Large-scale, high-density (up to 512 channels) recording of local circuits in behaving animals

Monitoring representative fractions of neurons from multiple brain circuits in behaving animals is necessary for understanding neuronal computation. Here, we describe a system that allows high-channel-count recordings from a small volume of neuronal tissue using a lightweight signal multiplexing hea...

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Bibliographic Details
Published in:Journal of neurophysiology 2014-03, Vol.111 (5), p.1132-1149
Main Authors: Berényi, Antal, Somogyvári, Zoltán, Nagy, Anett J, Roux, Lisa, Long, John D, Fujisawa, Shigeyoshi, Stark, Eran, Leonardo, Anthony, Harris, Timothy D, Buzsáki, György
Format: Article
Language:English
Subjects:
Animals
Behavior, Animal - physiology
Biotechnology
Cerebral Cortex - physiology
Cerebral Cortex - surgery
Electrodes, Implanted
Innovative Methodology
Life Sciences
Male
Mice
Nerve Net - physiology
Nerve Net - surgery
Neurons - physiology
Rats
Rats, Long-Evans
Signal Processing, Computer-Assisted
Software
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Summary:Monitoring representative fractions of neurons from multiple brain circuits in behaving animals is necessary for understanding neuronal computation. Here, we describe a system that allows high-channel-count recordings from a small volume of neuronal tissue using a lightweight signal multiplexing headstage that permits free behavior of small rodents. The system integrates multishank, high-density recording silicon probes, ultraflexible interconnects, and a miniaturized microdrive. These improvements allowed for simultaneous recordings of local field potentials and unit activity from hundreds of sites without confining free movements of the animal. The advantages of large-scale recordings are illustrated by determining the electroanatomic boundaries of layers and regions in the hippocampus and neocortex and constructing a circuit diagram of functional connections among neurons in real anatomic space. These methods will allow the investigation of circuit operations and behavior-dependent interregional interactions for testing hypotheses of neural networks and brain function.
ISSN:0022-3077
1522-1598
DOI:10.1152/jn.00785.2013