Correlated Neuronal Activity: High- and Low-Level Views - Computational Neuroscience: A Comprehensive Approach - page 342

neuron's spikes. The key quantity here is the spike-triggered average of the LFP, or
STA. The STA is obtained by adding, for each spike recorded, a segment of the LFP
centered on the time of the spike; the final sum is then divided by the total number
of spikes. The result is the average LFP waveform that is observed around the time
of a spike. STAs were computed for attention outside and inside the receptive field.
They were similar, but not identical: rapid fluctuations were more pronounced when
attention was directed inside the receptive field; in the Fourier decomposition, power
in the low frequency band (0-17 Hz) decreased while power in the high frequency
band (30-70 Hz) increased. Because the STA reflects the correlation between one
neuron and the neighboring population, the interpretation is that, as attention shifts
to the receptive fields of a cluster of neurons, these become more synchronized at
high frequencies and less so at low frequencies. Although the changes in synchrony
were modest — on average, low-frequency synchronization decreased by 23% and
high-frequency synchronization increased by 19% — changes in firing rate were also
small; these were enhanced by a median of 16% with attention inside the receptive
field. Under these conditions the changes in synchrony could be significant in terms
of their impact on the responses of downstream neurons.
The study just discussed [35] suggests that synchrony specifically in the gamma
band (roughly 30-80 Hz) may enhance the processing of information in some way.
But what exactly is the impact of such synchronization? Another recent study [34]
suggests at least one measurable consequence: the latencies of synchronized neu-
rons responding to a stimulus may shift in unison. In this case the paradigm was
very simple: oriented bars of light were flashed and the responses of two or more
neurons in primary visual cortex (V1) were recorded, along with LFPs. Neurons
were activated by the stimuli, and the key quantity examined was the time that it
took the neurons to respond — the latency — which was calculated on each trial.
Latencies covaried fairly strongly from trial to trial (mean correlation coefficient of
0.34, with a range from 0.18 to 0.55), so pairs of neurons tended to fire early or late
together. This tendency depended on the amount of gamma power in the LFPs right
before the stimulus. When the LFPs from two electrodes both had a strong gamma
component, the latency covariation between the two recorded neurons from the same
pair of electrodes was high. Note that the spectral composition of the LFPs was only
weakly related to changes in firing rate, so short latencies were probably not due
to changes in excitability. This means that, if neurons get synchronized around 40
Hz right before a stimulus is presented, they will respond at about the same time
[34]. In other words, while the mean firing rates are mostly insensitive to shifts in
oscillation frequencies, the time spread in the evoked spikes from multiple neurons is
much smaller when the gamma oscillations are enhanced. This could certainly have
an impact on a downstream population driven by these neurons [18-67]. Thus, the
modulation of latency covariations [34] is a concrete example of how the synchrony
of a local circuit may be used to control the strength of a neural signal.
Finally, we want to mention two other studies [36, 37] that also investigated the
synchronization of V1 neurons, this time using an interocular rivalry paradigm. In
rivalry experiments, different images are shown to the two eyes but only one im-
age is perceived at any given moment [52]. The perception flips from one image