Anisotropic connectivity implements motion-based prediction in a spiking neural network

 The motion extrapolation problem. Sensory input, such as the smooth motion of a dot in visual space, may be perturbed by disruption of sensory drive, like when the eye blinks during a visual stimulation. It is essential that some mechanisms may fill this blank: this defines the motion extrapolation problem. We first define the problem by parameterizing a generic input and its perturbation. Left: The input is a Gaussian hill of activity in a topographically organized space, moving on a straight trajectory. We show here a snapshot in time of the input (blue) and the resulting input activity to the network (gray) for a period of $400~\si{\milli\second}$. This corresponds for instance to the activation of a low-level visual area to a single dot represented by a bell-shaped hill of activity (blue blurred circle). In addition, this input carries information about the motion of the object (blue arrow) and drives neurons which have a close selectivity in position and velocity (gray arrows). Right: We show the time course of the input for one representative neuron (denoted by the yellow star in the left panel). Top: The blue trace shows the envelope of the inhomogeneous Poisson process that creates the input spike train. For $0~\si{\milli\second} < t \leq 200~\si{\milli\second}$ and $600~\si{\milli\second} < t \leq 800~\si{\milli\second}$ the stimulus is blanked, that is, that all neurons in the sensory layer receive input from a Poisson process with the same rate. We permuted the input vector fed into the network among all the cells in the network for each time step during the blank. Black vertical lines indicate input spikes. Bottom: Histogram of the input spike train with a bin size of $50~\si{\milli\second}$. This shows clearly the missing information during the blank. We define the goal of solving the motion extrapolation problem as representing the prediction of information on motion (speed and position) during the blank.

reference

• Bernhard A. Kaplan, Anders Lansner, Guillaume S. Masson, Laurent U. Perrinet. Anisotropic connectivity implements motion-based prediction in a spiking neural network, URL . Front. Comput. Neurosci. 7:112. , 2013 abstract.

 This work was supported by the FACETS ITN project (EU funding, grant number 237955), a 'Marie-Curie Initial Training Network'.

welcome:
• getACL = 0.015s
• i18n_init = 0.025s
• init = 0.026s