In earlier studies characterizing the temporal structure of sensory input to the OB, we found that ORN inputs are tightly coupled to inhalation, have intrinsic differences in their dynamics and are qualitatively altered by changes in sniff frequency (Spors et al., 2006
; Verhagen et al., 2007
; Carey et al., 2009
). These findings led to predictions about the relationship between sensory input dynamics and postsynaptic activity, and about how sniff frequency might affect MT responses. Here we tested those predictions by recording MT responses while manipulating sniffing in anesthetized rats, using sniff waveforms taken from awake rats. Several of our observations support earlier predictions: we found that inhalation-driven MT responses – like ORN inputs –show intrinsic differences in their response latency, rise-time, and duration that vary over a similar range, and that high-frequency sniffing attenuates the magnitude of the MT response. Other predictions – in particular, that temporal patterning of MT responses would diminish at high frequencies – were not supported. Instead most MT cells maintained strong temporal patterning of responses at all frequencies tested; this was accompanied by changes in the shape of sniff-evoked responses as sniff frequency increased.
High-frequency sniffing is a hallmark feature of active odor sampling, yet only a few studies have attempted to characterize how changes in sniff frequency affect olfactory processing. Recordings from awake, freely-moving rats have yielded mixed results on the effect of sniff frequency, with some reports that temporal patterning of OB neurons is diminished at high frequencies (Bhalla and Bower, 1997
; Kay and Laurent, 1999
) and one other reporting that patterning persists (Cury and Uchida, 2010
). Resolving these differences is difficult because frequency effects were not analyzed systematically and because of the potential confound of behavioral state-dependent modulation of responses (Bhalla and Bower, 1997
; Kay and Laurent, 1999
). A previous study using artificial sniffing in anesthetized rats found that most MT cells maintain respiratory patterning at moderate (3 Hz) and high (6 Hz) sniff frequencies, with a reduction in response magnitude (Bathellier et al., 2008
Our results extend these earlier reports by establishing that inhalation-driven responses change significantly with sniff frequency and that these changes are driven by a ‘bottom-up’ pathway related to active sampling rather than ‘top-down’, neuromodulatory pathways which can also shape MT spike timing in awake animals (Doucette et al., 2011
). For many cells – in particular those that maintained the strongest temporal patterning at high sniff frequencies – the duration and the rise-time of the inhalation-driven response shortened, leading to a temporal sharpening of activity. Thus, odorant-evoked response patterns of MT cells change with sampling behavior. This conclusion contrasts with a recent characterization of MT responses in behaving rats, which reported that firing patterns were frequency-invariant (Cury and Uchida, 2010
). That study, however, focused only on the first in a bout of high-frequency sniffs. Here, we found that the first sniff in any bout elicits equivalent responses regardless of the frequency of subsequent sniffs; we have previously reported the same result for ORN inputs (Verhagen et al., 2007
). Responses to later sniffs, however, do depend on recent sniff history.
One parameter of MT response dynamics that remained invariant to sniff frequency was onset latency relative to inhalation. This invariance is consistent with MT response timing depending on ORN inputs, which also show frequency-invariant latencies (Carey et al., 2009
). The fixed timing of MT responses relative to inhalation constrains models of temporal coding that rely on the phase of MT activity relative to the respiratory cycle (Chaput, 1986
; Hopfield, 1995
). These models have demonstrated robust phase-based coding of odor information by MT response onsets but have typically only considered cases of regular respiration rates. In behaving rodents, sniffs are rarely repeated at regular intervals and sniff frequency can vary dramatically from one sniff to the next (Welker, 1964
; Kepecs et al., 2007
; Wesson et al., 2008b
). One proposal for adapting phase-based coding schemes to the diverse and irregular sniffing patterns has been to translate absolute timing into phase-based timing using the period between two successive inhalations (Roux et al., 2006
; Fantana et al., 2008
). We found that translating MT responses onto a phase timebase eliminated the frequency-invariance in response latencies as well as response durations. These results – along with others arising from awake, behaving animals – suggest that information coding by MT responses appears to be organized in terms of absolute timing relative to inhalation rather than relative to a respiratory ‘cycle’ (Margrie and Schaefer, 2003
; Cury and Uchida, 2010
; Junek et al. 2010
At the population level, the net effect of frequency-dependent changes in MT responses was to enhance respiratory patterning. Responses evoked by 1-Hz sniffing were significantly less modulated when repeated at higher frequency than responses during 5-Hz sniffing. Furthermore, 5-Hz sniff-evoked responses were able to maintain significant patterning even when repeated at 10 Hz (e.g., ). Thus, the frequency-dependent temporal sharpening of MT spike bursts enables coherent population-level spiking dynamics to remain linked to each inhalation across the range of sniff frequencies expressed during behavior.
What changes in the inhalation-evoked MT response underlie their ability to maintain respiratory patterning across frequencies? The two parameters that changed most consistently were duration and rise-time: MT responses reached a peak more quickly and shut down more quickly at high sniff frequencies. MT response rise-times and durations were strongly correlated, with faster rise-times correlating with shorter durations. Several pieces of evidence suggest that these frequency-dependent changes are mediated by OB postsynaptic circuitry rather than frequency-dependent changes in sniff waveform or presynaptic activity patterns. First, MT responses to the first sniff in a bout did not change shape with frequency, despite the fact that sniff waveform did change slightly. Second, ORN inputs did not show substantial changes in shape as frequency increased (e.g., ), and presynaptic inhibition of ORN inputs does not change with sniff frequency (Pírez and Wachowiak, 2008
). Third, frequency-dependent effects on duration and rise-time were apparent only for a subset of MT cells – in particular, cells with relatively long (> 100 ms) response durations at low sniff frequencies.
One candidate circuit mediating these effects involves connections between external tufted (ET) cells, inhibitory periglomerular interneurons and MT cells. ET cells are excitatory interneurons receiving strong ORN input and directly exciting periglomerular interneurons within the same glomerulus (Hayar et al., 2004b
). ET cells show entrainment of spike bursts to rhythmic ORN inputs (Hayar et al., 2004a
). Thus, an increase in ET cell burst frequency as sniff frequency increases is predicted to cause stronger and more synchronous activation of periglomerular interneurons, leading in turn to a shortened MT cell response as sniff frequency increases and enhanced respiratory modulation at higher sniff frequencies (Wachowiak and Shipley, 2006
). The results observed here support these predictions. Alternatively, temporal sharpening might be mediated by recurrent inhibition between MT and granule cells. Recurrent inhibition can enhance the temporal precision of MT cell firing in the face of rhythmic ORN inputs (Balu et al., 2004
; Schoppa, 2006
); (David et al., 2007
), and there is evidence that recurrent inhibition is stronger at higher sniff frequencies (Young and Wilson, 1999
). In either case, ORN inputs that are more synchronous would lead to faster activation of MT cells and also to stronger recruitment of the feedforward or recurrent inhibitory circuits that shorten the MT cell response, consistent with the correlation we measured between response rise-time, duration, and the degree of frequency-dependent temporal patterning (e.g., and ). One set of predictions that follow from this model is that the rise-time of ORN inputs to a glomerulus should correlate with that of MT cells innervating the same glomerulus, and that glomeruli receiving faster ORN inputs should show stronger modulation of MT cell responses at high sniff frequencies. These predictions need to be tested by comparing ORN inputs to and MT cell responses for the same glomerulus.
Odorant sampling in behaving animals is a dynamic but precisely controlled behavior that presumably optimizes the detection and processing of sensory information. How might frequency-dependent attenuation and temporal sharpening of MT spike bursts optimize odor information processing? First, maintaining (and even enhancing) the coherence of MT spiking with sniffing has been shown to enhance the precision of MT spike timing, thus increasing the potential discriminatory power of MT spike trains (Hopfield, 1995
; Schaefer et al., 2006
). Second, temporally-patterned MT spiking appears to be important for information processing downstream of MT cells - for example, the temporal integration of inputs from multiple MT cells by pyramidal neurons in piriform cortex (Luna and Schoppa, 2008
; Stokes and Isaacson, 2010
). Third, frequency-dependent changes in MT response patterns may leave odor identity coding unchanged but enhance the ability of the population to process other aspects of odor information relevant to active sensing. For example, the reduction in MT response magnitude may reduce the salience of odorants that are constant across a high-frequency sniff bout while maintaining sensitivity to newly-encountered odorants (Verhagen et al., 2007
). Finally, high-frequency, temporally-patterned MT bursts may be important in driving synaptic plasticity within the OB or its targets during odor learning. Further electrophysiological experiments combined with behavioral analyses will be important to test these possibilities.