Abstract
The influence of cortical cell spiking activity on nearby cells has been studied extensively in vitro. Less is known, however, about the impact of single cell firing on local cortical networks in vivo. In a pioneering study, Kwan and Dan (Kwan and Dan, 2012) reported that in mouse layer 2/3 (L2/3), under anesthesia, stimulating a single pyramidal cell recruits ~2.1% of neighboring units. Here we employ two-photon calcium imaging in layer 2/3 of mouse V1, in conjunction with single-cell patch clamp stimulation in layer 2/3 or layer 4, to probe, in both the awake and lightly anesthetized states, how (i) activating single L2/3 pyramidal neurons recruits neighboring units within L2/3 and from layer 4 (L4) to L2/3, and whether (ii) activating single pyramidal neurons changes population activity in local circuit. To do this, it was essential to develop an algorithm capable of quantifying how sensitive the calcium signal is at detecting effectively recruited units (“followers”). This algorithm allowed us to estimate the chance of detecting a follower as a function of the probability that an epoch of stimulation elicits one extra action potential (AP) in the follower cell. Using this approach, we found only a small fraction (<0.75%) of L2/3 cells to be significantly activated within a radius of ~200 μm from a stimulated neighboring L2/3 pyramidal cell. This fraction did not change significantly in the awake vs. the lightly anesthetized state, nor when stimulating L2/3 vs. underlying L4 pyramidal neurons. These numbers are in general agreement with, though lower than, the percentage of neighboring cells (2.1% pyramidal cells and interneurons combined) reported by Kwan and Dan to be activated upon stimulating single L2/3 pyramidal neurons under anesthesia (Kwan and Dan, 2012). Interestingly, despite the small number of individual units found to be reliably driven, we did observe a modest but significant elevation in aggregate population responses compared to sham stimulation. This underscores the distributed impact that single cell stimulation has on neighboring microcircuit responses, revealing only a small minority of relatively strongly connected partners.One sentence summaryPatch-clamp stimulation in conjunction with 2-photon imaging shows that activating single layer-2/3 or layer-4 pyramidal neurons produces few (<1% of local units) reliable single-cell followers in L2/3 of mouse area V1, either under light anesthesia or in quiet wakefulness: instead, single cell stimulation was found to elevate aggregate population activity in a weak but highly distributed fashion.
Highlights
It is important to understand how a single neuron’s spiking activity influences nearby cortical circuit function
It has been argued that several dozen neurons need to be simultaneously active to drive behavioral tasks in the mouse (Huber et al, 2008), or to elicit postsynaptic spiking in guinea pig primary visual cortex (V1) slices (Sáez and Friedlander, 2009)
The patched cell and the target population of neurons were labeled prior to patching with bulk-injected Oregon-Green BAPTA (OGB, Figure 1a; see section Methods), which allowed us to monitor the activity of individual units
Summary
It is important to understand how a single neuron’s spiking activity influences nearby cortical circuit function. Using a simple network model, Shadlen and Newsome (1998) estimated that, absent inhibition, a neuron can produce an AP in response to 10–40 input spikes with 10–20 ms interspike-intervals (ISI) This suggests that physiological presynaptic activity in just a single cell is potentially capable of driving its postsynaptic partners, if the cell fires at high rates. Single unit firing has been reported to (i) increase the firing rate of postsynaptic targets (London et al, 2010), (ii) stabilize network activity sparseness (Ikegaya et al, 2013), (iii) elicit whisker movements (Brecht et al, 2004), (iv) switch between global up-and down states (Li et al, 2009), and (v) elicit behavioral somatosensory responses (Houweling and Brecht, 2008). These studies suggest that single unit activity can influence neural network state (Li et al, 2009) and even animal behavior (Houweling and Brecht, 2008)
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