Coherent olfactory bulb gamma oscillations arise from coupling independent columnar oscillators.

Spike timing-based representations of sensory information depend on embedded dynamical frameworks within neuronal networks that establish the rules of local computation and interareal communication. Here, we investigated the dynamical properties of olfactory bulb circuitry in mice of both sexes using microelectrode array recordings from slice and in vivo preparations. Neurochemical activation or optogenetic stimulation of sensory afferents evoked persistent gamma oscillations in the local field potential. These oscillations arose from slower, GABA(A) receptor-independent intracolumnar oscillators coupled by GABA(A)-ergic synapses into a faster, broadly coherent network oscillation. Consistent with the theoretical properties of coupled-oscillator networks, the spatial extent of zero-phase coherence was bounded in slices by the reduced density of lateral interactions. The intact in vivo network, however, exhibited long-range lateral interactions that suffice in simulation to enable zero-phase gamma coherence across the olfactory bulb. The timing of action potentials in a subset of principal neurons was phase-constrained with respect to evoked gamma oscillations. Coupled-oscillator dynamics in olfactory bulb thereby enable a common clock, robust to biological heterogeneities, that is capable of supporting gamma-band spike synchronization and phase coding across the ensemble of activated principal neurons.NEW & NOTEWORTHY Odor stimulation evokes rhythmic gamma oscillations in the field potential of the olfactory bulb, but the dynamical mechanisms governing these oscillations have remained unclear. Establishing these mechanisms is important as they determine the biophysical capacities of the bulbar circuit to, for example, maintain zero-phase coherence across a spatially extended network, or coordinate the timing of action potentials in principal neurons. These properties in turn constrain and suggest hypotheses of sensory coding.

Rates of ultrasonic vocalizations are more strongly related than acoustic features to non-vocal behaviors in mouse pups.

Mouse pups produce. ultrasonic vocalizations (USVs) in response to isolation from the nest (i.e., isolation USVs). Rates and acoustic features of isolation USVs change dramatically over the first two weeks of life, and there is also substantial variability in the rates and acoustic features of isolation USVs at a given postnatal age. The factors that contribute to within age variability in isolation USVs remain largely unknown. Here, we explore the extent to which non-vocal behaviors of mouse pups relate to the within age variability in rates and acoustic features of their USVs. We recorded non-vocal behaviors of isolated C57BL/6J mouse pups at four postnatal ages (postnatal days 5, 10, 15, and 20), measured rates of isolation USV production, and applied a combination of pre-defined acoustic feature measurements and an unsupervised machine learning-based vocal analysis method to examine USV acoustic features. When we considered different categories of non-vocal behavior, our analyses revealed that mice in all postnatal age groups produce higher rates of isolation USVs during active non-vocal behaviors than when lying still. Moreover, rates of isolation USVs are correlated with the intensity (i.e., magnitude) of non-vocal body and limb movements within a given trial. In contrast, USVs produced during different categories of non-vocal behaviors and during different intensities of non-vocal movement do not differ substantially in their acoustic features. Our findings suggest that levels of behavioral arousal contribute to within age variability in rates, but not acoustic features, of mouse isolation USVs.

A physicochemical model of odor sampling.

We present a general physicochemical sampling model for olfaction, based on established pharmacological laws, in which arbitrary combinations of odorant ligands and receptors can be generated and their individual and collective effects on odor representations and olfactory performance measured. Individual odor ligands exhibit receptor-specific affinities and efficacies; that is, they may bind strongly or weakly to a given receptor, and can act as strong agonists, weak agonists, partial agonists, or antagonists. Ligands interacting with common receptors compete with one another for dwell time; these competitive interactions appropriately simulate the degeneracy that fundamentally defines the capacities and limitations of odorant sampling. The outcome of these competing ligand-receptor interactions yields a pattern of receptor activation levels, thereafter mapped to glomerular presynaptic activation levels based on the convergence of sensory neuron axons. The metric of greatest interest is the mean discrimination sensitivity, a measure of how effectively the olfactory system at this level is able to recognize a small change in the physicochemical quality of a stimulus. This model presents several significant outcomes, both expected and surprising. First, adding additional receptors reliably improves the system's discrimination sensitivity. Second, in contrast, adding additional ligands to an odorscene initially can improve discrimination sensitivity, but eventually will reduce it as the number of ligands increases. Third, the presence of antagonistic ligand-receptor interactions produced clear benefits for sensory system performance, generating higher absolute discrimination sensitivities and increasing the numbers of competing ligands that could be present before discrimination sensitivity began to be impaired. Finally, the model correctly reflects and explains the modest reduction in odor discrimination sensitivity exhibited by transgenic mice in which the specificity of glomerular targeting by primary olfactory neurons is partially disrupted.

A Systematic Framework for Olfactory Bulb Signal Transformations.

We describe an integrated theory of olfactory systems operation that incorporates experimental findings across scales, stages, and methods of analysis into a common framework. In particular, we consider the multiple stages of olfactory signal processing as a collective system, in which each stage samples selectively from its antecedents. We propose that, following the signal conditioning operations of the nasal epithelium and glomerular-layer circuitry, the plastic external plexiform layer of the olfactory bulb effects a process of category learning-the basis for extracting meaningful, quasi-discrete odor representations from the metric space of undifferentiated olfactory quality. Moreover, this early categorization process also resolves the foundational problem of how odors of interest can be recognized in the presence of strong competitive interference from simultaneously encountered background odorants. This problem is fundamentally constraining on early-stage olfactory encoding strategies and must be resolved if these strategies and their underlying mechanisms are to be understood. Multiscale general theories of olfactory systems operation are essential in order to leverage the analytical advantages of engineered approaches together with our expanding capacity to interrogate biological systems.

Dextran-coated iron oxide nanoparticle-induced nanotoxicity in neuron cultures.

Recent technological advances have introduced diverse engineered nanoparticles (ENPs) into our air, water, medicine, cosmetics, clothing, and food. However, the health and environmental effects of these increasingly common ENPs are still not well understood. In particular, potential neurological effects are one of the most poorly understood areas of nanoparticle toxicology (nanotoxicology), in that low-to-moderate neurotoxicity can be subtle and difficult to measure. Culturing primary neuron explants on planar microelectrode arrays (MEAs) has emerged as one of the most promising in vitro techniques with which to study neuro-nanotoxicology, as MEAs enable the fluorescent tracking of nanoparticles together with neuronal electrical activity recording at the submillisecond time scale, enabling the resolution of individual action potentials. Here we examine the dose-dependent neurotoxicity of dextran-coated iron oxide nanoparticles (dIONPs), a common type of functionalized ENP used in biomedical applications, on cultured primary neurons harvested from postnatal day 0-1 mouse brains. A range of dIONP concentrations (5-40 µg/ml) were added to neuron cultures, and cells were plated either onto well plates for live cell, fluorescent reactive oxidative species (ROS) and viability observations, or onto planar microelectrode arrays (MEAs) for electrophysiological measurements. Below 10 µg/ml, there were no dose-dependent cellular ROS increases or effects in MEA bursting behavior at sub-lethal dosages. However, above 20 µg/ml, cell death was obvious and widespread. Our findings demonstrate a significant dIONP toxicity in cultured neurons at concentrations previously reported to be safe for stem cells and other non-neuronal cell types.

Context-dependent odor learning requires the anterior olfactory nucleus.

Learning to associate the context in which a stimulus occurs is an important aspect of animal learning. We propose that the association of an olfactory stimulus with its multisensory context is mediated by projections from ventral hippocampus (vHC) networks to the anterior olfactory nucleus (AON). Using a contextually cued olfactory discrimination task, rats were trained to associate 2 olfactory stimuli with different responses depending on visuospatial context. Temporary lesions of the AON or vHC impaired performance on this task. In contrast, such lesions did not impair performance on a noncontextual olfactory discrimination task. Moreover, vHC lesions also impaired performance on an analogous contextually cued texture discrimination task, whereas AON lesions affected only olfactory contextual associations. We describe a distinct role for the AON in olfactory processing and conclude that early olfactory networks such as the olfactory bulb and AON function as multimodal integration networks rather than processing olfactory signals exclusively. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Rapid online learning and robust recall in a neuromorphic olfactory circuit.

We present a neural algorithm for the rapid online learning and identification of odourant samples under noise, based on the architecture of the mammalian olfactory bulb and implemented on the Intel Loihi neuromorphic system. As with biological olfaction, the spike timing-based algorithm utilizes distributed, event-driven computations and rapid (one-shot) online learning. Spike timing-dependent plasticity rules operate iteratively over sequential gamma-frequency packets to construct odour representations from the activity of chemosensor arrays mounted in a wind tunnel. Learned odourants then are reliably identified despite strong destructive interference. Noise resistance is further enhanced by neuromodulation and contextual priming. Lifelong learning capabilities are enabled by adult neurogenesis. The algorithm is applicable to any signal identification problem in which high-dimensional signals are embedded in unknown backgrounds.

Central olfactory structures.

Axons from the olfactory bulb (OB) project to multiple central structures of the brain, many of which, in turn, send axons back into the OB and/or to one another. These secondary sensory regions underlie many aspects of odor representation, valence, and learning, as well as serving some nonolfactory functions, though many details remain unclear. We here describe the connectivity and essential structural and functional properties of these postbulbar olfactory regions in the mammalian brain.

A Spike Time-Dependent Online Learning Algorithm Derived From Biological Olfaction.

We have developed a spiking neural network (SNN) algorithm for signal restoration and identification based on principles extracted from the mammalian olfactory system and broadly applicable to input from arbitrary sensor arrays. For interpretability and development purposes, we here examine the properties of its initial feedforward projection. Like the full algorithm, this feedforward component is fully spike timing-based, and utilizes online learning based on local synaptic rules such as spike timing-dependent plasticity (STDP). Using an intermediate metric to assess the properties of this initial projection, the feedforward network exhibits high classification performance after few-shot learning without catastrophic forgetting, and includes a none of the above outcome to reflect classifier confidence. We demonstrate online learning performance using a publicly available machine olfaction dataset with challenges including relatively small training sets, variable stimulus concentrations, and 3 years of sensor drift.

Code Generation in Computational Neuroscience: A Review of Tools and Techniques.

Advances in experimental techniques and computational power allowing researchers to gather anatomical and electrophysiological data at unprecedented levels of detail have fostered the development of increasingly complex models in computational neuroscience. Large-scale, biophysically detailed cell models pose a particular set of computational challenges, and this has led to the development of a number of domain-specific simulators. At the other level of detail, the ever growing variety of point neuron models increases the implementation barrier even for those based on the relatively simple integrate-and-fire neuron model. Independently of the model complexity, all modeling methods crucially depend on an efficient and accurate transformation of mathematical model descriptions into efficiently executable code. Neuroscientists usually publish model descriptions in terms of the mathematical equations underlying them. However, actually simulating them requires they be translated into code. This can cause problems because errors may be introduced if this process is carried out by hand, and code written by neuroscientists may not be very computationally efficient. Furthermore, the translated code might be generated for different hardware platforms, operating system variants or even written in different languages and thus cannot easily be combined or even compared. Two main approaches to addressing this issues have been followed. The first is to limit users to a fixed set of optimized models, which limits flexibility. The second is to allow model definitions in a high level interpreted language, although this may limit performance. Recently, a third approach has become increasingly popular: using code generation to automatically translate high level descriptions into efficient low level code to combine the best of previous approaches. This approach also greatly enriches efforts to standardize simulator-independent model description languages. In the past few years, a number of code generation pipelines have been developed in the computational neuroscience community, which differ considerably in aim, scope and functionality. This article provides an overview of existing pipelines currently used within the community and contrasts their capabilities and the technologies and concepts behind them.

Generative Biophysical Modeling of Dynamical Networks in the Olfactory System.

Generative models are computational models designed to generate appropriate values for all of their embedded variables, thereby simulating the response properties of a complex system based on the coordinated interactions of a multitude of physical mechanisms. In systems neuroscience, generative models are generally biophysically based compartmental models of neurons and networks that are explicitly multiscale, being constrained by experimental data at multiple levels of organization from cellular membrane properties to large-scale network dynamics. As such, they are able to explain the origins of emergent properties in complex systems, and serve as tests of sufficiency and as quantitative instantiations of working hypotheses that may be too complex to simply intuit. Moreover, when adequately constrained, generative biophysical models are able to predict novel experimental outcomes, and consequently are powerful tools for experimental design. We here outline a general strategy for the iterative design and implementation of generative, multiscale biophysical models of neural systems. We illustrate this process using our ongoing, iteratively developing model of the mammalian olfactory bulb. Because the olfactory bulb exhibits diverse and interesting properties at multiple scales of organization, it is an attractive system in which to illustrate the value of generative modeling across scales.

Kinase activity in the olfactory bulb is required for odor memory consolidation.

Long-term fear memory formation in the hippocampus and neocortex depends upon brain-derived neurotrophic factor (BDNF) signaling after acquisition. Incremental, appetitive odor discrimination learning is thought to depend substantially on the differentiation of adult-born neurons within the olfactory bulb (OB)-a process that is closely associated with BDNF signaling. We sought to elucidate the role of neurotrophin signaling within the OB on odor memory consolidation. Male mice were trained on odor-reward associative discriminations after bilateral infusion of the kinase inhibitor K252a, or vehicle control, into the OB. K252a is a partially selective inhibitor of tyrosine kinase (Trk) receptors, including the TrkB receptor for BDNF, though it also inhibits other plasticity-related kinases such as PKC and CaMKII/IV. K252a infusion into the OB did not impair odor acquisition or short-term (2 h) memory for the learned discriminations, but significantly impaired long-term (48 h) odor memory (LTM). This LTM deficit also was associated with reduced selectivity for the conditioned odorant in a reward-seeking digging task. Infusions of K252a immediately prior to testing did not impair LTM recall. These results indicate that kinase activation in the OB is required for the consolidation of odor memory of incrementally acquired information.

Toxoplasma gondii alters NMDAR signaling and induces signs of Alzheimer's disease in wild-type, C57BL/6 mice.

BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disease associated with cognitive decline and complete loss of basic functions. The ubiquitous apicomplexan parasite Toxoplasma gondii (T. gondii) infects up to one third of the world's population and is implicated in AD. METHODS: We infected C57BL/6 wild-type male and female mice with 10 T. gondii ME49 cysts and assessed whether infection led to behavioral and anatomical effects using immunohistochemistry, immunofluorescence, Western blotting, cell culture assays, as well as an array of mouse behavior tests. RESULTS: We show that T. gondii infection induced two major hallmarks of AD in the brains of C57BL/6 male and female mice: beta-amyloid (Aß) immunoreactivity and hyperphosphorylated Tau. Infected mice showed significant neuronal death, loss of N-methyl-D-aspartate receptor (NMDAR) expression, and loss of olfactory sensory neurons. T. gondii infection also caused anxiety-like behavior, altered recognition of social novelty, altered spatial memory, and reduced olfactory sensitivity. This last finding was exclusive to male mice, as infected females showed intact olfactory sensitivity. CONCLUSIONS: These results demonstrate that T. gondii can induce advanced signs of AD in wild-type mice and that it may induce AD in some individuals with underlying health problems.

A coupled-oscillator model of olfactory bulb gamma oscillations.

The olfactory bulb transforms not only the information content of the primary sensory representation, but also its underlying coding metric. High-variance, slow-timescale primary odor representations are transformed by bulbar circuitry into secondary representations based on principal neuron spike patterns that are tightly regulated in time. This emergent fast timescale for signaling is reflected in gamma-band local field potentials, presumably serving to efficiently integrate olfactory sensory information into the temporally regulated information networks of the central nervous system. To understand this transformation and its integration with interareal coordination mechanisms requires that we understand its fundamental dynamical principles. Using a biophysically explicit, multiscale model of olfactory bulb circuitry, we here demonstrate that an inhibition-coupled intrinsic oscillator framework, pyramidal resonance interneuron network gamma (PRING), best captures the diversity of physiological properties exhibited by the olfactory bulb. Most importantly, these properties include global zero-phase synchronization in the gamma band, the phase-restriction of informative spikes in principal neurons with respect to this common clock, and the robustness of this synchronous oscillatory regime to multiple challenging conditions observed in the biological system. These conditions include substantial heterogeneities in afferent activation levels and excitatory synaptic weights, high levels of uncorrelated background activity among principal neurons, and spike frequencies in both principal neurons and interneurons that are irregular in time and much lower than the gamma frequency. This coupled cellular oscillator architecture permits stable and replicable ensemble responses to diverse sensory stimuli under various external conditions as well as to changes in network parameters arising from learning-dependent synaptic plasticity.

Comparing Phlebotomy by Tail Tip Amputation, Facial Vein Puncture, and Tail Vein Incision in C57BL/6 Mice by Using Physiologic and Behavioral Metrics of Pain and Distress.

Tail tip amputation with minimal restraint is not widely used for mouse phlebotomy. In part, this infrequency may reflect policies influenced by tail tip amputation procedures for genotyping, which involve greater handling and tissue removal. To assess tail tip amputation with minimal restraint as a phlebotomy technique, we compared it with 2 more common methods: scruffing with facial vein puncture and lateral tail vein incision with minimal restraint. Blood glucose levels, audible and ultrasonic vocalizations, postphlebotomy activity and grooming behavior, open field and elevated plus maze behaviors, nest-building scores, and histologic changes at the phlebotomy site were evaluated. Mice in the facial vein phlebotomy group produced more audible vocalizations, exhibited lower postphlebotomy activity in the open field, and had more severe histologic changes than did mice in the tail incision and tail tip amputation groups. Facial vein phlebotomy did not affect grooming behavior relative to sham groups, whereas tail vein incision-but not tail tip amputation-increased tail grooming compared with that in control mice. Blood glucose levels, nest-building scores, and elevated plus maze behavior did not differ between groups, and no mice in any group produced ultrasonic vocalizations. Tail tip amputation mice did not perform differently than sham mice in any metric analyzed, indicating that this technique is a potentially superior method of blood collection in mice in terms of animal wellbeing.

Neuromodulation of olfactory transformations.

The olfactory bulb and piriform cortex are the best studied structures of the mammalian olfactory system and are heavily innervated by extrinsic neuromodulatory inputs. The state-dependent release of acetylcholine, norepinephrine, serotonin, and other neuromodulators into these olfactory structures alters a constellation of physiological parameters in neurons and synapses that together modify the computations performed on sensory signals. These modifications affect the specificity, detectability, discriminability, and other properties of odor representations and thereby govern perceptual performance. Whereas different neuromodulators have distinct cellular effects, and tend to be associated with nominally different functions, it also is clear that these purported functions overlap substantially, and that ad hoc hypotheses regarding the roles of particular neuromodulators may have reached the limits of their usefulness.

Biophysical constraints on lateral inhibition in the olfactory bulb.

The mitral cells (MCs) of the mammalian olfactory bulb (OB) constitute one of two populations of principal neurons (along with middle/deep tufted cells) that integrate afferent olfactory information with top-down inputs and intrinsic learning and deliver output to downstream olfactory areas. MC activity is regulated in part by inhibition from granule cells, which form reciprocal synapses with MCs along the extents of their lateral dendrites. However, with MC lateral dendrites reaching over 1.5 mm in length in rats, the roles of distal inhibitory synapses pose a quandary. Here, we systematically vary the properties of a MC model to assess the capacity of inhibitory synaptic inputs on lateral dendrites to influence afferent information flow through MCs. Simulations using passivized models with varying dendritic morphologies and synaptic properties demonstrated that, even with unrealistically favorable parameters, passive propagation fails to convey effective inhibitory signals to the soma from distal sources. Additional simulations using an active model exhibiting action potentials, subthreshold oscillations, and a dendritic morphology closely matched to experimental values further confirmed that distal synaptic inputs along the lateral dendrite could not exert physiologically relevant effects on MC spike timing at the soma. Larger synaptic conductances representative of multiple simultaneous inputs were not sufficient to compensate for the decline in signal with distance. Reciprocal synapses on distal MC lateral dendrites may instead serve to maintain a common fast oscillatory clock across the OB by delaying spike propagation within the lateral dendrites themselves.

Functional differentiation of cholinergic and noradrenergic modulation in a biophysical model of olfactory bulb granule cells.

Olfactory bulb granule cells are modulated by both acetylcholine (ACh) and norepinephrine (NE), but the effects of these neuromodulators have not been clearly distinguished. We used detailed biophysical simulations of granule cells, both alone and embedded in a microcircuit with mitral cells, to measure and distinguish the effects of ACh and NE on cellular and microcircuit function. Cholinergic and noradrenergic modulatory effects on granule cells were based on data obtained from slice experiments; specifically, ACh reduced the conductance densities of the potassium M current and the calcium-dependent potassium current, whereas NE nonmonotonically regulated the conductance density of an ohmic potassium current. We report that the effects of ACh and NE on granule cell physiology are distinct and functionally complementary to one another. ACh strongly regulates granule cell firing rates and afterpotentials, whereas NE bidirectionally regulates subthreshold membrane potentials. When combined, NE can regulate the ACh-induced expression of afterdepolarizing potentials and persistent firing. In a microcircuit simulation developed to investigate the effects of granule cell neuromodulation on mitral cell firing properties, ACh increased spike synchronization among mitral cells, whereas NE modulated the signal-to-noise ratio. Coapplication of ACh and NE both functionally improved the signal-to-noise ratio and enhanced spike synchronization among mitral cells. In summary, our computational results support distinct and complementary roles for ACh and NE in modulating olfactory bulb circuitry and suggest that NE may play a role in the regulation of cholinergic function.

Embracing multiple definitions of learning.

Definitions of learning vary widely across disciplines, driven largely by different approaches used to assess its occurrence. These definitions can be better reconciled with each other if each is recognized as coherent with a common conceptualization of learning, while appreciating the practical utility of different learning definitions in different contexts.

Properties and mechanisms of olfactory learning and memory.

Memories are dynamic physical phenomena with psychometric forms as well as characteristic timescales. Most of our understanding of the cellular mechanisms underlying the neurophysiology of memory, however, derives from one-trial learning paradigms that, while powerful, do not fully embody the gradual, representational, and statistical aspects of cumulative learning. The early olfactory system-particularly olfactory bulb-comprises a reasonably well-understood and experimentally accessible neuronal network with intrinsic plasticity that underlies both one-trial (adult aversive, neonatal) and cumulative (adult appetitive) odor learning. These olfactory circuits employ many of the same molecular and structural mechanisms of memory as, for example, hippocampal circuits following inhibitory avoidance conditioning, but the temporal sequences of post-conditioning molecular events are likely to differ owing to the need to incorporate new information from ongoing learning events into the evolving memory trace. Moreover, the shapes of acquired odor representations, and their gradual transformation over the course of cumulative learning, also can be directly measured, adding an additional representational dimension to the traditional metrics of memory strength and persistence. In this review, we describe some established molecular and structural mechanisms of memory with a focus on the timecourses of post-conditioning molecular processes. We describe the properties of odor learning intrinsic to the olfactory bulb and review the utility of the olfactory system of adult rodents as a memory system in which to study the cellular mechanisms of cumulative learning.