included neurons increases, so too does the dimensionality of the data necessary for such measurements, leading to an exponential and, therefore, intractible increase in the amounts of data required for valid measurements. Here we put forth a novel method for the estimation of the amount of information transmitted by the discharge of a large population of neurons, a method which exploits the little-known fact that (under certain constraints) the Fourier coefficients of variables such as neural spike trains follow a Gaussian distribution. This fact enables an accurate measure of information even with limited data. The method, which we call the Fourier Method, is presented in detail, tested for robustness, and its application is demonstrated with both simulated and real spike trains. ii
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
Distributed Estimation of Graph 4-Profiles
We present a novel distributed algorithm for counting all four-node induced
subgraphs in a big graph. These counts, called the -profile, describe a
graph's connectivity properties and have found several uses ranging from
bioinformatics to spam detection. We also study the more complicated problem of
estimating the local -profiles centered at each vertex of the graph. The
local -profile embeds every vertex in an -dimensional space that
characterizes the local geometry of its neighborhood: vertices that connect
different clusters will have different local -profiles compared to those
that are only part of one dense cluster.
Our algorithm is a local, distributed message-passing scheme on the graph and
computes all the local -profiles in parallel. We rely on two novel
theoretical contributions: we show that local -profiles can be calculated
using compressed two-hop information and also establish novel concentration
results that show that graphs can be substantially sparsified and still retain
good approximation quality for the global -profile.
We empirically evaluate our algorithm using a distributed GraphLab
implementation that we scaled up to cores. We show that our algorithm can
compute global and local -profiles of graphs with millions of edges in a few
minutes, significantly improving upon the previous state of the art.Comment: To appear in part at WWW'1
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