Inferring network activity from synaptic noise

Inferring network activity from synaptic noise.

During intense network activity in vivo, cortical neurons are in a high-conductance state, in which the membrane potential (V(m)) is subject to a tremendous fluctuating activity. Clearly, this "synaptic noise" contains information about the activity of the network, but there are presently no methods available to extract this information. We focus here on this problem from a computational neuros...

Inferring TV Content from Electrical Noise

In this paper, we critically examine how modern switching power supplies found in many new consumer electronic devices are a source of information leakage through the powerline infrastructure. Unlike current consumption-based security vulnerabilities we show how a single easy to install plug-in device can infer the content of what is being watched on television by simply monitoring the electric...

Inferring Centrality from Network Snapshots

The topology and dynamics of a complex network shape its functionality. However, the topologies of many large-scale networks are either unavailable or incomplete. Without the explicit knowledge of network topology, we show how the data generated from the network dynamics can be utilised to infer the tempo centrality, which is proposed to quantify the influence of nodes in a consensus network. W...

Inferring Network Structure from Cascades

Many physical, biological, and social phenomena can be described by cascades taking place on a network. Often, the activity can be empirically observed, but not the underlying network of interactions. In this paper we offer three topological methods to infer the structure of any directed network given a set of cascade arrival times. Our formulas hold for a very general class of models where the...

Inferring Synaptic E : I Balance from Field Potentials 1 Title : Inferring Synaptic Excitation / Inhibition Balance from Field Potentials 1