Development of the sleep-wake switch in rats during the P2-P21 early infancy period.

In early infancy, rats randomly alternate between the sleeping and waking states-from postnatal day 2-10 (P2-P10), sleep and wake bouts are both exponentially distributed with increasing means, while from P10-P21 sleep and wake bout means continue to increase, though there is a striking qualitative shift in the distribution of wake bouts from exponential to power law. The behavioral states of sleep and wakefulness correspond to the activity of sleep-active and wake-active neuronal brainstem populations, with reciprocal inhibition between the two ensuring that only one population is active at a time. The locus coeruleus (LC) forms a third component of this circuit that rises in prominence during the P10-P21 period, as experimental evidence shows that an as-of-yet undeciphered interaction of the LC with sleep-active and wake-active populations is responsible for the transformation of the wake bout distribution from exponential to power law. Interestingly, the LC undergoes remarkable physiological changes during the P10-P21 period-gap junctions within the LC are pruned and network-wide oscillatory synchrony declines and vanishes. In this work, we discuss a series of models of sleep-active, wake-active, and the LC populations, and we use these models to postulate the nature of the interaction between these three populations and how these interactions explain empirical observations of sleep and wake bout dynamics. We hypothesize a circuit in which there is reciprocal excitation between the LC and wake-active population with inhibition from the sleep-active population to the LC that suppresses the LC during sleep bouts. During the P2-P10 period, we argue that a noise-based switching mechanism between the sleep-active and wake-active populations provides a simple and natural way to account for exponential bout distributions, and that the locked oscillatory state of the LC prevents it from impacting bout distributions. From P10-P21, we use our models to postulate that, as the LC gradually shifts from a state of synchronized oscillations to a state of continuous firing, reciprocal excitation between the LC and the wake-active population is able to gradually transform the wake bout distribution from exponential to power law.

Experimental and theoretical probe on mechano- and chemosensory integration in the insect antennal lobe.

In nature, olfactory signals are delivered to detectors-for example, insect antennae-by means of turbulent air, which exerts concurrent chemical and mechanical stimulation on the detectors. The antennal lobe, which is traditionally viewed as a chemosensory module, sits downstream of antennal inputs. We review experimental evidence showing that, in addition to being a chemosensory structure, antennal lobe neurons also respond to mechanosensory input in the form of wind speed. Benchmarked with empirical data, we constructed a dynamical model to simulate bimodal integration in the antennal lobe, with model dynamics yielding insights such as a positive correlation between the strength of mechanical input and the capacity to follow high frequency odor pulses, an important task in tracking odor sources. Furthermore, we combine experimental and theoretical results to develop a conceptual framework for viewing the functional significance of sensory integration within the antennal lobe. We formulate the testable hypothesis that the antennal lobe alternates between two distinct dynamical regimes, one which benefits odor plume tracking and one which promotes odor discrimination. We postulate that the strength of mechanical input, which correlates with behavioral contexts such being mid-flight versus hovering near a flower, triggers the transition from one regime to the other.

Mechanism of carbachol-induced 40 Hz gamma oscillations and the effects of NMDA activation on oscillatory dynamics in a model of the CA3 subfield of the hippocampus.

Gamma oscillations are a prominent feature of various neural systems, including the CA3 subfield of the hippocampus. In CA3, in vitro carbachol application induces ∼40 Hz gamma oscillations in the network of glutamatergic excitatory pyramidal neurons (PNs) and local GABAergic inhibitory neurons (INs). Activation of NMDA receptors within CA3 leads to an increase in the frequency of carbachol-induced oscillations to ∼60 Hz, a broadening of the distribution of individual oscillation cycle frequencies, and a decrease in the time lag between PN and IN spike bursts. In this work, we develop a biophysical integrate-and-fire model of the CA3 subfield, we show that the dynamics of our model are in concordance with physiological observations, and we provide computational support for the hypothesis that the 'E-I' mechanism is responsible for the emergence of ∼40 Hz gamma oscillations in the absence of NMDA activation. We then incorporate NMDA receptors into our CA3 model, and we show that our model exhibits the increase in gamma oscillation frequency, broadening of the cycle frequency distribution, and decrease in the time lag between PN and IN spike bursts observed experimentally. Remarkably, we find an inverse relationship in our model between the net NMDA current delivered to PNs and INs in an oscillation cycle and cycle frequency. Furthermore, we find a disparate effect of NMDA receptors on PNs versus INs - we show that NMDA receptors on INs tend to increase oscillation frequency, while NMDA receptors on PNs tend to slightly decrease or not affect oscillation frequency. We find that these observations can be explained if NMDA activity above a threshold level causes a shift in the mechanism underlying gamma oscillations; in the absence of NMDA receptors, the 'E-I' mechanism is primarily responsible for the generation of gamma oscillations (at 40 Hz), while when NMDA receptors are active, the mechanism of gamma oscillations shifts to the 'I-I' mechanism, and we argue that within the 'I-I' regime (which displays a higher baseline oscillation frequency of ∼60 Hz), slight changes in the level of NMDA activity are inversely related to cycle frequency.

A model of lateral interactions as the origin of multiwhisker receptive fields in rat barrel cortex.

While cells within barrel cortex respond primarily to deflections of their principal whisker (PW), they also exhibit responses to non-principal, or adjacent, whiskers (AWs), albeit responses with diminished amplitudes and longer latencies. The origin of multiwhisker receptive fields of barrel cells remains a point of controversy within the experimental literature, with three contending possibilities: (i) barrel cells inherit their AW responses from the AW responses of thalamocortical (TC) cells within their aligned barreloid; (ii) the axons of TC cells within a barreloid ramify to innervate multiple barrels, rather than only terminating within their aligned barrel; (iii) lateral intracortical transmission between barrels conveys AW responsivity to barrel cells. In this work, we develop a detailed, biologically plausible model of multiple barrels in order to examine possibility (iii); in order to isolate the dynamics that possibility (iii) entails, we incorporate lateral connections between barrels while assuming that TC cells respond only to their PW and that TC cell axons are confined to their home barrel. We show that our model is capable of capturing a broad swath of experimental observations on multiwhisker receptive field dynamics within barrels, and we compare and contrast the dynamics of this model with model dynamics from prior work in which employ a similar general modeling strategy to examine possibility (i).

Effects of Mechanosensory Input on the Tracking of Pulsatile Odor Stimuli by Moth Antennal Lobe Neurons.

Air turbulence ensures that in a natural environment insects tend to encounter odor stimuli in a pulsatile fashion. The frequency and duration of odor pulses varies with distance from the source, and hence successful mid-flight odor tracking requires resolution of spatiotemporal pulse dynamics. This requires both olfactory and mechanosensory input (from wind speed), a form of sensory integration observed within the antennal lobe (AL). In this work, we employ a model of the moth AL to study the effect of mechanosensory input on AL responses to pulsatile stimuli; in particular, we examine the ability of model neurons to: (1) encode the temporal length of a stimulus pulse; (2) resolve the temporal dynamics of a high frequency train of brief stimulus pulses. We find that AL glomeruli receiving olfactory input are adept at encoding the temporal length of a stimulus pulse but less effective at tracking the temporal dynamics of a pulse train, while glomeruli receiving mechanosensory input but little olfactory input can efficiently track the temporal dynamics of high frequency pulse delivery but poorly encode the duration of an individual pulse. Furthermore, we show that stronger intrinsic small-conductance calcium-dependent potassium (SK) currents tend to skew cells toward being better trackers of pulse frequency, while weaker SK currents tend to entail better encoding of the temporal length of individual pulses. We speculate a possible functional division of labor within the AL, wherein, for a particular odor, glomeruli receiving strong olfactory input exhibit prolonged spiking responses that facilitate detailed discrimination of odor features, while glomeruli receiving mechanosensory input (but little olfactory input) serve to resolve the temporal dynamics of brief, pulsatile odor encounters. Finally, we discuss how this hypothesis extends to explaining the functional significance of intraglomerular variability in observed phase II response patterns of AL neurons.

Olfactory encoding within the insect antennal lobe: The emergence and role of higher order temporal correlations in the dynamics of antennal lobe spiking activity.

In this review, we focus on the antennal lobe (AL) of three insect species - the fruit fly, sphinx moth, and locust. We first review the experimentally elucidated anatomy and physiology of the early olfactory system of each species; empirical studies of AL activity, however, often focus on assessing firing rates (averaged over time scales of about 100 ms), and hence the AL odor code is often analyzed in terms of a temporally evolving vector of firing rates. However, such a perspective necessarily misses the possibility of higher order temporal correlations in spiking activity within a single cell and across multiple cells over shorter time scales (of about 10 ms). Hence, we then review our prior theoretical work, where we constructed biophysically detailed, species-specific AL models within the fly, moth, and locust, finding that in each case higher order temporal correlations in spiking naturally emerge from model dynamics (i.e., without a prioriincorporation of elements designed to produce correlated activity). We therefore use our theoretical work to argue the perspective that temporal correlations in spiking over short time scales, which have received little experimental attention to-date, may provide valuable coding dimensions (complementing the coding dimensions provided by the vector of firing rates) that nature has exploited in the encoding of odors within the AL. We further argue that, if the AL does indeed utilize temporally correlated activity to represent odor information, such an odor code could be naturally and easily deciphered within the Mushroom Body.

Dynamics of sensory integration of olfactory and mechanical stimuli within the response patterns of moth antennal lobe neurons.

Odors emanating from a biologically relevant source are rapidly embedded within a windy, turbuluent medium that folds and spins filaments into fragmented strands of varying sizes. Environmental odor plumes therefore exhibit complex spatiotemporal dynamics, and rarely yield an easily discernible concentration gradient marking an unambiguous trail to an odor source. Thus, sensory integration of chemical input, encoding odor identity or concentration, and mechanosensory input, encoding wind speed, is a critical task that animals face in resolving the complex dynamics of odor plumes and tracking an odor source. In insects, who employ olfactory navigation as their primary means of foraging for food and finding mates, the antennal lobe (AL) is the first brain structure that processes sensory odor information. Although the importance of chemosensory and mechanosensory integration is widely recognized, the AL itself has traditionally been viewed purely from the perspective of odor encoding, with little attention given to its role as a bimodal integrator. In this work, we seek to explore the AL as a model for studying sensory integration - it boasts well-understood architecture, well-studied olfactory responses, and easily measurable cells. Using a moth model, we present experimental data that clearly demonstrates that AL neurons respond, in dynamically distinct ways, to both chemosensory and mechanosensory input; mechanosensory responses are transient and temporally precise, while olfactory responses are long-lasting but lack temporal precision. We further develop a computational model of the AL, show that our model captures odor response dynamics reported in the literature, and examine the dynamics of our model with the inclusion of mechanosensory input; we then use our model to pinpoint dynamical mechanisms underlying the bimodal AL responses revealed in our experimental work. Finally, we propose a novel hypothesis about the role of mechanosensory input in sculpting AL dynamics and the implications for biological odor tracking.

Analysis of feedforward mechanisms of multiwhisker receptive field generation in a model of the rat barrel cortex.

There is substantial anatomical segregation in the organization of the rodent barrel system - each whisker on the mystacial pad sends input to TC cells within a dedicated thalamic barreloid, which in turn innervates a corresponding cortical barrel, and RS cells within a barrel respond primarily to deflections of the corresponding whisker at the beginning of the dedicated transmission line (the principal whisker, PW). However, it is also well-established that barrel cells exhibit multiwhisker receptive fields (RFs), and display lower amplitude, longer latency responses to deflections of non-PWs (or adjacent whiskers, AWs). There is considerable controversy regarding the origin of such multiwhisker RFs; three possibilities include: (i) TC cells within a barreloid respond to multiple whiskers, and barrel RS cells simply inherit multiwhisker responses from their aligned barreloid; (ii) TC cells respond only to the PW, but individual barreloids innervate multiple barrels; (iii) multiwhisker responses of barrel cells arise from lateral corticocortical (barrel-to-barrel) synaptic transmission. Ablation studies attempting to pinpoint the source of RS cell AW responses are often contradictory (though experimental work tends to suggest possibilities (i) or (iii) to be most plausible), and hence it is important to carefully evaluate these hypotheses in terms of available physiological data on barreloid and barrel response dynamics. In this work, I employ a biologically detailed model of the rat barrel cortex to evaluate possibility (i), and I show that, within the model, hypothesis (i) is capable of explaining a broad range of the available physiological data on responses to single (PW or AW) deflections and paired whisker deflections (AW deflection followed by PW deflection), as well as the dependence of such responses on the angular direction of whisker deflection. In particular, the model shows that barrel RS cells can exhibit AW direction tuning despite the fact that barreloid to barrel wiring has no systematic dependence on the AW direction preference of TC cells. Future modeling work will examine the other possibilities for the generation of multiwhisker RS cell RFs, and compare and contrast the different possible mechanisms within the context of available experimental data.

Effects of Adaptation on Discrimination of Whisker Deflection Velocity and Angular Direction in a Model of the Barrel Cortex.

Two important stimulus features represented within the rodent barrel cortex are velocity and angular direction of whisker deflection. Each cortical barrel receives information from thalamocortical (TC) cells that relay information from a single whisker, and TC input is decoded by barrel regular-spiking (RS) cells through a feedforward inhibitory architecture (with inhibition delivered by cortical fast-spiking or FS cells). TC cells encode deflection velocity through population synchrony, while deflection direction is encoded through the distribution of spike counts across the TC population. Barrel RS cells encode both deflection direction and velocity with spike rate, and are divided into functional domains by direction preference. Following repetitive whisker stimulation, system adaptation causes a weakening of synaptic inputs to RS cells and diminishes RS cell spike responses, though evidence suggests that stimulus discrimination may improve following adaptation. In this work, I construct a model of the TC, FS, and RS cells comprising a single barrel system-the model incorporates realistic synaptic connectivity and dynamics and simulates both angular direction (through the spatial pattern of TC activation) and velocity (through synchrony of the TC population spikes) of a deflection of the primary whisker, and I use the model to examine direction and velocity selectivity of barrel RS cells before and after adaptation. I find that velocity and direction selectivity of individual RS cells (measured over multiple trials) sharpens following adaptation, but stimulus discrimination using a simple linear classifier by the RS population response during a single trial (a more biologically meaningful measure than single cell discrimination over multiple trials) exhibits strikingly different behavior-velocity discrimination is similar both before and after adaptation, while direction classification improves substantially following adaptation. This is the first model, to my knowledge, that simulates both whisker deflection velocity and angular direction and examines the ability of the RS population response to pinpoint both stimulus features within the context of adaptation.

Deterministic stability regimes and noise-induced quasistable behavior in a pair of reciprocally inhibitory neurons.

Reciprocal inhibition is a common motif exploited by neuronal networks; an intuitive and tractable way to examine the behaviors produced by reciprocal inhibition is to consider a pair of neurons that synaptically inhibit each other and receive constant or noisy excitatory driving currents. In this work, we examine reciprocal inhibition using two models (a voltage-based and a current-based integrate-and-fire model with instantaneous or temporally structured input), and we use analytic and computational tools to examine the bifurcations that occur and study the various possible monostable, bistable, and tristable regimes that can exist; we find that, depending on system parameters (and on choice of neuron model), there can exist up to 3 distinct monostable regimes (denoted M0, M1, M2), 3 distinct bistable regimes (denoted B, B1, B2), and a single tristable regime (denoted T). We also find that synaptic inhibition exerts independent control over the two neurons - inhibition from neuron 1 to neuron 2 governs the spiking behavior of neuron 2 but has no impact on the spiking behavior of neuron 1 (and vice versa). The excitatory driving current, however, does not exhibit this property - the excitatory current to neuron 1 affects the spiking behavior of both neurons 1 and 2 (as does the excitatory current to neuron 2). Furthermore, we develop a methodology to examine the behavior of the system when the excitatory driving currents are allowed to be noisy, and we investigate the relationship between the behavior of the noisy system with the stability regime of the corresponding deterministic system.

Spiking and Excitatory/Inhibitory Input Dynamics of Barrel Cells in Response to Whisker Deflections of Varying Velocity and Angular Direction.

The spiking of barrel regular-spiking (RS) cells is tuned for both whisker deflection direction and velocity. Velocity tuning arises due to thalamocortical (TC) synchrony (but not spike quantity) varying with deflection velocity, coupled with feedforward inhibition, while direction selectivity is not fully understood, though may be due partly to direction tuning of TC spiking. Data show that as deflection direction deviates from the preferred direction of an RS cell, excitatory input to the RS cell diminishes minimally, but temporally shifts to coincide with the time-lagged inhibitory input. This work constructs a realistic large-scale model of a barrel; model RS cells exhibit velocity and direction selectivity due to TC input dynamics, with the experimentally observed sharpening of direction tuning with decreasing velocity. The model puts forth the novel proposal that RS→RS synapses can naturally and simply account for the unexplained direction dependence of RS cell inputs - as deflection direction deviates from the preferred direction of an RS cell, and TC input declines, RS→RS synaptic transmission buffers the decline in total excitatory input and causes a shift in timing of the excitatory input peak from the peak in TC input to the delayed peak in RS input. The model also provides several experimentally testable predictions on the velocity dependence of RS cell inputs. This model is the first, to my knowledge, to study the interaction of direction and velocity and propose physiological mechanisms for the stimulus dependence in the timing and amplitude of RS cell inputs.

Role of the locus coeruleus in the emergence of power law wake bouts in a model of the brainstem sleep-wake system through early infancy.

Infant rats randomly cycle between the sleeping and waking states, which are tightly correlated with the activity of mutually inhibitory brainstem sleep and wake populations. Bouts of sleep and wakefulness are random; from P2-P10, sleep and wake bout lengths are exponentially distributed with increasing means, while during P10-P21, the sleep bout distribution remains exponential while the distribution of wake bouts gradually transforms to power law. The locus coeruleus (LC), via an undeciphered interaction with sleep and wake populations, has been shown experimentally to be responsible for the exponential to power law transition. Concurrently during P10-P21, the LC undergoes striking physiological changes - the LC exhibits strong global 0.3 Hz oscillations up to P10, but the oscillation frequency gradually rises and synchrony diminishes from P10-P21, with oscillations and synchrony vanishing at P21 and beyond. In this work, we construct a biologically plausible Wilson Cowan-style model consisting of the LC along with sleep and wake populations. We show that external noise and strong reciprocal inhibition can lead to switching between sleep and wake populations and exponentially distributed sleep and wake bout durations as during P2-P10, with the parameters of inhibition between the sleep and wake populations controlling mean bout lengths. Furthermore, we show that the changing physiology of the LC from P10-P21, coupled with reciprocal excitation between the LC and wake population, can explain the shift from exponential to power law of the wake bout distribution. To our knowledge, this is the first study that proposes a plausible biological mechanism, which incorporates the known changing physiology of the LC, for tying the developing sleep-wake circuit and its interaction with the LC to the transformation of sleep and wake bout dynamics from P2-P21.

A Simplified model of mutually inhibitory sleep-active and wake-active neuronal populations employing a noise-based switching mechanism.

Infant rats switch randomly between the sleeping and waking states; during early infancy (up to postnatal day 8), sleep and wake bouts are random, brief (with means on the order of several seconds) and exponentially distributed, with the length of a particular bout independent of the length of prior bouts. As the rat ages during this early period, mean sleep and wake bout lengths gradually increase, though sleep and wake bouts remain exponentially distributed. Additionally, sleep and wake bouts are regulated independently of each other - alterations in the development of sleep (wake) bouts has no impact on the regulation wake (sleep) bouts. Sleep and wake bout behavior is associated with the activity of mutually inhibitory sleep-active and wake-active brainstem populations. In this work, I employ a simplified biophysical model of two mutually inhibitory populations consisting of ten integrate-and-fire neurons each and a noise-based switching mechanism. I show that such a noise-based switching mechanism naturally accounts for the experimentally observed features of sleep-wake switching during early infancy - random alternating activity bouts occur as a consequence of noise (provided inhibition is strong relative to excitation), bout durations are exponential (due to a lack of memory within the system), and cross-population inhibition or intrapopulation excitatory coupling provide mechanisms for changing and independently regulated sleep and wake bout means.

Modeling the evolving oscillatory dynamics of the rat locus coeruleus through early infancy.

The mammalian locus coeruleus (LC) is a brainstem structure that displays extensive interconnections with numerous brain regions, and in particular plays a prominent role in the regulation of sleep and arousal. Postnatal LC development is known to drastically alter sleep-wake switching behavior through early infancy, and, in rats, exerts its most significant influence from about postnatal day 8 to postnatal day 21 (P8-P21). Physiologically, several dramatic changes are seen in LC functionality through this time period. Prior to P8, LC neurons are extensively coupled via electrical gap junctions and chemical synapses, and the entire LC network exhibits synchronized ~0.3 Hz subthreshold oscillations and spiking. From P8 to P21, the network oscillation frequency rises up to ~3 Hz (at P21) while the amplitude of the network oscillation decreases. Beyond P21, synchronized network oscillations vanish and gap junction coupling is sparse or nonexistent. In this work, we develop a large-scale, biophysically realistic model of the rat LC and we use this model to examine the changing physiology of the LC through the pivotal P8-P21 developmental period. We find that progressive gap junction pruning is sufficient to account for all of the physiological changes observed from P8 to P21.

Encoding whisker deflection velocity within the rodent barrel cortex using phase-delayed inhibition.

The primary sensory feature represented within the rodent barrel cortex is the velocity with which a whisker has been deflected. Whisker deflection velocity is encoded within the thalamus via population synchrony (higher deflection velocities entail greater synchrony among the corresponding thalamic population). Thalamic (TC) cells project to regular spiking (RS) cells within the barrel cortex, as well as to inhibitory cortical fast-spiking (FS) neurons, which in turn project to RS cells. Thus, TC spikes result in EPSPs followed, with a small time lag, by IPSPs within an RS cell, and hence the RS cell decodes TC population synchrony by employing a phase-delayed inhibition synchrony detection scheme. As whisker deflection velocity is increased, the probability that an RS cell spikes rises, while jitter in the timing of RS cell spikes remains constant. Furthermore, repeated whisker deflections with fixed velocity lead to system adaptation--TC →RS, TC →FS, and FS →RS synapses all weaken substantially, leading to a smaller probability of spiking of the RS cell and increased jitter in the timing of RS cell spikes. Interestingly, RS cell activity is better able to distinguish among different whisker deflection velocities after adaptation. In this work, we construct a biophysical model of a basic 'building block' of barrel cortex - the feedforward circuit consisting of TC cells, FS cells, and a single RS cell - and we examine the ability of the purely feedforward circuit to explain the experimental data on RS cell spiking probability, jitter, adaptation, and deflection velocity discrimination. Moreover, we study the contribution of the phase-delayed inhibition network structure to the ability of an RS cell to decode whisker deflection velocity encoded via TC population synchrony.

Switching mechanisms and bout times in a pair of reciprocally inhibitory neurons.

Within the appropriate parameter regime, a deterministic model of a pair of mutually inhibitory neurons receiving excitatory driving currents exhibits bistability-each of the two stable states corresponds to one neuron being active and the other being quiescent. The presence of noise in the driving currents results in a system that randomly switches back and forth between these two states, causing alternating bouts of spiking activity. In this work, we examine the random bout durations of the two neurons and dependence on system parameters. We find that bout durations of each neuron are exponentially distributed, with changes in system parameters altering only the mean of the distribution. Synaptic inhibition independently controls the bout durations of the two neurons-the mean bout time of a neuron is a function of efferent (or outgoing) inhibition, and is independent of afferent (or incoming) inhibition. Furthermore, we find that the mean bout time of a neuron exhibits a critical dependence on the time course (rather than amplitude) of efferent inhibition-mean bout time of a neuron grows exponentially with the time course of efferent inhibition, and the growth rate of this exponential function depends only on the excitatory driving current to that neuron (and not on any other system parameters). We discuss the relevance of our results to the regulation of sleep-wake cycling by medullary and pontine structures within the brain.

Decoding synchronized oscillations within the brain: phase-delayed inhibition provides a robust mechanism for creating a sharp synchrony filter.

The widespread presence of synchronized neuronal oscillations within the brain suggests that a mechanism must exist that is capable of decoding such activity. Two realistic designs for such a decoder include: (1) a read-out neuron with a high spike threshold, or (2) a phase-delayed inhibition network motif. Despite requiring a more elaborate network architecture, phase-delayed inhibition has been observed in multiple systems, suggesting that it may provide inherent advantages over simply imposing a high spike threshold. In this work, we use a computational and mathematical approach to investigate the efficacy of the phase-delayed inhibition motif in detecting synchronized oscillations. We show that phase-delayed inhibition is capable of creating a synchrony detector with sharp synchrony filtering properties that depend critically on the time course of inputs. Additionally, we show that phase-delayed inhibition creates a synchrony filter that is far more robust than that created by a high spike threshold.

Coding of odors by temporal binding within a model network of the locust antennal lobe.

The locust olfactory system interfaces with the external world through antennal receptor neurons (ORNs), which represent odors in a distributed, combinatorial manner. ORN axons bundle together to form the antennal nerve, which relays sensory information centrally to the antennal lobe (AL). Within the AL, an odor generates a dynamically evolving ensemble of active cells, leading to a stimulus-specific temporal progression of neuronal spiking. This experimental observation has led to the hypothesis that an odor is encoded within the AL by a dynamically evolving trajectory of projection neuron (PN) activity that can be decoded piecewise to ascertain odor identity. In order to study information coding within the locust AL, we developed a scaled-down model of the locust AL using Hodgkin-Huxley-type neurons and biologically realistic connectivity parameters and current components. Using our model, we examined correlations in the precise timing of spikes across multiple neurons, and our results suggest an alternative to the dynamic trajectory hypothesis. We propose that the dynamical interplay of fast and slow inhibition within the locust AL induces temporally stable correlations in the spiking activity of an odor-dependent neural subset, giving rise to a temporal binding code that allows rapid stimulus detection by downstream elements.

Encoding with synchrony: phase-delayed inhibition allows for reliable and specific stimulus detection.

Synchronized oscillations are observed in a diverse array of neuronal systems, suggesting that synchrony represents a common mechanism used by the brain to encode and relay information. Coherent population activity can be deciphered by a decoder neuron with a high spike threshold or by a decoder using phase-delayed inhibition. These two mechanisms are fundamentally different - a high spike threshold detects a minimum number of synchronous input spikes (absolute synchrony), while phase-delayed inhibition requires a fixed fraction of incoming spikes to be synchronous (relative synchrony). We show that, in a system with noisy encoders where stimuli are encoded through synchrony, phase-delayed inhibition enables the creation of a decoder that can respond both reliably and specifically to a stimulus, while a high spike threshold does not.

Stimulus encoding within the barn owl optic tectum using gamma oscillations vs. spike rate: a modeling approach.

The optic tectum of the barn owl is a multimodal structure with multiple layers, with each layer topographically organized according to spatial receptive field. The response of a site to a stimulus can be measured as either spike rate or local field potential (LFP) gamma (25-90 Hz) power; within superficial layers, spike rate and gamma power spatial tuning curves are narrow and contrast-response functions rise slowly. Within deeper layers, however, spike rate tuning curves broaden and gamma power contrast-response functions sharpen. In this work, we employ a computational model to describe the inputs required to generate these transformations from superficial to deep layers and show that gamma power and spike rate can act as parallel information processing streams.