Download 1 From Multi Electrode Arrays to Raster Plots

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From Multi Electrode Arrays to Raster Plots
The pioneering work of Hubel and Wiesel based on anatomy and single cell recording on
brain visual areas was very useful.
However at that time, little was known about the
properties of the retinal neural network. Similarly, today, the anatomical description of
dierent types of G cells is a well known piece of literature, in contrast to their collective neural response that is partly missing. To overcome limitations of single-electrodes
recording and to access to the coding response of a population of neurons, multi-electrodes
(MEA) devices are used in physiology. MEA devices are formed by an array of isolated
electrodes (64 to 256, separated from 30- 200 microns each). When in contact with a small
piece of neural tissue, a MEA is able to record the simultaneous activity (spike and/or eld
potential) from, e.g., 10-150 G cells. The nal goal is to produce from the MEA signal a
raster plot of G cells activity, namely a graph with time in abscissa and a neuron labeling
in ordinate such that a vertical bar is drawn each time a neuron emits a spike.
This
poses an important challenge for signal processing: to sort out from a complex (spatial
and temporal) neural signal superposition recording the contribution of each cell. With
the recent increase in the number of electrodes of MEA devices, the necessity of adequate
spike sorting algorithms turns out to be critical. Recently the Berry's lab at Princeton has
developed an ecient method, enabling to sort out, from a 256 MEA experiment, about
200 dierent G cells (personal communication). MEA devices constitute an excellent tool
to track the physiological properties of G cells as well as their coding capacity.
Before
the introduction of MEA devices, the neural coding properties of single G cells was study
using intra or extra cellular electrodes, giving a limited sense of their collective role. In
that respect, the work of Markus Meister using MEA devices was pioneer. With simple
stimulus, like checkerboard random white noise, and spike sorting algorithms these authors
were able to determinate the number of spiking cells and their respective RF. They have
shown that concerted G cells are critical, not only for retina development, but for the
neural coding processing.
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Figure 1: (a) Data from neural tissue are collected with a multielectrode array. Here, a
retina is placed over the electrodes. Typically, tens to hundreds of neurons are recorded,
but tens of thousands of neurons are in the slice.
(b) Activity in individual neurons is
detected as very short voltage spikes. The time of minimum voltage is marked by a dot.
(c) All spikes from all neurons are plotted over time, where a dot represents the time of
a spike. (d) The state of an ensemble of ve neurons in one time bin is represented as a
vector, where ones indicate spikes and zeros indicate no spikes. Time bins are usually a
few milliseconds. (e) The goal of the second-order maximum entropy model is to explain
the probability distribution of states found in the data, given only information about
the ring rates and pairwise correlations between neurons. Here a schematic probability
distribution is shown for data and the model.
Neural Networks (Entropy))
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(Maximum Entropy Approaches to Living