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1.
Front Physiol ; 5: 140, 2014.
Article in English | MEDLINE | ID: mdl-24795640

ABSTRACT

Many animals use a diverse repertoire of complex acoustic signals to convey different types of information to other animals. The information in each vocalization therefore must be coded by neurons in the auditory system. One way in which the auditory system may discriminate among different vocalizations is by having highly selective neurons, where only one or two different vocalizations evoke a strong response from a single neuron. Another strategy is to have specific spike timing patterns for particular vocalizations such that each neural response can be matched to a specific vocalization. Both of these strategies seem to occur in the auditory midbrain of mice. The neural mechanisms underlying rate and time coding are unclear, however, it is likely that inhibition plays a role. Here, we examined whether inhibition is involved in shaping neural selectivity to vocalizations via rate and/or time coding in the mouse inferior colliculus (IC). We examined extracellular single unit responses to vocalizations before and after iontophoretically blocking GABAA and glycine receptors in the IC of awake mice. We then applied a number of neurometrics to examine the rate and timing information of individual neurons. We initially evaluated the neuronal responses using inspection of the raster plots, spike-counting measures of response rate and stimulus preference, and a measure of maximum available stimulus-response mutual information. Subsequently, we used two different event sequence distance measures, one based on vector space embedding, and one derived from the Victor/Purpura D q metric, to direct hierarchical clustering of responses. In general, we found that the most salient feature of pharmacologically blocking inhibitory receptors in the IC was the lack of major effects on the functional properties of IC neurons. Blocking inhibition did increase response rate to vocalizations, as expected. However, it did not significantly affect spike timing, or stimulus selectivity of the studied neurons. We observed two main effects when inhibition was locally blocked: (1) Highly selective neurons maintained their selectivity and the information about the stimuli did not change, but response rate increased slightly. (2) Neurons that responded to multiple vocalizations in the control condition, also responded to the same stimuli in the test condition, with similar timing and pattern, but with a greater number of spikes. For some neurons the information rate generally increased, but the information per spike decreased. In many of these neurons, vocalizations that generated no responses in the control condition generated some response in the test condition. Overall, we found that inhibition in the IC does not play a substantial role in creating the distinguishable and reliable neuronal temporal spike patterns in response to different vocalizations.

2.
PLoS Comput Biol ; 7(5): e1002041, 2011 May.
Article in English | MEDLINE | ID: mdl-21573206

ABSTRACT

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.


Subject(s)
Gryllidae/physiology , Models, Neurological , Neurons/physiology , Action Potentials/physiology , Animals , Computational Biology , Computer Simulation , Female , Information Theory , Linear Models , Nervous System Physiological Phenomena , Nonlinear Dynamics , Time Factors
3.
J Comput Neurosci ; 30(1): 163-79, 2011 Feb.
Article in English | MEDLINE | ID: mdl-20730481

ABSTRACT

We present an application of the information distortion approach to neural coding. The approach allows the discovery of neural symbols and the corresponding stimulus space of a neuron or neural ensemble simultaneously and quantitatively, making few assumptions about the nature of either code or relevant features. The neural codebook is derived by quantitizing sensory stimuli and neural responses into small reproduction sets, and optimizing the quantization to minimize the information distortion function. The application of this approach to the analysis of coding in sensory interneurons involved a further restriction of the space of allowed quantitizers to a smaller family of parametric distributions. We show that, for some cells in this system, a significant amount of information is encoded in patterns of spikes that would not be discovered through analyses based on linear stimulus-response measures.


Subject(s)
Information Theory , Models, Neurological , Sensory Receptor Cells/physiology , Algorithms , Animals , Gryllidae , Humans , Membrane Potentials/physiology , Physical Stimulation , Principal Component Analysis , Sense Organs/cytology , Sensory Receptor Cells/classification , Time Factors
4.
J Neurophysiol ; 103(4): 1823-32, 2010 Apr.
Article in English | MEDLINE | ID: mdl-20107118

ABSTRACT

The cercal sensory system of crickets mediates sensitivity to low-amplitude air currents. The sense organ for this system is a pair of antenna-like abdominal appendages called cerci, each of which is about 1 cm long in normal adult crickets. Although this system has been used extensively as a model system for studying the mechanisms underlying neural coding at the cellular and system levels, no previous studies have considered the functional significance of the physical dimensions of cerci. We show that the differential conduction characteristics of the receptor array in Acheta domesticus crickets are of substantial significance. All filiform sensory afferent axons we examined had the same propagation speeds to within a small variance, resulting in a significant and systematic differential propagation time for spikes from sensory receptors at different locations along the structure. Thus the sensory structures operate as delay lines. The delay-line structure supports neural computations in many of the projecting cercal interneurons (INs) that yield substantial differential sensitivity to the direction and velocity of naturalistic stimuli. Several INs show delay-line-derived sensitivities that are equivalent, in an engineering sense, to "notch filtering," through which background noise is selectively eliminated by the delay-line-based processing.


Subject(s)
Gryllidae/physiology , Animals , Auditory Pathways/physiology , Auditory Perception/physiology , Evoked Potentials, Auditory/physiology , Female , Interneurons/physiology , Neurons, Afferent/physiology , Reaction Time/physiology , Time Factors
5.
J Neurosci ; 28(18): 4592-603, 2008 Apr 30.
Article in English | MEDLINE | ID: mdl-18448635

ABSTRACT

While sensory information is encoded by firing patterns of individual sensory neurons, it is also represented by spatiotemporal patterns of activity in populations of the neurons. Postsynaptic interneurons decode the population response and extract specific sensory information. This extraction of information represented by presynaptic activities is a process critical to defining the input-output function of postsynaptic neuron. To understand the "algorithm" for the extraction, we examined directional sensitivities of presynaptic and postsynaptic Ca(2+) responses in dendrites of two types of wind-sensitive interneurons (INs) with different dendritic geometries in the cricket cercal sensory system. In IN 10-3, whose dendrites arborize with various electrotonic distances to the spike-initiating zone (SIZ), the directional sensitivity of dendritic Ca(2+) responses corresponded to those indicated by Ca(2+) signals in presynaptic afferents arborizing on that dendrite. The directional tuning properties of individual dendrites varied from each other, and the directional sensitivity of the nearest dendrite to the SIZ dominates the tuning properties of the spiking response. In IN 10-2 with dendrites isometric to the SIZ, directional tuning properties of different dendrites were similar to each other, and each response property could be explained by the directional profile of the spatial overlap between that dendrite and Ca(2+)-elevated presynaptic terminals. For IN 10-2, the directional sensitivities extracted by the different dendritic-branches would contribute equally to the overall tuning. It is possible that the differences in the distribution of synaptic weights because of the dendritic geometry are related to the algorithm for extraction of sensory information in the postsynaptic interneurons.


Subject(s)
Algorithms , Dendrites/physiology , Models, Neurological , Neurons, Afferent/cytology , Neurons, Afferent/physiology , Synapses/physiology , Animals , Calcium/metabolism , Calcium Signaling , Computer Simulation , Ganglia, Invertebrate , Gryllidae , In Vitro Techniques , Male , Membrane Potentials/physiology , Neural Conduction , Organic Chemicals/metabolism , Physical Stimulation/methods , Presynaptic Terminals/physiology , Presynaptic Terminals/radiation effects
6.
J Neurobiol ; 66(3): 293-307, 2006 Feb 15.
Article in English | MEDLINE | ID: mdl-16329129

ABSTRACT

The cercal sensory system of the cricket mediates the detection and analysis of low velocity air currents in the animal's immediate environment, and is implemented around an internal representation of air current direction that demonstrates the essential features of a continuous neural map. Previous neurophysiological and anatomical studies have yielded predictions of the global spatio-temporal patterns of activity that should be evoked in the sensory afferent map by air current stimuli of different directions. We tested those predictions by direct visualization of ensemble afferent activity patterns using Ca2+ -sensitive indicators. The AM ester of the fluorescent Ca2+ indicator (Oregon Green 488 BAPTA-1 AM) was injected under the sheath of a cercal sensory nerve containing all of the mechanosensory afferent axons from one cercus. Optical signals were recorded with a digital intensified CCD camera. Control experiments using direct electrical stimulation of stained and unstained nerves demonstrated that the observed Ca2+ signals within the terminal abdominal ganglion (TAG) were due to activation of the dye-loaded sensory afferent neurons. To visualize the spatial patterns of air-current-evoked ensemble activity, unidirectional air currents were applied repeatedly from eight different directions, and the optically recorded responses from each direction were averaged. The dispersion of the optical signals by the ganglion limited the spatial resolution with which these ensemble afferent activity patterns could be observed. However, resolution was adequate to demonstrate that different directional stimuli induced different spatial patterns of Ca2+ elevation in the terminal arbors of afferents within the TAG. These coarsely- resolved, optically-recorded patterns were consistent with the anatomy-based predictions.


Subject(s)
Diagnostic Imaging , Ganglia, Invertebrate/physiology , Gryllidae/physiology , Mechanoreceptors/physiology , Neurons, Afferent/physiology , Air Movements , Animals , Calcium , Electric Stimulation , Female , Fluorescent Dyes , Ganglia, Invertebrate/anatomy & histology , Ganglia, Sensory/anatomy & histology , Ganglia, Sensory/physiology , Image Processing, Computer-Assisted , Microinjections , Staining and Labeling
7.
J Neurosci ; 25(22): 5323-32, 2005 Jun 01.
Article in English | MEDLINE | ID: mdl-15930380

ABSTRACT

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.


Subject(s)
Action Potentials , Interneurons/physiology , Neurons, Afferent/physiology , Algorithms , Animals , Female , Ganglia, Invertebrate/physiology , Gryllidae , In Vitro Techniques , Information Theory , Physical Stimulation , Reaction Time
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