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Overcoming these limitations, Brecht et al. [19] stimulated single neurons in infragranular layers of rat primary motor cortex and found that single-cell stimulation of pyramidal neurons is capable of producing a behavioral output, i.e., bursts of low-amplitude rhythmic whisking. The latency and amplitude of the movement depended on the number and the frequency of evoked spikes, respectively. The main conclusion is that, in vibrissa motor cortex, activity modulation of single neurons in awake animals indeed can have an effect on ongoing behavior. Stimulating single neurons in the lateral facial nucleus which directly project to the whisker musculature revealed that even a single spike is sufficient to evoke detectable whisker movements [20].

In all single-cell stimulation studies discussed thus far, multiple action potentials were induced at high frequencies. Choosing a different approach, London et al. [34] investigated whether the introduction of a single action potential into an individual cortical neuron in layer 5 causes a measurable change in the network activity. Similar to our approach, multi-unit activity was recorded with a 16-channel multi-electrode array positioned in close proximity (< 300 µm) of the cortical neuron, which was electrically stimulated with a patch electrode. Their finding that a single spike added to the network yields measureable effects is perhaps the strongest demonstration of the power of single neurons to influence overall cortical processing.

At present, little is known about how evoked spike patterns relate to network effects. Stimulating in motor cortex, Brecht et al. [19] found larger movement amplitudes for higher spike frequencies. In contrast, Doron et al. [24] found a negative correlation between spike frequency and the magnitude of the perceptual bias, and a more pronounced effect for irregular as compared to regular spike trains. Especially the latter effect is highly interesting because it suggests that the network is highly sensitive to patterned stimulation.

In this proof of principle study, we compared spike counts in and outside of stimulation windows, collapsing all juxtacellularly stimulated neurons. With larger samples, it will be possible to (1) separately analyze the effects of neurons in different layers and with different stimulation profiles (e.g., steady versus burst firing, Figure 5), (2) separately analyze the effects within and across different cortical layers, and (3) separating putative excitatory (broad-spike) and inhibitory (thin-spike) neurons [24,25].

The voltage traces of the juxtacellular recordings were processed in Spike 2 and band-pass filtered from 300 to 3000 Hz (finite impulse response filter, transition gap: 200 Hz). Spikes were extracted with amplitude thresholds at 0.5 mV or higher (noise band usually ~±0.15 mV). Due to the outstanding signal-to-noise ratio owing to the juxtacellular configuration, no further spike sorting techniques were applied, unless further units of different amplitude were readily identifiable. The spiking activity was assessed in a 220 ms analysis window starting at the onset of the stimulus until the offset of the stimulus, including the 10 ms rise and fall phases. The activity of the stimulus was compared to the activity during a baseline of 220 ms, directly preceding the stimulus onset. Post-stimulus activity was assessed in a 220 ms analysis window directly following the stimulus offset.

The voltage traces of local network recordings were processed in Offline Sorter and high-pass filtered at 500 Hz (2-pole Bessel). Events surpassing a threshold of four (JR16) or five (JR20) standard deviations below the noise band were considered putative spikes. Semi-automated sorting strategies were applied to separate distinct single- and multi-units, which were collapsed for the analyses presented in this paper.

To exclude potential stimulus onset or offset artifacts, we moved the analysis window by 5 ms into the rise phase of the juxtacellular stimulation. To assess the effect of juxtacellular stimulation on network activity, we performed three different analyses: first, we counted spikes both during the stimulation window as well as during a 200-ms window immediately preceding stimulation, and performed paired t-tests for each network unit. The resulting number of units with significant modulation was then compared to the expected number using a binomial test. Second, we performed a bootstrapping analysis, in which we selected random time points during the recording and then compared spike counts in the 200 ms before to those during 200 ms after those time points, and performed paired t-tests. This procedure was repeated 1000 times to derive a distribution of the numbers of significant units (which were entirely false positives), and compared the resulting bootstrapped distribution to the actually observed numbers of significant units. Third, we calculated mean firing rates during baseline and stimulation for each neuron, and performed a single paired t-test across all network units. 2b1af7f3a8