Maurice J. Chacron

Neural variability, detection thresholds, and information transmission in the vestibular system

A fundamental issue in neural coding is the role of spike timing variation in information transmission of sensory stimuli. Vestibular afferents are particularly well suited to study this issue because they are classified as either regular or irregular based on resting discharge variability as well as morphology. Here, we compared the responses of each afferent class to sinusoidal and random head rotations using both information theoretic and gain measures. Information theoretic measures demonstrated that regular afferents transmitted, on average, two times more information than irregular afferents, despite having significantly lower gains. Moreover, consistent with information theoretic measures, regular afferents had angular velocity detection thresholds that were 50% lower than those of irregular afferents (∼4 vs 8°/s). Finally, to quantify the information carried by spike times, we added spike-timing jitter to the spike trains of both regular and irregular afferents. Our results showed that this significantly reduced information transmitted by regular afferents whereas it had little effect on irregular afferents. Thus, information is carried in the spike times of regular but not irregular afferents. Using a simple leaky integrate and fire model with a dynamic threshold, we show that differential levels of intrinsic noise can explain differences in the resting discharge, the responses to sensory stimuli, as well as the information carried by action potential timings of each afferent class. Our experimental and modeling results provide new insights as to how neural variability influences the strategy used by two different classes of sensory neurons to encode behaviorally relevant stimuli.

Non-classical receptive field mediates switch in a sensory neuron's frequency tuning.

Animals have developed stereotyped communication calls to which specific sensory neurons are well tuned. These communication calls must be discriminated from environmental signals such as those produced by prey. Sensory systems might have evolved neural circuitry to encode both categories. In weakly electric fish, prey and communication signals differ in their spatial extent and frequency content. Here we show that stimuli of different spatial extents mimicking prey and communication signals cause a switch in the frequency tuning and spike-timing precision of electrosensory pyramidal neurons, resulting in the selective and optimal encoding of both stimulus categories. As in other sensory systems, pyramidal neurons respond only to stimuli located within a restricted region of space known as the classical receptive field (CRF). In some systems, stimulation outside the CRF but within a non-classical receptive field (nCRF) can modulate the neural response to CRF stimulation even though nCRF stimulation alone fails to elicit responses. We show that pyramidal neurons possess a nCRF and that it can modulate the response to CRF stimuli to induce this neurobiological switch in frequency tuning.

Parallel processing of sensory input by bursts and isolated spikes.

Burst firing is commonly observed in many sensory systems and is proposed to transmit information reliably. Although a number of biophysical burst mechanisms have been identified, the relationship between burst dynamics and information transfer is uncertain. Electrosensory pyramidal cells have a well defined backpropagation-dependent burst mechanism. We used in vivo, in vitro, and modeling approaches to investigate pyramidal cell responses to mimics of behaviorally relevant sensory input. We found that within a given spike train, bursts are biased toward low-frequency events while isolated spikes simultaneously code for the entire frequency range. We also demonstrated that burst dynamics are essential for optimal feature detection but are not required for stimulus estimation. We conclude that burst and spike dynamics can segregate a single spike train into two parallel and complementary streams of information transfer.

Inhibitory feedback required for network oscillatory responses to communication but not prey stimuli

Stimulus-induced oscillations occur in visual, olfactory and somatosensory systems. Several experimental and theoretical studies have shown how such oscillations can be generated by inhibitory connections between neurons. But the effects of realistic spatiotemporal sensory input on oscillatory network dynamics and the overall functional roles of such oscillations in sensory processing are poorly understood. Weakly electric fish must detect electric field modulations produced by both prey (spatially localized) and communication (spatially diffuse) signals. Here we show, through in vivo recordings, that sensory pyramidal neurons in these animals produce an oscillatory response to communication-like stimuli, but not to prey-like stimuli. On the basis of well-characterized circuitry, we construct a network model of pyramidal neurons that predicts that diffuse delayed inhibitory feedback is required to achieve oscillatory behaviour only in response to communication-like stimuli. This prediction is experimentally verified by reversible blockade of feedback inhibition that removes oscillatory behaviour in the presence of communication-like stimuli. Our results show that a sensory system can use inhibitory feedback as a mechanism to ‘toggle’ between oscillatory and non-oscillatory firing states, each associated with a naturalistic stimulus.

Electroreceptor neuron dynamics shape information transmission.

The gymnotiform weakly electric fish Apteronotus leptorhynchus can capture prey using electrosensory cues that are dominated by low temporal frequencies. However, conventional tuning curves predict poor electroreceptor afferent responses to low-frequency stimuli. We compared conventional tuning curves with information tuning curves and found that the latter predicted substantially improved responses to these behaviorally relevant stimuli. Analysis of receptor afferent baseline activity showed that negative correlations reduced low-frequency noise levels, thereby increasing information transmission. Multiunit recordings from receptor afferents showed that this increased information transmission could persist at the population level. Finally, we verified that this increased low-frequency information is preserved in the spike trains of central neurons that receive receptor afferent input. Our results demonstrate that conventional tuning curves can be misleading when certain noise reduction strategies are used by the nervous system.

Temporal processing across multiple topographic maps in the electrosensory system.

Multiple topographic representations of sensory space are common in the nervous system and presumably allow organisms to separately process particular features of incoming sensory stimuli that vary widely in their attributes. We compared the response properties of sensory neurons within three maps of the body surface that are arranged strictly in parallel to two classes of stimuli that mimic prey and conspecifics, respectively. We used information-theoretic approaches and measures of phase locking to quantify neuronal responses. Our results show that frequency tuning in one of the three maps does not depend on stimulus class. This map acts as a low-pass filter under both conditions. A previously described stimulus-class-dependent switch in frequency tuning is shown to occur in the other two maps. Only a fraction of the information encoded by all neurons could be recovered through a linear decoder. Particularly striking were low-pass neurons the information of which in the high-frequency range could not be decoded linearly. We then explored whether intrinsic cellular mechanisms could partially account for the differences in frequency tuning across maps. Injection of a Ca2+ chelator had no effect in the map with low-pass characteristics. However, injection of the same Ca2+ chelator in the other two maps switched the tuning of neurons from band-pass/high-pass to low-pass. These results show that Ca2+-dependent processes play an important part in determining the functional roles of different sensory maps and thus shed light on the evolution of this important feature of the vertebrate brain.

Feedback and Feedforward Control of Frequency Tuning to Naturalistic Stimuli

Sensory neurons must respond to a wide variety of natural stimuli that can have very different spatiotemporal characteristics. Optimal responsiveness to subsets of these stimuli can be achieved by devoting specialized neural circuitry to different stimulus categories, or, alternatively, this circuitry can be modulated or tuned to optimize responsiveness to current stimulus conditions. This study explores the mechanisms that enable neurons within the initial processing station of the electrosensory system of weakly electric fish to shift their tuning properties based on the spatial extent of the stimulus. These neurons are tuned to low frequencies when the stimulus is restricted to a small region within the receptive field center but are tuned to higher frequencies when the stimulus impinges on large regions of the sensory epithelium. Through a combination of modeling and in vivo electrophysiology, we reveal the respective contributions of the filtering characteristics of extended dendritic structures and feedback circuitry to this shift in tuning. Our results show that low-frequency tuning can result from the cable properties of an extended dendrite that conveys receptor-afferent information to the cell body. The shift from low- to high-frequency tuning, seen in response to spatially extensive stimuli, results from increased wide-band input attributable to activation of larger populations of receptor afferents, as well as the activation of parallel fiber feedback from the cerebellum. This feedback provides a cancellation signal with low-pass characteristics that selectively attenuates low-frequency responsiveness. Thus, with spatially extensive stimuli, these cells preferentially respond to the higher-frequency components of the receptor-afferent input.

Interspike interval correlations, memory, adaptation, and refractoriness in a leaky integrate-and-fire model with threshold fatigue

Neuronal adaptation as well as interdischarge interval correlations have been shown to be functionally important properties of physiological neurons. We explore the dynamics of a modified leaky integrate-and-fire (LIF) neuron, referred to as the LIF with threshold fatigue, and show that it reproduces these properties. In this model, the postdischarge threshold reset depends on the preceding sequence of discharge times. We show that in response to various classes of stimuli, namely, constant currents, step currents, white gaussian noise, and sinusoidal currents, the model exhibits new behavior compared with the standard LIF neuron. More precisely, (1) step currents lead to adaptation, that is, a progressive decrease of the discharge rate following the stimulus onset, while in the standard LIF, no such patterns are possible; (2) a saturation in the firing rate occurs in certain regimes, a behavior not seen in the LIF neuron; (3) interspike intervals of the noise-driven modified LIF under constant current are correlated in a way reminiscent of experimental observations, while those of the standard LIF are independent of one another; (4) the magnitude of the correlation coefficients decreases as a function of noise intensity; and (5) the dynamics of the sinusoidally forced modified LIF are described by iterates of an annulus map, an extension to the circle map dynamics displayed by the LIF model. Under certain conditions, this map can give rise to sensitivity to initial conditions and thus chaotic behavior.

Population Coding by Electrosensory Neurons

Sensory stimuli typically activate many receptors at once and therefore should lead to increases in correlated activity among central neurons. Such correlated activity could be a critical feature in the encoding and decoding of information in central circuits. Here we characterize correlated activity in response to two biologically relevant classes of sensory stimuli in the primary electrosensory nuclei, the electrosensory lateral line lobe, of the weakly electric fish Apteronotus leptorhynchus. Our results show that these neurons can display significant correlations in their baseline activities that depend on the amount of receptive field overlap. A detailed analysis of spike trains revealed that correlated activity resulted predominantly from a tendency to fire synchronous or anti-synchronous bursts of spikes. We also explored how different stimulation protocols affected correlated activity: while prey-like stimuli increased correlated activity, conspecific-like stimuli decreased correlated activity. We also computed the correlations between the variabilities of each neuron to repeated presentations of the same stimulus (noise correlations) and found lower amounts of noise correlation for communication stimuli. Therefore the decrease in correlated activity seen with communication stimuli is caused at least in part by reduced noise correlations. This differential modulation in correlated activity occurred because of changes in burst firing at the individual neuron level. Our results show that different categories of behaviorally relevant input will differentially affect correlated activity. In particular, we show that the number of correlated bursts within a given time window could be used by postsynaptic neurons to distinguish between both stimulus categories.

Efficient computation via sparse coding in electrosensory neural networks.

The electric sense combines spatial aspects of vision and touch with temporal features of audition. Its accessible neural architecture shares similarities with mammalian sensory systems and allows for recordings from successive brain areas to test hypotheses about neural coding. Further, electrosensory stimuli encountered during prey capture, navigation, and communication, can be readily synthesized in the laboratory. These features enable analyses of the neural circuitry that reveal general principles of encoding and decoding, such as segregation of information into separate streams and neural response sparsification. A systems level understanding arises via linkage between cellular differentiation and network architecture, revealed by in vitro and in vivo analyses, while computational modeling reveals how single cell dynamics and connectivity shape the sparsification process.