Download Document

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project

Document related concepts

Neuroanatomy wikipedia , lookup

Central pattern generator wikipedia , lookup

Metastability in the brain wikipedia , lookup

Biological neuron model wikipedia , lookup

Stimulus (physiology) wikipedia , lookup

Neural coding wikipedia , lookup

Neuropsychopharmacology wikipedia , lookup

Development of the nervous system wikipedia , lookup

Optogenetics wikipedia , lookup

Nervous system network models wikipedia , lookup

Feature detection (nervous system) wikipedia , lookup

Single-unit recording wikipedia , lookup

Channelrhodopsin wikipedia , lookup

Electrophysiology wikipedia , lookup

Multielectrode array wikipedia , lookup

Transcript
Multi-Electrode Arrays (MEAs)
March 25, 2005
Introduction
Multi-electrode Arrays, or MEAs, are quickly becoming a common tool to
investigate patterns of activity.
Often see 4-, 8-, 16-, or even 60-channel array – experimenter’s choice
Made of : microwires (stainless steel, tungsten, platinum/iridium) coated with
nonconductive polymers (Teflon or formvar), or silicon; tip diameter of 1-5 mm
Horizontal array: inter-electrode separation of 400-500 mm
Vertical array: interelectrode separation 250-300 mm
Luo & Katz, 2003
recording in a behaving mouse
Response of a neuron from a CBA male during
investigation of anesthetized stimulus animals including
BALBc male, CBA male, BALBc female, and CBA
female, demonstrating excitation by a BALBc male and
inhibition by a CBA female. Sound, playback of
spiking activity. Colored bar on the right, the cell’s
mean firing rate; red, excitation; blue, inhibition. Note
the gradual increase of neuronal activity, strong
excitatory response to its investigation of the face area
of the BALBc male, and the inhibition following strong
excitation.
An economical multi-channel
cortical electrode array for extended
periods of recording during behavior
Rennaker, R.L., Ruyle, A.M., Street, S.E.
and A.M. Sloan. (2005)
J. Neurosci Meth 142:97-105.
Figure 1 – Rennaker et al.
Figure 2 – Rennaker et al.
Figure 3 – Rennaker et al.
Figure 4 – Rennaker et al.
Figure 5 – Rennaker et al.
Figure 6 – Rennaker et al.
Figure 7 – Rennaker et al.
Multi-unit recordings reveal contextdependent modulation of synchrony in
odor-specific neural ensembles
Christensen, T.A., Pawlowski, V.M., Lei,
H. and J.G. Hildebrand (2000) Nat
Neurosci 3:927-931.
Christensen et al. - Introduction
• Studies in Manduca Sexta, a moth, that is a common model for
insect olfaction.
• The antennal lobe (AL) is a structural and functional analogue of
the mammalian olfactory bulb (OB)
• Glomerulus = functional unit of the AL (and OB) that
receives odor information from a single type of sensory
neuron
• each glomerulus then sends odor-specific information out to
higher level brain centers via the projection neurons (PNs)
• glomeruli are thought to be activated in stereotypical patterns
that then make up an “odor map” of each odor
Figure 1 – Christensen et al.
Figure 2 – Christensen et al.
Figure 3 – Christensen et al.
Figure 4 – Christensen et al.
Figure 5 – Christensen et al.
Recording spikes from a large
fraction of the ganglion cells in a
retinal patch
Segev, R., Goodhouse, J., Puchalla, J. and M.J. Berry II
2003 Nature Neurosci 7:1155-1162.
Segev et al. - Introduction
• Why the retina?
Promising for systematic study of large populations of neurons
because of its modular organization – recording from a small patch of
ganglion cells should sample its full functional diversity
• Limitations of multielectrode arrays?
Sorting the signals obtained into spike trains from individual neurons
• This paper:
Development of a new method of recording and spike sorting that uses
a dense array and combines signals from up to 30 electrodes to sort
spikes. Records from ganglion cells, output cells of retina.
Segev et al. - Introduction
How accomplished?
1. Find the average voltage pattern on the array when a ganglion cell
fires a spike
2. Use an iterative algorithm to match multiple spike patterns to the
raw data
Because every ganglion cell occupies a unique position in space,
and because extracellular signals decay rapidly with distance,
each ganglion cell produces a unique pattern of activity on the
dense array
This unique pattern can be used to identify the source of
overlapping spikes, which might appear ambiguous if using only
one electrode.
Figure 1 – Segev et al.
Figure 2 – Segev et al.
Figure 3 – Segev et al.
Figure 3 – Segev et al.
Figure 3 – Segev et al.
Figure 4 – Segev et al.
Figure 4 – Segev et al.
Figure 5 – Segev et al.
Figure 6 – Segev et al.
Figure 7 – Segev et al.
Figure 7 – Segev et al.
Figure 8 – Segev et al.