How we perceive what we see depends on the context in which we see it, such as what we are doing at the time. For example, we perceive a park landscape differently when we are running through it than when we are sitting on a park bench. Behavior can also alter neuronal responses in the brain. Indeed, the neurons in the part of the brain that receives information related to vision (known as the visual cortex) respond differently to visual stimuli when an animal is moving compared to when the animal is still. However, while some recent studies revealed that specific types of neurons become more or less responsive during movement, others reported the opposite results.

One hypothesis that would explain these contradictory findings would be if the way that behavior, in this case movement, affects neuronal responses also depends on the external context in which the movement happens. Now, Pakan et al. have tested this hypothesis by imaging the activity of different types of neurons in the primary visual cortex of mice that were either running on a treadmill or staying still. The experiments were conducted in two different contexts: in total darkness (in which the mice could not see) and in the presence of display screens (which provided the mice with visual stimulation).

Pakan et al. confirmed that running does indeed affect the activity of specific neurons in different ways in different contexts. For example, when the mice received visual stimulation, the three main classes of neurons that send inhibitory signals in the visual cortex became more active during running. However, when the mouse ran in the dark, two of these neuron types became more active during running while the third type of neuron was unresponsive. This finding reveals more about the dynamic nature of inhibitory activity that strongly depends on the animal’s behaviour. It also shows how these neurons influence the excitatory neurons in the visual cortex, which send information to the rest of the brain for further processing towards perception.

The next step will be to identify what precise mechanism makes these neurons respond differently in unique contexts, and to tease apart how these movement-dependent signals affect the way animals perceive visual stimuli.

Sensory perceptions are modulated by the context in which they are experienced. In primary sensory areas, neuronal responses to sensory inputs are also modulated by behavioral states, including level of arousal, attention and locomotion (Iriki et al., 1996; Petersen and Crochet, 2013; Bennett et al., 2014; McGinley et al., 2015). In vivo recordings in awake mice have shown that locomotion modulates the response properties of neurons in the primary visual cortex (V1), resulting in an increased gain of excitatory neuron responses to visual stimuli (Niell and Stryker, 2010; Keller et al., 2012; Bennett et al., 2013; Polack et al., 2013; Saleem et al., 2013; Erisken et al., 2014; Reimer et al., 2014). However, the neuronal circuits underlying this response modulation are unclear.

Recent studies have revealed that a specific subclass of inhibitory neurons, expressing vasoactive intestinal peptide (VIP), strongly increase their activity during locomotion (Fu et al., 2014; Reimer et al., 2014; Jackson et al., 2016). VIP neurons mainly inhibit a second class of inhibitory neurons, expressing somatostatin (SST; Figure 1A; Pfeffer et al., 2013; Jiang et al., 2015; Urban-Ciecko and Barth, 2016). It has been proposed that cholinergic activation of VIP neurons during locomotion would inhibit SST neurons, alleviating inhibition onto excitatory neurons and, as a consequence, increase the gain of excitatory neuron visual responses (Figure 1B; Fu et al., 2014). However, a previous study has reported an increase of SST spiking activity in layer 2/3 during locomotion (Polack et al., 2013), an observation that challenges the hypothesis of an SST-cell mediated disinhibitory circuit. The aforementioned recordings of SST neuronal activity were acquired in different sensory contexts, either in darkness or during the presentation of visual stimuli. One hypothesis that would explain the discrepancies between these results is that V1 neuronal responses to locomotion are context-dependent.

Figure 1

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In this study, we tested this hypothesis by directly comparing the locomotion responses of excitatory and inhibitory neurons in darkness and during visual stimulation. We used two-photon calcium imaging to monitor the activity of excitatory neurons as well as of three non-overlapping populations of inhibitory neurons (VIP, SST and parvalbumin [PV] neurons) in layer 2/3 and layer 4 of V1 in awake behaving mice. Our results show that during visual stimulation these three classes of interneurons increase their activity with locomotion, challenging the model of a disinhibitory circuit mediated through SST neurons. We found that locomotion affects the activity of inhibitory circuits differently in darkness and during visual stimulation, revealing a context-dependent, cell type specific response to locomotion in V1. The highest proportion of context-dependent responses to locomotion was found among SST neurons, which play a central role in V1 microcircuits. We suggest alternative mechanisms of how locomotion modulates the neuronal activity in V1, highlighting the dynamic nature of interneurons function that strongly depends on the behavioral context of the animal.

We compared the modulation of neuronal activity by locomotion in the mouse primary visual cortex (V1), between two different sensory contexts: darkness and visual stimulation. To do this, we used two-photon calcium imaging in head-fixed mice that ran freely on a cylindrical treadmill (Figure 1C). The relative changes in somatic fluorescence of the genetically-encoded calcium indicator GCaMP6f were used as a non-linear readout of the neuronal spiking activity (Chen et al., 2013). Inhibitory neuronal subtypes were labeled by injecting adeno-associated viruses (AAVs) into V1 of Cre-recombinase transgenic mice (PV-, SST-, or VIP-Cre mice) for the Cre-inducible expression of the genetically-encoded calcium indicator GCaMP6f (Figure 1D–E; Chen et al., 2013). To image excitatory neurons, we co-injected a floxed version of GCaMP6f and an AAV where Cre expression is driven by a CaMKII promoter, into C57/BL6 mice. After 2–3 weeks of expression, we recorded the running speed and GCaMP6f signals simultaneously, both in total darkness and during visual stimulation (Figure 1E).

Layer 2/3 celltype-specific responses to locomotion differ in darkness and during visual stimulation

Excitatory neurons

We quantified, for each excitatory neuron (n = 1124 in 12 mice), the mean amplitude of calcium transients during locomotion periods and stationary periods, both during visual stimulation (drifting gratings) and in darkness (Figure 2A(i),B(i)). In agreement with previous electrophysiological observations (Niell and Stryker, 2010; Keller et al., 2012; Bennett et al., 2013; Polack et al., 2013; Saleem et al., 2013; Erisken et al., 2014; Reimer et al., 2014), we observed that, on average, locomotion increased the amplitude of calcium transients in excitatory neurons during visual stimulation (Figure 2B(i), Figure 2—figure supplement 1B(i) mean change in fluorescence [∆F/F₀] = 0.12 ± 0.02 locomotion versus 0.07 ± 0.01 stationary; p<0.001, n = 12, Wilcoxon signed rank test). We quantified the effect of locomotion by calculating a locomotion modulation index (LMI) for each neuron, corresponding to the difference between the mean ∆F/F₀ during locomotion (R_L) and stationary (R_s) periods, normalized by the sum of the mean ∆F/F₀ during both behavioral states (LMI = (R_L – R_s)/(R_L + R_s)). An LMI equal to 0 indicates no difference between locomotion and stationary periods, while an LMI equal to 0.5 indicates that the average amplitude of calcium transients was three times higher during locomotion than during stationary periods. Comparing the distribution of LMIs between the two sensory contexts, we found that the modulation of the activity of excitatory neurons by locomotion was significantly different in darkness compared to visual stimulation (Figure 2C(i),D(i); mean of median LMI: 0.07 ± 0.02 darkness versus 0.19 ± 0.02 visual stimulation; p=0.001, n = 12, Kruskal–Wallis test). During visual stimulation, 47 ± 4% of excitatory neurons were significantly locomotion responsive (see Materials and methods for locomotion responsive criteria), compared with 28 ± 4% in darkness. Additionally, in the dark, a small proportion of neurons were inhibited by locomotion, decreasing their activity during locomotion periods relative to stationary periods (10 ± 1% of neurons).

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VIP neurons

As reported in previous studies (Fu et al., 2014; Reimer et al., 2014; Jackson et al., 2016), we found that VIP neurons (n = 210 in 12 mice) strongly responded to locomotion (Figure 1E and Figure 2A(ii),B(ii)). This was true both in darkness (mean ∆F/F₀ = 0.51 ± 0.12 locomotion versus 0.10 ± 0.03 stationary; p<0.001, n = 12, Wilcoxon signed rank test) as well as during visual stimulation (mean ∆F/F₀ = 0.42 ± 0.14 locomotion versus 0.09 ± 0.02 stationary; p<0.001, n = 12) with no significant difference in the average LMI between sensory contexts (Figure 2C(ii),D(ii), mean of median LMI: 0.60 ± 0.05 darkness versus 0.49 ± 0.06 visual stimulation; p=0.106, n = 12, Kruskal–Wallis test; see also Figure 2—figure supplement 1B(ii)). A high proportion of VIP neurons were significantly locomotion responsive in both sensory contexts (85 ± 7% in darkness and 79 ± 6% during visual stimulation).

In order to compare our results directly with previous findings (Fu et al., 2014), we calculated the cross-correlation between VIP calcium signals and running speed. We confirmed the presence of a single positive peak around time zero, both in darkness and during visual stimulation (Figure 2—figure supplement 2A(ii)). We also observed a lower amplitude during visual stimulation but this decrease was not significant (mean zero-time correlation: 0.26 ± 0.04 in darkness versus 0.20 ± 0.02 during visual stimulation; p=0.225, n = 12, Kruskal–Wallis test; Figure 2—figure supplement 2C). Similarly, the mean ∆F/F₀ (Figure 2—figure supplement 3C(ii) and the mean LMI (Figure 2D(ii)) of VIP neurons also decreased during visual stimulation, without reaching significance (mean ∆F/F₀ = 0.51 ± 0.12 in darkness versus 0.42 ± 0.14 during visual stimulation; p=0.151, n = 12, Wilcoxon signed rank test).

SST neurons

In contrast to VIP neurons, responses of SST neurons (n = 79 in 11 mice) to locomotion were found to be highly context-dependent. During visual stimulation, the mean ∆F/F₀ during locomotion periods was significantly higher than during stationary periods (Figure 2B(iii), Figure 2—figure supplement 1B(iii); mean ∆F/F₀ = 0.25 ± 0.05 locomotion versus 0.10 ± 0.03 stationary; p=0.001, n = 11, Wilcoxon signed rank test). However, in darkness, SST neurons were either non-responsive, increased or even decreased their activity during locomotion with, on average, no significant difference between locomotion and stationary periods (Figure 2A(iii), Figure 2—figure supplement 1B(iii); mean ∆F/F₀ = 0.06 ± 0.02 locomotion versus 0.06 ± 0.01 stationary; p=0.102, n = 11, Wilcoxon signed rank test). As a result, the modulation of SST neuron responses by locomotion was found to be significantly different across sensory contexts (Figure 2C(iii),D(iii), mean of median LMI: 0.06 ± 0.04 darkness versus 0.33 ± 0.06 visual stimulation; p=0.002, n = 11, Kruskal–Wallis test). During visual stimulation, 63 ± 7% of SST neurons were significantly locomotion responsive (increasing their activity) and only 4 ± 3% were decreasing their activity during locomotion. In darkness, the percentage of neurons increasing their activity dropped to 24 ± 6% with an additional 11 ± 5% of SST neurons decreasing their activity during locomotion.

In line with these results, the cross-correlation between SST calcium transients and running speed significantly increased during visual stimulation compared to darkness (mean zero-time correlation = 0.04 ± 0.01 in darkness versus 0.13 ± 0.01 during visual stimulation; p=0.001, n = 11, Kruskal–Wallis test; Figure 2—figure supplement 2C). Notably, SST neurons were strongly responsive to visual stimulation (Figure 2—figure supplement 3(iii; mean ∆F/F₀ during locomotion = 0.06 ± 0.02 darkness versus 0.25 ± 0.05 visual stimulation; p=0.001, n = 11, Wilcoxon signed rank test). These results indicate that most SST neurons respond to visual stimuli and, in addition to this visual response, they become responsive to locomotion. In darkness, however, they have low spontaneous activity and are largely non-responsive to locomotion (Figure 2E).

PV neurons

Finally, PV neurons (n = 199 in 13 mice) were strongly responsive to locomotion in both sensory contexts (Figure 2A(iv),B(iv), Figure 2—figure supplement 1B(iv); dark: mean ∆F/F₀ = 0.33 ± 0.07 locomotion versus 0.13 ± 0.02 stationary; p=0.001;, visual stimulation: mean ∆F/F₀ = 0.41 ± 0.08 locomotion versus 0.16 ± 0.03 stationary; p<0.0001; n = 13 Wilcoxon signed rank test), with no significant difference between sensory conditions (Figure 1E, Figure 2C(iv),D(iv); mean of median LMI: 0.32 ± 0.06 darkness versus 0.35 ± 0.04 visual stimulation; p=0.663, n = 13, Kruskal–Wallis test). Similarly, the cross-correlation between running speed and calcium transients showed a positive peak around time zero both in darkness and during visual stimulation, with no significant difference (p=0.778; n = 13, Kruskal–Wallis test; Figure 2—figure supplement 2).

Diversity of context-dependent locomotion responses within cell types

While comparisons of a neuronal population’s LMI distribution (Figure 2C) indicates how, on average, that cell type is modulated by locomotion in different sensory contexts, it does not provide information about the context-dependent responses of single neurons. For instance, the average LMI could be the same in darkness and during visual stimulation even though individual neurons may have large changes in their LMI, which cancel out when considering the population as a whole.

In order to show the diversity of locomotion responses within each neuronal subtype, we examined the LMI value in darkness versus during visual stimulation for each neuron (Figure 3A). Neurons near the identity line show context-independent locomotion responses (similar LMI in darkness and during visual stimulation), while the other neurons changed their response to locomotion from one context to another (context-dependent responses). We first quantified this diversity by calculating the difference between the LMI value during visual stimulation and the LMI value in darkness for each neuron (Figure 3B). These results confirmed that VIP neurons displayed mainly context-independent locomotion responses (Figure 3B(ii), narrow distribution, centered around 0), while locomotion responses of SST neurons were mainly context dependent (Figure 3B(iii), broad distribution shifted towards positive values). Both excitatory and PV neuronal populations included a diversity of locomotion responses (broad distributions). To quantify the proportions of context-independent and context-dependent neurons in each cell type, we first determined the variability of the locomotion responses for each context by comparing neuronal responses across odd and even locomotion periods (Figure 3—figure supplement 1; see Materials and methods). We found high correlation values for all neuronal populations, both in darkness and during visual stimulation (0.676 < R < 0.944; p<0.0001), indicating a general low variability of the responses across different locomotion periods in both contexts. We determined the proportion of context-dependent neurons meeting two criteria: i) with a response that was significantly different across contexts (neurons distance from the identity line in Figure 3A, to estimate the error on the LMI in both dark and stimulated conditions for each neuron, bootstrapping was employed (see Materials and methods)), and ii) with low variability of locomotion responses (Figure 3—figure supplement 1).

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These results confirm that most VIP neurons were context-independent, remaining locomotion-responsive in both sensory contexts (66%), with only 17% of neurons showing context-dependent responses (Figure 3A(ii), Figure 3—figure supplement 2). The proportion of context-dependent neurons was the highest among SST neurons, with 49% of neurons showing context-dependent responses to locomotion (Figure 3A(iii), Figure 3—figure supplement 2). Both excitatory and PV neurons had approximately the same proportion of context-dependent neurons (22% for excitatory and 25% for PV neurons) (Figure 3A(i), (iv)).

Finally, we tested whether context-dependent neurons differ from context-independent ones with regard to the following characteristics: percentage of visually responsive neurons, orientation selectivity and direction selectivity. We did not find any significant difference in any neuronal population (comparisons between context-dependent and context independent neurons for each cell type, OSI, p>0.261; DSI p>0.093, Kruskal–Wallis test), suggesting that the mechanisms underlying the modulation of locomotion responses differ from those determining the selectivity of visual responses.

Layer 4 excitatory and inhibitory responses to locomotion are similar to layer 2/3

Layer 2/3 neurons receive sensory information from excitatory neurons in layer 4, the main thalamo-recipient layer, as well as top-down information from higher cortical areas (Niell, 2015). In addition, these neurons receive subcortical inputs from the dorsal lateral geniculate nucleus as well as neuromodulatory inputs (Polack et al., 2013; Fu et al., 2014; Lee et al., 2014). Context-dependent locomotion responses of layer 2/3 neurons may thus come from one of these distinct inputs or from a combination of them. By using the same approach as for layer 2/3 neurons, we recorded locomotion responses in layer 4 neurons (excitatory n = 331; VIP n = 57; SST n = 74; PV n = 109; in 6, 4, 6 and 6 mice, respectively). As in layer 2/3, we used local injections of AAVs into V1 for the Cre-inducible expression of the genetically-encoded calcium indicator GCaMP6f. However, we observed that on average the GCaMP6f labelling in layer 4 was sparser than in layer 2/3 (Figure 1D). Thus, we cannot exclude that we preferentially labelled subtypes of layer 4 neurons in which transduction efficiency with these AAV vectors would be higher. The quantification of locomotion responses showed no significant difference between layer 2/3 and layer 4 neurons, in any cell type, both in darkness and during visual stimulation (Figure 4B,C). The results showed a higher mean LMI value for PV neurons in layer 4 (0.45 ± 0.04) compared to layer 2/3 (0.35 ± 0.04) during visual stimulation. However, this did not reach significance; p=0.058, Mann-Whitney U-test). In addition, the results showed similar proportions of context-dependent responses in layer 4 as described in layer 2/3 (Figure 4A, see also Figure 3A; context-dependent neurons: Exc, L2/3: 22%, L4: 17%; VIP, L2/3: 17%, L4: 26%; SST, L2/3: 49%, L4: 42%; PV, L2/3: 25%, L4: 23%).

Figure 4

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The increased gain of visual responses during locomotion provides a model to elucidate the circuit mechanisms underlying behavioral-state dependent changes of sensory responses. In this study, we found that the modulation of neuronal activity by locomotion is context-dependent and cell type specific, in layer 2/3 and layer 4 of mouse V1. During periods of visual stimulation, locomotion increases the activity of excitatory neurons as well as of three classes of inhibitory neurons (VIP, SST, PV; Figure 2E). These results indicate that the enhancement of excitatory neuron visual responses during locomotion does not result from the inhibition of SST neurons, in mouse V1. Our findings thus challenge the generality of a disinhibitory circuit involving VIP, SST and pyramidal neurons for the gain control of sensory responses by behavioral state.

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Author details

1. Janelle MP Pakan

   Centre for Integrative Physiology, School of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   JMPP, Conception and design, Acquired the data, Analysed and interpreted the data, Revised the manuscript

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0001-9384-8067

2. Scott C Lowe

   Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   SCL, Developed the NMF-based neuropil correction method and analysed the data

   Competing interests

   The authors declare that no competing interests exist.

3. Evelyn Dylda

   Centre for Integrative Physiology, School of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   ED, Acquired and analysed the data

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-1883-4498

4. Sander W Keemink

   1. Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
   2. Bernstein Center Freiburg, Faculty of Biology, University of Freiburg, Freiburg, Germany

   Contribution

   SWK, Developed the NMF-based neuropil correction method

   Competing interests

   The authors declare that no competing interests exist.

5. Stephen P Currie

   Centre for Integrative Physiology, School of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   SPC, Acquired data

   Competing interests

   The authors declare that no competing interests exist.

6. Christopher A Coutts

   Centre for Integrative Physiology, School of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   CAC, Analyzed the data

   Competing interests

   The authors declare that no competing interests exist.

7. Nathalie L Rochefort

   Centre for Integrative Physiology, School of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   NLR, Designed the experiments, Acquired the data, Analysed and interpreted the data, Wrote the manuscript with input from all authors

   For correspondence

   n.rochefort@ed.ac.uk

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-3498-6221

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