Neural Encoding of Odors during Active Sampling and in Turbulent Plumes

Sensory inputs are often fluctuating and intermittent, yet animals reliably utilize them to direct behavior. Here we ask how natural stimulus fluctuations influence the dynamic neural encoding of odors. Using the locust olfactory system, we isolated two main causes of odor intermittency: chaotic odor plumes and active sampling behaviors. Despite their irregularity, chaotic odor plumes still drove dynamic neural response features including the synchronization, temporal patterning, and short-term plasticity of spiking in projection neurons, enabling classifier-based stimulus identification and activating downstream decoders (Kenyon cells). Locusts can also impose odor intermittency through active sampling movements with their unrestrained antennae. Odors triggered immediate, spatially targeted antennal scanning that, paradoxically, weakened individual neural responses. However, these frequent but weaker responses were highly informative about stimulus location. Thus, not only are odor-elicited dynamic neural responses compatible with natural stimulus fluctuations and important for stimulus identification, but locusts actively increase intermittency, possibly to improve stimulus localization

Information Transmission in Cercal Giant Interneurons Is Unaffected by Axonal Conduction Noise

What are the fundamental constraints on the precision and accuracy with which nervous systems can process information? One constraint must reflect the intrinsic “noisiness” of the mechanisms that transmit information between nerve cells. Most neurons transmit information through the probabilistic generation and propagation of spikes along axons, and recent modeling studies suggest that noise from spike propagation might pose a significant constraint on the rate at which information could be transmitted between neurons. However, the magnitude and functional significance of this noise source in actual cells remains poorly understood. We measured variability in conduction time along the axons of identified neurons in the cercal sensory system of the cricket Acheta domesticus, and used information theory to calculate the effects of this variability on sensory coding. We found that the variability in spike propagation speed is not large enough to constrain the accuracy of neural encoding in this system

Model of change in information rates as a function of transmission jitter and expansive non-linearity.

<p>A surface depicting the percentage change in mutual information between encoding and decoding sites is shown in greyscale for model parametrized by the magnitude of the transmission jitter (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030115#pone-0030115-g001" target="_blank">Fig. 1C</a>) and the time constant of the expansive nonlinearity (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030115#pone-0030115-g002" target="_blank">Fig. 2A</a>). The parameter combinations measured in 8 neurons are shown with grey x's.</p

Change in ISI Distribution and Relation to Stimulus Coding.

<p><i>A</i>, Percentage change in probability of ISI at decoding site relative to encoding site, same data as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030115#pone-0030115-g002" target="_blank">figure 2D</a>. The shaded region indicates ISIs that occur more frequently at the decoding site than at the encoding site. <i>B</i>, The correlation between first and second spikes of ISIs reliably elicited by repeated presentations of identical stimuli (“frozen noise”). <i>C</i>, The linearity of stimuli associated with doublet patterns of spikes with various ISIs, as assessed with log likelihood ratios. Data in <i>B</i> & <i>C</i> are from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030115#pone.0030115-Aldworth2" target="_blank">[28]</a>, reprinted with permission.</p

Conduction failures.

<p><i>A</i>, Simultaneous intracellular recording from encoding (lower trace) and decoding (upper trace) sites in a single 10-2a neuron, showing an instance of an action potential which failed to propagate the length of the axon (red arrows). <i>B</i>, Distribution of spike propagation time as a function of ISI, along with exponential fit (dashed red line), as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030115#pone-0030115-g002" target="_blank">figure 2</a>. Also shown is the length of the preceding ISI for 32 action potentials that failed to propagate (blue circles, arbitrary ordinate position). <i>C</i>, Data are presented as in <i>B</i>, but for a different cell (class 10-3a). Scale bars: <i>A</i>, horizontal, 20 ms; vertical, 5 mV.</p

Measurement of jitter and information rates.

<p><i>A</i>, Comparison of jitter at encoding end (light grey portion of bar) assessed over repeated presentations of stimulus, and transmission jitter (black portion of bar), measured in three different neurons. <i>B</i>, Mutual information rates for the three neurons in <i>A</i>, calculated at the encoding (light grey) and decoding (black) sites. Error bars represent Bayesian 95% confidence interval from CTW calculation.</p

Transmission jitter and fit parameters for all 8 intra-intra and intra-extra experiments.

<p>Transmission jitter and fit parameters for all 8 intra-intra and intra-extra experiments.</p

Temporal encoding in a nervous system

We examined the extent to which temporal encoding may be implemented by single neurons in the cercal sensory system of the house cricket Acheta domesticus. We found that these neurons exhibit a greater-than-expected coding capacity, due in part to an increased precision in brief patterns of action potentials. We developed linear and non-linear models for decoding the activity of these neurons. We found that the stimuli associated with short-interval patterns of spikes (ISIs of 8 ms or less) could be predicted better by second-order models as compared to linear models. Finally, we characterized the difference between these linear and second-order models in a low-dimensional subspace, and showed that modification of the linear models along only a few dimensions improved their predictive power to parity with the second order models. Together these results show that single neurons are capable of using temporal patterns of spikes as fundamental symbols in their neural code, and that they communicate specific stimulus distributions to subsequent neural structures

Dejittered spike-conditioned stimulus waveforms yield improved estimates of neuronal feature selectivity and spike-timing precision of sensory interneurons

What is the meaning associated with a single action potential in a neural spike train? The answer depends on the way the question is formulated. One general approach toward formulating this question involves estimating the average stimulus waveform preceding spikes in a spike train. Many different algorithms have been used to obtain such estimates, ranging from spike-triggered averaging of stimuli to correlation-based extraction of "stimulus-reconstruction" kernels or spatiotemporal receptive fields. We demonstrate that all of these approaches miscalculate the stimulus feature selectivity of a neuron. Their errors arise from the manner in which the stimulus waveforms are aligned to one another during the calculations. Specifically, the waveform segments are locked to the precise time of spike occurrence, ignoring the intrinsic "jitter" in the stimulus-to-spike latency. We present an algorithm that takes this jitter into account. "Dejittered" estimates of the feature selectivity of a neuron are more accurate (i.e., provide a better estimate of the mean waveform eliciting a spike) and more precise (i.e., have smaller variance around that waveform) than estimates obtained using standard techniques. Moreover, this approach yields an explicit measure of spike-timing precision. We applied this technique to study feature selectivity and spike-timing precision in two types of sensory interneurons in the cricket cercal system. The dejittered estimates of the mean stimulus waveforms preceding spikes were up to three times larger than estimates based on the standard techniques used in previous studies and had power that extended into higher-frequency ranges. Spike timing precision was approximately 5 ms