RT Journal Article
SR Electronic
T1 From Neurons to Circuits: Linear Estimation of Local Field Potentials
JF The Journal of Neuroscience
JO J. Neurosci.
FD Society for Neuroscience
SP 13785
OP 13796
DO 10.1523/JNEUROSCI.2390-09.2009
VO 29
IS 44
A1 Malte Rasch
A1 Nikos K. Logothetis
A1 Gabriel Kreiman
YR 2009
UL http://www.jneurosci.org/content/29/44/13785.abstract
AB Extracellular physiological recordings are typically separated into two frequency bands: local field potentials (LFPs) (a circuit property) and spiking multiunit activity (MUA). Recently, there has been increased interest in LFPs because of their correlation with functional magnetic resonance imaging blood oxygenation level-dependent measurements and the possibility of studying local processing and neuronal synchrony. To further understand the biophysical origin of LFPs, we asked whether it is possible to estimate their time course based on the spiking activity from the same electrode or nearby electrodes. We used “signal estimation theory” to show that a linear filter operation on the activity of one or a few neurons can explain a significant fraction of the LFP time course in the macaque monkey primary visual cortex. The linear filter used to estimate the LFPs had a stereotypical shape characterized by a sharp downstroke at negative time lags and a slower positive upstroke for positive time lags. The filter was similar across different neocortical regions and behavioral conditions, including spontaneous activity and visual stimulation. The estimations had a spatial resolution of ∼1 mm and a temporal resolution of ∼200 ms. By considering a causal filter, we observed a temporal asymmetry such that the positive time lags in the filter contributed more to the LFP estimation than the negative time lags. Additionally, we showed that spikes occurring within ∼10 ms of spikes from nearby neurons yielded better estimation accuracies than nonsynchronous spikes. In summary, our results suggest that at least some circuit-level local properties of the field potentials can be predicted from the activity of one or a few neurons.