Spatiotemporal receptive fields of barrel cortex revealed by reverse correlation of synaptic input.
Of all of the sensory areas, barrel cortex is among the best understood in terms of circuitry, yet least understood in terms of sensory function. We combined intracellular recording in rats with a multi-directional, multi-whisker stimulator system to estimate receptive fields by reverse correlation of stimuli to synaptic inputs. Spatiotemporal receptive fields were identified orders of magnitude faster than by conventional spike-based approaches, even for neurons with little spiking activity. Given a suitable stimulus representation, a linear model captured the stimulus-response relationship for all neurons with high accuracy. In contrast with conventional single-whisker stimuli, complex stimuli revealed markedly sharpened receptive fields, largely as a result of adaptation. This phenomenon allowed the surround to facilitate rather than to suppress responses to the principal whisker. Optimized stimuli enhanced firing in layers 4-6, but not in layers 2/3, which remained sparsely active. Surround facilitation through adaptation may be required for discriminating complex shapes and textures during natural sensing.
1] Department of Neuroscience, Columbia University, New York, New York, USA. [2] Kavli Institute for Brain Science, Columbia University, New York, New York, USA.
Alejandro
A
Ramirez
1] Kavli Institute for Brain Science, Columbia University, New York, New York, USA. [2] Center for Theoretical Neuroscience, Columbia University, New York, New York, USA. [3] Department of Statistics, Columbia University, New York, New York, USA [4] The Grossman Center for the Statistics of Mind, Columbia University, New York, New York, USA.
Eftychios A
EA
Pnevmatikakis
1] Department of Neuroscience, Columbia University, New York, New York, USA. [2] Kavli Institute for Brain Science, Columbia University, New York, New York, USA. [3] Center for Theoretical Neuroscience, Columbia University, New York, New York, USA. [4] Department of Statistics, Columbia University, New York, New York, USA [5] The Grossman Center for the Statistics of Mind, Columbia University, New York, New York, USA.
Josh
J
Merel
1] Department of Neuroscience, Columbia University, New York, New York, USA. [2] Kavli Institute for Brain Science, Columbia University, New York, New York, USA. [3] Center for Theoretical Neuroscience, Columbia University, New York, New York, USA. [4] Department of Statistics, Columbia University, New York, New York, USA [5] The Grossman Center for the Statistics of Mind, Columbia University, New York, New York, USA.
Liam
L
Paninski
1] Department of Neuroscience, Columbia University, New York, New York, USA. [2] Kavli Institute for Brain Science, Columbia University, New York, New York, USA. [3] Center for Theoretical Neuroscience, Columbia University, New York, New York, USA.
Kenneth D
KD
Miller
1] Department of Neuroscience, Columbia University, New York, New York, USA. [2] Kavli Institute for Brain Science, Columbia University, New York, New York, USA.
Randy M
RM
Bruno
D000818:Animals; D002540:Cerebral Cortex / Q000502:physiology*; D005260:Female; D009474:Neurons / Q000502:physiology; D010812:Physical Stimulation / Q000379:methods; D051381:Rats; D017208:Rats, Wistar; D013569:Synapses / Q000502:physiology*; D055366:Synaptic Potentials / Q000502:physiology*; D013997:Time Factors; D014738:Vibrissae / Q000502:physiology*