Intrinsic biophysical diversity decorrelates neuronal firing while increasing information content

Behavioral discrimination and olfactory bulb encoding of odor plume intermittency

In order to survive, animals often need to navigate a complex odor landscape where odors can exist in airborne plumes. Several odor plume properties change with distance from the odor source, providing potential navigational cues to searching animals. Here, we focus on odor intermittency, a temporal odor plume property that measures the fraction of time odor is above a threshold at a given point within the plume and decreases with increasing distance from the odor source. We sought to determine if mice can use changes in intermittency to locate an odor source. To do so, we trained mice on an intermittency discrimination task. We establish that mice can discriminate odor plume samples of low and high intermittency and that the neural responses in the olfactory bulb can account for task performance and support intermittency encoding. Modulation of sniffing, a behavioral parameter that is highly dynamic during odor-guided navigation, affects both behavioral outcome on the intermittency discrimination task and neural representation of intermittency. Together, this work demonstrates that intermittency is an odor plume property that can inform olfactory search and more broadly supports the notion that mammalian odor-based navigation can be guided by temporal odor plume properties.

Neural heterogeneity controls computations in spiking neural networks

The brain is composed of complex networks of interacting neurons that express considerable heterogeneity in their physiology and spiking characteristics. How does this neural heterogeneity influence macroscopic neural dynamics, and how might it contribute to neural computation? In this work, we use a mean-field model to investigate computation in heterogeneous neural networks, by studying how the heterogeneity of cell spiking thresholds affects three key computational functions of a neural population: the gating, encoding, and decoding of neural signals. Our results suggest that heterogeneity serves different computational functions in different cell types. In inhibitory interneurons, varying the degree of spike threshold heterogeneity allows them to gate the propagation of neural signals in a reciprocally coupled excitatory population. Whereas homogeneous interneurons impose synchronized dynamics that narrow the dynamic repertoire of the excitatory neurons, heterogeneous interneurons act as an inhibitory offset while preserving excitatory neuron function. Spike threshold heterogeneity also controls the entrainment properties of neural networks to periodic input, thus affecting the temporal gating of synaptic inputs. Among excitatory neurons, heterogeneity increases the dimensionality of neural dynamics, improving the network's capacity to perform decoding tasks. Conversely, homogeneous networks suffer in their capacity for function generation, but excel at encoding signals via multistable dynamic regimes. Drawing from these findings, we propose intra-cell-type heterogeneity as a mechanism for sculpting the computational properties of local circuits of excitatory and inhibitory spiking neurons, permitting the same canonical microcircuit to be tuned for diverse computational tasks.

Diversity-induced trivialization and resilience of neural dynamics

Heterogeneity is omnipresent across all living systems. Diversity enriches the dynamical repertoire of these systems but remains challenging to reconcile with their manifest robustness and dynamical persistence over time, a fundamental feature called resilience. To better understand the mechanism underlying resilience in neural circuits, we considered a nonlinear network model, extracting the relationship between excitability heterogeneity and resilience. To measure resilience, we quantified the number of stationary states of this network, and how they are affected by various control parameters. We analyzed both analytically and numerically gradient and non-gradient systems modeled as non-linear sparse neural networks evolving over long time scales. Our analysis shows that neuronal heterogeneity quenches the number of stationary states while decreasing the susceptibility to bifurcations: a phenomenon known as trivialization. Heterogeneity was found to implement a homeostatic control mechanism enhancing network resilience to changes in network size and connection probability by quenching the system's dynamic volatility.

Controlling morpho-electrophysiological variability of neurons with detailed biophysical models

Variability, which is known to be a universal feature among biological units such as neuronal cells, holds significant importance, as, for example, it enables a robust encoding of a high volume of information in neuronal circuits and prevents hypersynchronizations. While most computational studies on electrophysiological variability in neuronal circuits were done with single-compartment neuron models, we instead focus on the variability of detailed biophysical models of neuron multi-compartmental morphologies. We leverage a Markov chain Monte Carlo method to generate populations of electrical models reproducing the variability of experimental recordings while being compatible with a set of morphologies to faithfully represent specifi morpho-electrical type. We demonstrate our approach on layer 5 pyramidal cells and study the morpho-electrical variability and in particular, find that morphological variability alone is insufficient to reproduce electrical variability. Overall, this approach provides a strong statistical basis to create detailed models of neurons with controlled variability.

Rapid, Activity-Dependent Intrinsic Plasticity in the Developing Zebra Finch Auditory Cortex

The acoustic environment an animal experiences early in life shapes the structure and function of its auditory system. This process of experience-dependent development is thought to be primarily orchestrated by potentiation and depression of synapses, but plasticity of intrinsic voltage dynamics may also contribute. Here, we show that in juvenile male and female zebra finches, neurons in a cortical-level auditory area, the caudal mesopallium (CM), can rapidly change their firing dynamics. This plasticity was only observed in birds that were reared in a complex acoustic and social environment, which also caused increased expression of the low-threshold potassium channel Kv1.1 in the plasma membrane and endoplasmic reticulum (ER). Intrinsic plasticity depended on activity, was reversed by blocking low-threshold potassium currents, and was prevented by blocking intracellular calcium signaling. Taken together, these results suggest that Kv1.1 is rapidly mobilized to the plasma membrane by activity-dependent elevation of intracellular calcium. This produces a shift in the excitability and temporal integration of CM neurons that may be permissive for auditory learning in complex acoustic environments during a crucial period for the development of vocal perception and production.SIGNIFICANCE STATEMENT Neurons can change not only the strength of their connections to other neurons, but also how they integrate synaptic currents to produce patterns of action potentials. In contrast to synaptic plasticity, the mechanisms and functional roles of intrinisic plasticity remain poorly understood. We found that neurons in the zebra finch auditory cortex can rapidly shift their spiking dynamics within a few minutes in response to intracellular stimulation. This plasticity involves increased conductance of a low-threshold potassium current associated with the Kv1.1 channel, but it only occurs in birds reared in a rich acoustic environment. Thus, auditory experience regulates a mechanism of neural plasticity that allows neurons to rapidly adapt their firing dynamics to stimulation.

Relation of resting brain signal variability to cognitive and socioemotional measures in an adult lifespan sample

Temporal variability of the fMRI-derived blood-oxygen-level-dependent (BOLD) signal during cognitive tasks shows important associations with individual differences in age and performance. Less is known about relations between spontaneous BOLD variability measured at rest and relatively stable cognitive measures, such as IQ or socioemotional function. Here, we examined associations among resting BOLD variability, cognitive/socioemotional scores from the NIH Toolbox and optimal time of day for alertness (chronotype) in a sample of 157 adults from 20 to 86 years of age. To investigate individual differences in these associations independently of age, we regressed age out from both behavioral and BOLD variability scores. We hypothesized that greater BOLD variability would be related to higher fluid cognition scores, more positive scores on socioemotional scales and a morningness chronotype. Consistent with this idea, we found positive correlations between resting BOLD variability, positive socioemotional scores (e.g. self-efficacy) and morning chronotype, as well as negative correlations between variability and negative emotional scores (e.g. loneliness). Unexpectedly, we found negative correlations between BOLD variability and fluid cognition. These results suggest that greater resting brain signal variability facilitates optimal socioemotional function and characterizes those with morning-type circadian rhythms, but individuals with greater fluid cognition may be more likely to show less temporal variability in spontaneous measures of BOLD activity.

Adult-born granule cells improve stimulus encoding and discrimination in the dentate gyrus

Heterogeneity plays an important role in diversifying neural responses to support brain function. Adult neurogenesis provides the dentate gyrus with a heterogeneous population of granule cells (GCs) that were born and developed their properties at different times. Immature GCs have distinct intrinsic and synaptic properties than mature GCs and are needed for correct encoding and discrimination in spatial tasks. How immature GCs enhance the encoding of information to support these functions is not well understood. Here, we record the responses to fluctuating current injections of GCs of different ages in mouse hippocampal slices to study how they encode stimuli. Immature GCs produce unreliable responses compared to mature GCs, exhibiting imprecise spike timings across repeated stimulation. We use a statistical model to describe the stimulus-response transformation performed by GCs of different ages. We fit this model to the data and obtain parameters that capture GCs' encoding properties. Parameter values from this fit reflect the maturational differences of the population and indicate that immature GCs perform a differential encoding of stimuli. To study how this age heterogeneity influences encoding by a population, we perform stimulus decoding using populations that contain GCs of different ages. We find that, despite their individual unreliability, immature GCs enhance the fidelity of the signal encoded by the population and improve the discrimination of similar time-dependent stimuli. Thus, the observed heterogeneity confers the population with enhanced encoding capabilities.

Hyperpolarization-Activated Cation Channels Shape the Spiking Frequency Preference of Human Cortical Layer 5 Pyramidal Neurons

Discerning the contribution of specific ionic currents to complex neuronal dynamics is a difficult, but important, task. This challenge is exacerbated in the human setting, although the widely characterized uniqueness of the human brain compared with preclinical models necessitates the direct study of human neurons. Neuronal spiking frequency preference is of particular interest given its role in rhythm generation and signal transmission in cortical circuits. Here, we combine the frequency-dependent gain (FDG), a measure of spiking frequency preference, and novel in silico analyses to dissect the contributions of individual ionic currents to the suprathreshold features of human layer 5 (L5) neurons captured by the FDG. We confirm that a contemporary model of such a neuron, primarily constrained to capture subthreshold activity driven by the hyperpolarization-activated cyclic nucleotide gated (h-) current, replicates key features of the in vitro FDG both with and without h-current activity. With the model confirmed as a viable approximation of the biophysical features of interest, we applied new analysis techniques to quantify the activity of each modeled ionic current in the moments before spiking, revealing unique dynamics of the h-current. These findings motivated patch-clamp recordings in analogous rodent neurons to characterize their FDG, which confirmed that a biophysically detailed model of these neurons captures key interspecies differences in the FDG. These differences are correlated with distinct contributions of the h-current to neuronal activity. Together, this interdisciplinary and multispecies study provides new insights directly relating the dynamics of the h-current to suprathreshold spiking frequency preference in human L5 neurons.

Logarithmically scaled, gamma distributed neuronal spiking

Naturally log-scaled quantities abound in the nervous system. Distributions of these quantities have non-intuitive properties, which have implications for data analysis and the understanding of neural circuits. Here, we review the log-scaled statistics of neuronal spiking and the relevant analytical probability distributions. Recent work using log-scaling revealed that interspike intervals of forebrain neurons segregate into discrete modes reflecting spiking at different timescales and are each well-approximated by a gamma distribution. Each neuron spends most of the time in an irregular spiking 'ground state' with the longest intervals, which determines the mean firing rate of the neuron. Across the entire neuronal population, firing rates are log-scaled and well approximated by the gamma distribution, with a small number of highly active neurons and an overabundance of low rate neurons (the 'dark matter'). These results are intricately linked to a heterogeneous balanced operating regime, which confers upon neuronal circuits multiple computational advantages and has evolutionarily ancient origins.

Receptive field sizes and neuronal encoding bandwidth are constrained by axonal conduction delays

Studies on population coding implicitly assume that spikes from the presynaptic cells arrive simultaneously at the integrating neuron. In natural neuronal populations, this is usually not the case-neuronal signaling takes time and populations cover a certain space. The spread of spike arrival times depends on population size, cell density and axonal conduction velocity. Here we analyze the consequences of population size and axonal conduction delays on the stimulus encoding performance in the electrosensory system of the electric fish Apteronotus leptorhynchus. We experimentally locate p-type electroreceptor afferents along the rostro-caudal body axis and relate locations to neurophysiological response properties. In an information-theoretical approach we analyze the coding performance in homogeneous and heterogeneous populations. As expected, the amount of information increases with population size and, on average, heterogeneous populations encode better than the average same-size homogeneous population, if conduction delays are compensated for. The spread of neuronal conduction delays within a receptive field strongly degrades encoding of high-frequency stimulus components. Receptive field sizes typically found in the electrosensory lateral line lobe of A. leptorhynchus appear to be a good compromise between the spread of conduction delays and encoding performance. The limitations imposed by finite axonal conduction velocity are relevant for any converging network as is shown by model populations of LIF neurons. The bandwidth of natural stimuli and the maximum meaningful population sizes are constrained by conduction delays and may thus impact the optimal design of nervous systems.

Descending pathways increase sensory neural response heterogeneity to facilitate decoding and behavior

The functional role of heterogeneous spiking responses of otherwise similarly tuned neurons to stimulation, which has been observed ubiquitously, remains unclear to date. Here, we demonstrate that such response heterogeneity serves a beneficial function that is used by downstream brain areas to generate behavioral responses that follows the detailed timecourse of the stimulus. Multi-unit recordings from sensory pyramidal cells within the electrosensory system of Apteronotus leptorhynchus were performed and revealed highly heterogeneous responses that were similar for all cell types. By comparing the coding properties of a given neural population before and after inactivation of descending pathways, we found that heterogeneities were beneficial as decoding was then more robust to the addition of noise. Taken together, our results not only reveal that descending pathways actively promote response heterogeneity within a given cell type, but also uncover a beneficial function for such heterogeneity that is used by the brain to generate behavior.

Intrinsic neural diversity quenches the dynamic volatility of neural networks

Heterogeneity is the norm in biology. The brain is no different: Neuronal cell types are myriad, reflected through their cellular morphology, type, excitability, connectivity motifs, and ion channel distributions. While this biophysical diversity enriches neural systems' dynamical repertoire, it remains challenging to reconcile with the robustness and persistence of brain function over time (resilience). To better understand the relationship between excitability heterogeneity (variability in excitability within a population of neurons) and resilience, we analyzed both analytically and numerically a nonlinear sparse neural network with balanced excitatory and inhibitory connections evolving over long time scales. Homogeneous networks demonstrated increases in excitability, and strong firing rate correlations-signs of instability-in response to a slowly varying modulatory fluctuation. Excitability heterogeneity tuned network stability in a context-dependent way by restraining responses to modulatory challenges and limiting firing rate correlations, while enriching dynamics during states of low modulatory drive. Excitability heterogeneity was found to implement a homeostatic control mechanism enhancing network resilience to changes in population size, connection probability, strength and variability of synaptic weights, by quenching the volatility (i.e., its susceptibility to critical transitions) of its dynamics. Together, these results highlight the fundamental role played by cell-to-cell heterogeneity in the robustness of brain function in the face of change.

Heterogeneity of voltage gated sodium current density between neurons decorrelates spiking and suppresses network synchronization in Scn1b null mouse models

Voltage gated sodium channels (VGSCs) are required for action potential initiation and propagation in mammalian neurons. As with other ion channel families, VGSC density varies between neurons. Importantly, sodium current (INa) density variability is reduced in pyramidal neurons of Scn1b null mice. Scn1b encodes the VGSC β1/ β1B subunits, which regulate channel expression, trafficking, and voltage dependent properties. Here, we investigate how variable INa density in cortical layer 6 and subicular pyramidal neurons affects spike patterning and network synchronization. Constitutive or inducible Scn1b deletion enhances spike timing correlations between pyramidal neurons in response to fluctuating stimuli and impairs spike-triggered average current pattern diversity while preserving spike reliability. Inhibiting INa with a low concentration of tetrodotoxin similarly alters patterning without impairing reliability, with modest effects on firing rate. Computational modeling shows that broad INa density ranges confer a similarly broad spectrum of spike patterning in response to fluctuating synaptic conductances. Network coupling of neurons with high INa density variability displaces the coupling requirements for synchronization and broadens the dynamic range of activity when varying synaptic strength and network topology. Our results show that INa heterogeneity between neurons potently regulates spike pattern diversity and network synchronization, expanding VGSC roles in the nervous system.

Extreme multistability and phase synchronization in a heterogeneous bi-neuron Rulkov network with memristive electromagnetic induction

Memristive electromagnetic induction effect has been widely explored in bi-neuron network with homogeneous neurons, but rarely in bi-neuron network with heterogeneous ones. This paper builds a bi-neuron network by coupling heterogeneous Rulkov neurons with memristor and investigates the memristive electromagnetic induction effect. Theoretical analysis discloses that the bi-neuron network possesses a line equilibrium state and its stability depends on the memristor coupling strength and initial condition. That is, the stability of the line equilibrium state has a transition between unstable saddle-focus and stable node-focus via Hopf bifurcation. By employing parameters located in the stable node-focus region, dynamical behaviors related to the memristor coupling strength and initial conditions are revealed by Julia- and MATLAB-based multiple numerical tools. Numerical results demonstrate that the proposed heterogeneous bi-neuron Rulkov network can generate point attractor, period, chaos, chaos crisis, and period-doubling bifurcation. Note that extreme multistability are disclosed with respect to initial conditions of memristor and gated ion concentration. Coexisting infinitely multiple firing patterns of periodic firing patterns with different periodicities and chaotic firing patterns for different memristor initial conditions are demonstrated by phase portrait and time-domain waveform. Besides, the phase synchronization related to the memristor coupling strength and its initial condition is explored, which suggests that the two heterogeneous neurons become phase synchronization with large memristor coupling strength and initial condition. This also reflects that the plasticity of memristor synapse enables adaptive regulation in keeping energy balance between the neurons. What's more, MCU-based hardware experiments are executed to further confirm the numerical simulations.

Degeneracy in epilepsy: multiple routes to hyperexcitable brain circuits and their repair

Due to its complex and multifaceted nature, developing effective treatments for epilepsy is still a major challenge. To deal with this complexity we introduce the concept of degeneracy to the field of epilepsy research: the ability of disparate elements to cause an analogous function or malfunction. Here, we review examples of epilepsy-related degeneracy at multiple levels of brain organisation, ranging from the cellular to the network and systems level. Based on these insights, we outline new multiscale and population modelling approaches to disentangle the complex web of interactions underlying epilepsy and to design personalised multitarget therapies.

Neuroscience: Seq-ing maps in the olfactory cortex

Many cortical brain regions are spatially organized to optimize sensory representation. Such topographic maps have so far been elusive in the olfactory cortex. A high-throughput tracing study reveals that the neural circuits connecting olfactory regions are indeed topographically organized.

Mapping thalamic innervation to individual L2/3 pyramidal neurons and modeling their 'readout' of visual input

The thalamus is the main gateway for sensory information from the periphery to the mammalian cerebral cortex. A major conundrum has been the discrepancy between the thalamus's central role as the primary feedforward projection system into the neocortex and the sparseness of thalamocortical synapses. Here we use new methods, combining genetic tools and scalable tissue expansion microscopy for whole-cell synaptic mapping, revealing the number, density and size of thalamic versus cortical excitatory synapses onto individual layer 2/3 (L2/3) pyramidal cells (PCs) of the mouse primary visual cortex. We find that thalamic inputs are not only sparse, but remarkably heterogeneous in number and density across individual dendrites and neurons. Most surprising, despite their sparseness, thalamic synapses onto L2/3 PCs are smaller than their cortical counterparts. Incorporating these findings into fine-scale, anatomically faithful biophysical models of L2/3 PCs reveals how individual neurons with sparse and weak thalamocortical synapses, embedded in small heterogeneous neuronal ensembles, may reliably 'read out' visually driven thalamic input.

Effects of discontinuous blue light stimulation on the electrophysiological properties of neurons lacking opsin expression in vitro: Implications for optogenetic experiments

Neuronal sensitivity to light stimulation can be a significant confounding factor for assays that use light to study neuronal processes, such as optogenetics and fluorescent imaging. While continuous one-photon (1P) blue light stimulation has been shown to be responsible for a decrease in firing activity in several neuronal subtypes, discontinuous 1P blue light stimulation commonly used in optogenetic experiments is supposed to have a negligible action. In the present report, we tested experimentally this theoretical prediction by assessing the effects produced by the most commonly used patterns of discontinuous 1P light stimulation on several electrophysiological parameters in brain slices. We found that, compared with continuous stimulation, the artefactual effect of light is reduced when discontinuous stimulation is used, especially when the duty cycle and light power are low.

Model simulations unveil the structure-function-dynamics relationship of the cerebellar cortical microcircuit

The cerebellar network is renowned for its regular architecture that has inspired foundational computational theories. However, the relationship between circuit structure, function and dynamics remains elusive. To tackle the issue, we developed an advanced computational modeling framework that allows us to reconstruct and simulate the structure and function of the mouse cerebellar cortex using morphologically realistic multi-compartmental neuron models. The cerebellar connectome is generated through appropriate connection rules, unifying a collection of scattered experimental data into a coherent construct and providing a new model-based ground-truth about circuit organization. Naturalistic background and sensory-burst stimulation are used for functional validation against recordings in vivo, monitoring the impact of cellular mechanisms on signal propagation, inhibitory control, and long-term synaptic plasticity. Our simulations show how mossy fibers entrain the local neuronal microcircuit, boosting the formation of columns of activity travelling from the granular to the molecular layer providing a new resource for the investigation of local microcircuit computation and of the neural correlates of behavior.

How clock heterogeneity affects synchronization and can enhance stability

The production process of integrated electronic circuitry inherently leads to large heterogeneities on the component level. For electronic clock networks this implies detuned intrinsic frequencies and differences in coupling strength and the characteristic time delays associated with signal transmission, processing, and feedback. Using a phase-model description, we study the effects of such component heterogeneity on the dynamical properties of synchronization in networks of mutually delay-coupled Kuramoto oscillators. We test the theory against experimental results and circuit-level simulations in a prototype system of mutually delay-coupled electronic clocks, so-called phase-locked loops. Interestingly, our results show that heterogeneity in the system can actually enhance the stability of synchronized states. That means that beyond the optimizations that can be achieved by tuning homogeneous coupling strengths, time delays, and loop-filter cut-off frequencies, heterogeneities in these system parameters enable much better optimization of perturbation decay rates, stabilization of synchronous states, and tuning of phase differences between the clocks. Our theory enables the design of custom-fit synchronization layers according to the specific requirements and properties of electronic systems, such as operational frequencies, phase relations, and, e.g., transmission delays. These results are not restricted to electronic systems, because signal transmission, processing, and feedback delays are common to networks of spatially distributed and coupled autonomous oscillators.

Heterogeneity-induced synchronization in delay-coupled electronic oscillators

We study synchronization in networks of delay-coupled electronic oscillators, so-called phase-locked loops (PLLs). Using a phase-model description, we study the collective dynamics of mutually coupled PLLs and report the phenomenon of heterogeneity-induced synchronization. This phenomenon refers to the observation that heterogeneity in the system's parameters can induce synchronization by stabilizing the states which are unstable without such heterogeneity. In systems where component heterogeneity can be tuned and controlled, we show how the complex collective self-organized dynamics can be guided towards synchronized states with specific operational frequencies and phase relations. This is of importance for the technical applicability of self-organized dynamics. In electrical engineering, for example, where components can be strongly heterogeneous, our theoretical framework can inform the design process for networks of spatially distributed PLLs. The results presented here are also useful in understanding the collective dynamics in ensembles of phase oscillators with time-delayed interactions, inertia, and heterogeneity.

From synapses to circuits and back: Bridging levels of understanding in animal models of Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by behavioural changes that include memory loss and cognitive decline and is associated with the appearance of amyloid-β plaques and neurofibrillary tangles throughout the brain. Although aspects of the disease percolate across multiple levels of neuronal organization, from the cellular to the behavioural, it is increasingly clear that circuits are a critical junction between the cellular pathology and the behavioural phenotypes that bookend these levels of analyses. In this review, we discuss critical aspects of neural circuit research, beginning with synapses and progressing to network activity and how they influence our understanding of disease processed in AD.

Enhanced recruitment of glutamate receptors underlies excitotoxicity of mitral cells in acute hyperammonemia

Hepatic encephalopathy (HE)–a major complication of liver disease–has been found to increase the risk of olfactory dysfunction, which may be attributed to elevated levels of ammonia/ammonium in the blood and cerebrospinal fluid. However, the cellular mechanisms underlying hyperammonemia-induced olfactory dysfunction remain unclear. By performing patch-clamp recordings of mitral cells (MCs) in the mouse olfactory bulb (OB), we found that 3 mM ammonium (NH4+) increased the spontaneous firing frequency and attenuated the amplitude, but synaptic blockers could prevent the changes, suggesting the important role of glutamate receptors in NH4+-induced hyperexcitability of MCs. We also found NH4+ reduced the currents of voltage-gated K+ channel (Kv), which may lead to the attenuation of spontaneous firing amplitude by NH4+. Further studies demonstrated NH4+ enhanced the amplitude and integral area of long-lasting spontaneous excitatory post-synaptic currents (sEPSCs) in acute OB slices. This enhancement of excitatory neurotransmission in MCs occurred independently of pre-synaptic glutamate release and re-uptake, and was prevented by the exocytosis inhibitor TAT-NSF700. In addition, an NH4+-induced increasement in expression of NR1 and GluR1 was detected on cytoplasmic membrane, indicating that increased trafficking of glutamate receptors on membrane surface in MCs is the core mechanism. Moreover, NH4+-induced enhanced activity of glutamate receptors in acute OB slices caused cell death, which was prevented by antagonizing glutamate receptors or chelating intracellular calcium levels. Our study demonstrates that the enhancement of the activity and recruitment of glutamate receptor directly induces neuronal excitotoxicity, and contributes to the vulnerability of OB to acute hyperammonemia, thus providing a potential pathological mechanism of olfactory defects in patients with hyperammonemia and HE.

Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding

Synchronization of neuronal discharges on the millisecond scale has long been recognized as a prevalent and functionally important attribute of neural activity. In this article, I review classical concepts and corresponding evidence of the mechanisms that govern the synchronization of distributed discharges in cortical networks and relate those mechanisms to their possible roles in coding and cognitive functions. To accommodate the need for a selective, directed synchronization of cells, I propose that synchronous firing of distributed neurons is a natural consequence of spike-timing-dependent plasticity (STDP) that associates cells repetitively receiving temporally coherent input: the “synchrony through synaptic plasticity” hypothesis. Neurons that are excited by a repeated sequence of synaptic inputs may learn to selectively respond to the onset of this sequence through synaptic plasticity. Multiple neurons receiving coherent input could thus actively synchronize their firing by learning to selectively respond at corresponding temporal positions. The hypothesis makes several predictions: first, the position of the cells in the network, as well as the source of their input signals, would be irrelevant as long as their input signals arrive simultaneously; second, repeating discharge patterns should get compressed until all or some part of the signals are synchronized; and third, this compression should be accompanied by a sparsening of signals. In this way, selective groups of cells could emerge that would respond to some recurring event with synchronous firing. Such a learned response pattern could further be modulated by synchronous network oscillations that provide a dynamic, flexible context for the synaptic integration of distributed signals. I conclude by suggesting experimental approaches to further test this new hypothesis.

Physiological noise facilitates multiplexed coding of vibrotactile-like signals in somatosensory cortex

Neurons can use different aspects of their spiking to simultaneously represent (multiplex) different features of a stimulus. For example, some pyramidal neurons in primary somatosensory cortex (S1) use the rate and timing of their spikes to, respectively, encode the intensity and frequency of vibrotactile stimuli. Doing so has several requirements. Because they fire at low rates, pyramidal neurons cannot entrain 1:1 with high-frequency (100 to 600 Hz) inputs and, instead, must skip (i.e., not respond to) some stimulus cycles. The proportion of skipped cycles must vary inversely with stimulus intensity for firing rate to encode stimulus intensity. Spikes must phase-lock to the stimulus for spike times (intervals) to encode stimulus frequency, but, in addition, skipping must occur irregularly to avoid aliasing. Using simulations and in vitro experiments in which mouse S1 pyramidal neurons were stimulated with inputs emulating those induced by vibrotactile stimuli, we show that fewer cycles are skipped as stimulus intensity increases, as required for rate coding, and that intrinsic or synaptic noise can induce irregular skipping without disrupting phase locking, as required for temporal coding. This occurs because noise can modulate the reliability without disrupting the precision of spikes evoked by small-amplitude, fast-onset signals. Specifically, in the fluctuation-driven regime associated with sparse spiking, rate and temporal coding are both paradoxically improved by the strong synaptic noise characteristic of the intact cortex. Our results demonstrate that multiplexed coding by S1 pyramidal neurons is not only feasible under in vivo conditions, but that background synaptic noise is actually beneficial.

Voltage-Gated Ion Channels and the Variability in Information Transfer

The prerequisites for neurons to function within a circuit and be able to contain and transfer information efficiently and reliably are that they need to be homeostatically stable and fire within a reasonable range, characteristics that are governed, among others, by voltage-gated ion channels (VGICs). Nonetheless, neurons entail large variability in the expression levels of VGICs and their corresponding intrinsic properties, but the role of this variability in information transfer is not fully known. In this study, we aimed to investigate how this variability of VGICs affects information transfer. For this, we used a previously derived population of neuronal model neurons, each with the variable expression of five types of VGICs, fast Na+, delayed rectifier K+, A-type K+, T-type Ca++, and HCN channels. These analyses showed that the model neurons displayed variability in mutual information transfer, measured as the capability of neurons to successfully encode incoming synaptic information in output firing frequencies. Likewise, variability in the expression of VGICs caused variability in EPSPs and IPSPs amplitudes, reflected in the variability of output firing frequencies. Finally, using the virtual knockout methodology, we show that among the ion channels tested, the A-type K+ channel is the major regulator of information processing and transfer.

Molecular Mechanisms of Epilepsy: The Role of the Chloride Transporter KCC2

Epilepsy is a neurological disease characterized by abnormal or synchronous brain activity causing seizures, which may produce convulsions, minor physical signs, or a combination of symptoms. These disorders affect approximately 65 million people worldwide, from all ages and genders. Seizures apart, epileptic patients present a high risk to develop neuropsychological comorbidities such as cognitive deficits, emotional disturbance, and psychiatric disorders, which severely impair quality of life. Currently, the treatment for epilepsy includes the administration of drugs or surgery, but about 30% of the patients treated with antiepileptic drugs develop time-dependent pharmacoresistence. Therefore, further investigation about epilepsy and its causes is needed to find new pharmacological targets and innovative therapeutic strategies. Pharmacoresistance is associated to changes in neuronal plasticity and alterations of GABA_A receptor-mediated neurotransmission. The downregulation of GABA inhibitory activity may arise from a positive shift in GABA_A receptor reversal potential, due to an alteration in chloride homeostasis. In this paper, we review the contribution of K⁺-Cl^--cotransporter (KCC2) to the alterations in the Cl^- gradient observed in epileptic condition, and how these alterations are coupled to the increase in the excitability.

Artificial Visual Information Produced by Retinal Prostheses

Numerous retinal prosthetic systems have demonstrated somewhat useful vision can be restored to individuals who had lost their sight due to outer retinal degenerative diseases. Earlier prosthetic studies have mostly focused on the confinement of electrical stimulation for improved spatial resolution and/or the biased stimulation of specific retinal ganglion cell (RGC) types for selective activation of retinal ON/OFF pathway for enhanced visual percepts. To better replicate normal vision, it would be also crucial to consider information transmission by spiking activities arising in the RGC population since an incredible amount of visual information is transferred from the eye to the brain. In previous studies, however, it has not been well explored how much artificial visual information is created in response to electrical stimuli delivered by microelectrodes. In the present work, we discuss the importance of the neural information for high-quality artificial vision. First, we summarize the previous literatures which have computed information transmission rates from spiking activities of RGCs in response to visual stimuli. Second, we exemplify a couple of studies which computed the neural information from electrically evoked responses. Third, we briefly introduce how information rates can be computed in the representative two ways – direct method and reconstruction method. Fourth, we introduce in silico approaches modeling artificial retinal neural networks to explore the relationship between amount of information and the spiking patterns. Lastly, we conclude our review with clinical implications to emphasize the necessity of considering visual information transmission for further improvement of retinal prosthetics.

Loss of neuronal heterogeneity in epileptogenic human tissue impairs network resilience to sudden changes in synchrony

A myriad of pathological changes associated with epilepsy can be recast as decreases in cell and circuit heterogeneity. We thus propose recontextualizing epileptogenesis as a process where reduction in cellular heterogeneity, in part, renders neural circuits less resilient to seizure. By comparing patch clamp recordings from human layer 5 (L5) cortical pyramidal neurons from epileptogenic and non-epileptogenic tissue, we demonstrate significantly decreased biophysical heterogeneity in seizure-generating areas. Implemented computationally, this renders model neural circuits prone to sudden transitions into synchronous states with increased firing activity, paralleling ictogenesis. This computational work also explains the surprising finding of significantly decreased excitability in the population-activation functions of neurons from epileptogenic tissue. Finally, mathematical analyses reveal a bifurcation structure arising only with low heterogeneity and associated with seizure-like dynamics. Taken together, this work provides experimental, computational, and mathematical support for the theory that ictogenic dynamics accompany a reduction in biophysical heterogeneity.

Brain Injury Effects on Neuronal Activation and Synaptic Transmission in the Basolateral Amygdala of Adult Male and Female Wistar Rats

Traumatic brain injury (TBI) is defined as brain damage produced by an external mechanical force that leads to behavioral, cognitive, and psychiatric sequelae. The basolateral amygdala (BLA) is involved in emotional regulation, and its function and morphology are altered following TBI. Little is known about potential sex-specific effects of TBI on BLA neuronal function, but it is critical for the field to identify potential sex differences in TBI effects on brain and behavior. Here, we hypothesized that TBI would produce sex-specific acute (1 h) effects on BLA neuronal activation, excitability, and synaptic transmission in adult male and female rats. Forty-nine Wistar rats (n = 23 males and 26 females) were randomized to TBI (using lateral fluid percussion) or Sham groups in two separate studies. Study 1 used in situ hybridization (i.e., RNAscope) to measure BLA expression of c-fos (a marker of cell activation), vGlut, and vGat (markers of glutamatergic and GABAergic neurons, respectively) messenger RNA (mRNA). Study 2 used slice electrophysiology to measure intrinsic excitability and excitatory/inhibitory synaptic transmission in putative pyramidal neurons in the BLA. Physiological measures of injury severity were collected from all animals. Our results show that females exhibit increased apnea duration and reduced respiratory rate post-TBI relative to males. In male and female rats, TBI increased c-fos expression in BLA glutamatergic cells but not in BLA GABAergic cells, and TBI increased firing rate in BLA pyramidal neurons. Further, TBI increased spontaneous excitatory and inhibitory postsynaptic current (sEPSC and sIPSC) amplitude in BLA neurons of females relative to all other groups. TBI increased sEPSC frequency in BLA neurons of females relative to males but did not alter sIPSC frequency. In summary, lateral fluid percussion produced different physiological responses in male and female rats, as well as sex-specific alterations in BLA neuronal activation, excitability, and synaptic transmission 1 h after injury.

Top-down feedback enables flexible coding strategies in the olfactory cortex

In chemical sensation, multiple models have been proposed to explain how odors are represented in the olfactory cortex. One hypothesis is that the combinatorial identity of active neurons within sniff-related time windows is critical, whereas another model proposes that it is the temporal structure of neural activity that is essential for encoding odor information. We find that top-down feedback to the main olfactory bulb dictates the information transmitted to the piriform cortex and switches between these coding strategies. Using a detailed network model, we demonstrate that feedback control of inhibition influences the excitation-inhibition balance in mitral cells, restructuring the dynamics of piriform cortical cells. This results in performance improvement in odor discrimination tasks. These findings present a framework for early olfactory computation, where top-down feedback to the bulb flexibly shapes the temporal structure of neural activity in the piriform cortex, allowing the early olfactory system to dynamically switch between two distinct coding models.

State-dependent representations of mixtures by the olfactory bulb

Sensory systems are often tasked to analyse complex signals from the environment, separating relevant from irrelevant parts. This process of decomposing signals is challenging when a mixture of signals does not equal the sum of its parts, leading to an unpredictable corruption of signal patterns. In olfaction, nonlinear summation is prevalent at various stages of sensory processing. Here, we investigate how the olfactory system deals with binary mixtures of odours under different brain states by two-photon imaging of olfactory bulb (OB) output neurons. Unlike previous studies using anaesthetised animals, we found that mixture summation is more linear in the early phase of evoked responses in awake, head-fixed mice performing an odour detection task, due to dampened responses. Despite smaller and more variable responses, decoding analyses indicated that the data from behaving mice was well discriminable. Curiously, the time course of decoding accuracy did not correlate strictly with the linearity of summation. Further, a comparison with naïve mice indicated that learning to accurately perform the mixture detection task is not accompanied by more linear mixture summation. Finally, using a simulation, we demonstrate that, while saturating sublinearity tends to degrade the discriminability, the extent of the impairment may depend on other factors, including pattern decorrelation. Altogether, our results demonstrate that the mixture representation in the primary olfactory area is state-dependent, but the analytical perception may not strictly correlate with linearity in summation.

Afterhyperpolarization Promotes the Firing of Mitral Cells through a Voltage-Dependent Modification of Action Potential Threshold

In the olfactory bulb, mitral cells (MCs) display a spontaneous firing that is characterized by bursts of action potentials (APs) intermixed with silent periods. Intraburst firing frequency and duration are heterogeneous among MCs and increase with membrane depolarization. By using patch-clamp recording on rat slices, we dissected out the intrinsic properties responsible for this bursting activity. We showed that the threshold of AP generation dynamically changes as a function of the preceding trajectory of the membrane potential. In fact, the AP threshold became more negative when the membrane was hyperpolarized and had a recovery rate inversely proportional to the membrane repolarization rate. Such variations appeared to be produced by changes in the inactivation state of voltage-dependent Na⁺ channels. Thus, AP initiation was favored by hyperpolarizing events, such as negative membrane oscillations or inhibitory synaptic input. After the first AP, the following fast afterhyperpolarization (AHP) brought the threshold to more negative values and then promoted the emission of the following AP. This phenomenon was repeated for each AP of the burst making the fast AHP a regenerative mechanism that sustained the firing, AHP with larger amplitudes and faster repolarizations being associated with larger and higher-frequency bursts. Burst termination was found to be because of the development of a slow repolarization component of the AHP (slow AHP). Overall, the AHP characteristics appeared as a major determinant of the bursting properties.

Synchronous inhibitory pathways create both efficiency and diversity in the retina

Complex connections in neural circuits make it difficult to quantitatively assign even the most basic neural computations to the actions of specific neurons. Retinal ganglion cells are most sensitive to changes in intensity across space and over time. This property, caused by a region known as the receptive field surround, improves information transmission about natural scenes. We dynamically manipulated individual interneurons to directly measure their effect on retinal receptive fields, finding that two inhibitory neuron types, horizontal cells and amacrine cells, synchronously create the same contribution to the receptive field surround at different spatial scales. By analyzing large populations of ganglion cells, we show that this arrangement increases diversity in retinal signaling while preserving maximal information transmission about natural scenes.

Sensory receptive fields combine features that originate in different neural pathways. Retinal ganglion cell receptive fields compute intensity changes across space and time using a peripheral region known as the surround, a property that improves information transmission about natural scenes. The visual features that construct this fundamental property have not been quantitatively assigned to specific interneurons. Here, we describe a generalizable approach using simultaneous intracellular and multielectrode recording to directly measure and manipulate the sensory feature conveyed by a neural pathway to a downstream neuron. By directly controlling the gain of individual interneurons in the circuit, we show that rather than transmitting different temporal features, inhibitory horizontal cells and linear amacrine cells synchronously create the linear surround at different spatial scales and that these two components fully account for the surround. By analyzing a large population of ganglion cells, we observe substantial diversity in the relative contribution of amacrine and horizontal cell visual features while still allowing individual cells to increase information transmission under the statistics of natural scenes. Established theories of efficient coding have shown that optimal information transmission under natural scenes allows a diverse set of receptive fields. Our results give a mechanism for this theory, showing how distinct neural pathways synthesize a sensory computation and how this architecture both generates computational diversity and achieves the objective of high information transmission.

Experience-dependent plasticity modulates ongoing activity in the antennal lobe and enhances odor representations

Ongoing neural activity has been observed across several brain regions and is thought to reflect the internal state of the brain. Yet, it is important to understand how ongoing neural activity interacts with sensory experience and shapes sensory representations. Here, we show that the projection neurons of the fruit fly antennal lobe exhibit spatiotemporally organized ongoing activity. After repeated exposure to odors, we observe a gradual and cumulative decrease in the amplitude and number of calcium events occurring in the absence of odor stimulation, as well as a reorganization of correlations between olfactory glomeruli. Accompanying these plastic changes, we find that repeated odor experience decreases trial-to-trial variability and enhances the specificity of odor representations. Our results reveal an odor-experience-dependent modulation of ongoing and sensory-evoked activity at peripheral levels of the fruit fly olfactory system.

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The fruit fly antennal lobe exhibits spatiotemporally organized ongoing activity

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Repeated odor experience decreases the amplitude and number of ongoing calcium events

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Odor experience enhances the robustness and the specificity of odor representations

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Representations of different odors become more dissimilar upon repeated exposure

Compensatory variability in network parameters enhances memory performance in the Drosophila mushroom body

Neural circuits use homeostatic compensation to achieve consistent behavior despite variability in underlying intrinsic and network parameters. However, it remains unclear how compensation regulates variability across a population of the same type of neurons within an individual and what computational benefits might result from such compensation. We address these questions in the Drosophila mushroom body, the fly's olfactory memory center. In a computational model, we show that under sparse coding conditions, memory performance is degraded when the mushroom body's principal neurons, Kenyon cells (KCs), vary realistically in key parameters governing their excitability. However, memory performance is rescued while maintaining realistic variability if parameters compensate for each other to equalize KC average activity. Such compensation can be achieved through both activity-dependent and activity-independent mechanisms. Finally, we show that correlations predicted by our model's compensatory mechanisms appear in the Drosophila hemibrain connectome. These findings reveal compensatory variability in the mushroom body and describe its computational benefits for associative memory.

Signal amplification enhanced by large phase disorder in coupled bistable units

We study the maximum response of network-coupled bistable units to subthreshold signals focusing on the effect of phase disorder. We find that for signals with large levels of phase disorder, the network exhibits an enhanced response for intermediate coupling strength, while generating a damped response for low levels of phase disorder. We observe that the large phase-disorder-enhanced response depends mainly on the signal intensity but not on the signal frequency or the network topology. We show that a zero average activity of the units caused by large phase disorder plays a key role in the enhancement of the maximum response. With a detailed analysis, we demonstrate that large phase disorder can suppress the synchronization of the units, leading to the observed resonancelike response. Finally, we examine the robustness of this phenomenon to the unit bistability, the initial phase distribution, and various signal waveform. Our result demonstrates a potential benefit of phase disorder on signal amplification in complex systems.

Neural heterogeneity promotes robust learning

The brain is a hugely diverse, heterogeneous structure. Whether or not heterogeneity at the neural level plays a functional role remains unclear, and has been relatively little explored in models which are often highly homogeneous. We compared the performance of spiking neural networks trained to carry out tasks of real-world difficulty, with varying degrees of heterogeneity, and found that heterogeneity substantially improved task performance. Learning with heterogeneity was more stable and robust, particularly for tasks with a rich temporal structure. In addition, the distribution of neuronal parameters in the trained networks is similar to those observed experimentally. We suggest that the heterogeneity observed in the brain may be more than just the byproduct of noisy processes, but rather may serve an active and important role in allowing animals to learn in changing environments.

Heterogeneous side effects of cortical inactivation in behaving animals

Cortical inactivation represents a key causal manipulation allowing the study of cortical circuits and their impact on behavior. A key assumption in inactivation studies is that the neurons in the target area become silent while the surrounding cortical tissue is only negligibly impacted. However, individual neurons are embedded in complex local circuits composed of excitatory and inhibitory cells with connections extending hundreds of microns. This raises the possibility that silencing one part of the network could induce complex, unpredictable activity changes in neurons outside the targeted inactivation zone. These off-target side effects can potentially complicate interpretations of inactivation manipulations, especially when they are related to changes in behavior. Here, we demonstrate that optogenetic inactivation of glutamatergic neurons in the superficial layers of monkey primary visual cortex (V1) induces robust suppression at the light-targeted site, but destabilizes stimulus responses in the neighboring, untargeted network. We identified four types of stimulus-evoked neuronal responses within a cortical column, ranging from full suppression to facilitation, and a mixture of both. Mixed responses were most prominent in middle and deep cortical layers. These results demonstrate that response modulation driven by lateral network connectivity is diversely implemented throughout a cortical column. Importantly, consistent behavioral changes induced by optogenetic inactivation were only achieved when cumulative network activity was homogeneously suppressed. Therefore, careful consideration of the full range of network changes outside the inactivated cortical region is required, as heterogeneous side effects can confound interpretation of inactivation experiments.

Optimal responsiveness and information flow in networks of heterogeneous neurons

Cerebral cortex is characterized by a strong neuron-to-neuron heterogeneity, but it is unclear what consequences this may have for cortical computations, while most computational models consider networks of identical units. Here, we study network models of spiking neurons endowed with heterogeneity, that we treat independently for excitatory and inhibitory neurons. We find that heterogeneous networks are generally more responsive, with an optimal responsiveness occurring for levels of heterogeneity found experimentally in different published datasets, for both excitatory and inhibitory neurons. To investigate the underlying mechanisms, we introduce a mean-field model of heterogeneous networks. This mean-field model captures optimal responsiveness and suggests that it is related to the stability of the spontaneous asynchronous state. The mean-field model also predicts that new dynamical states can emerge from heterogeneity, a prediction which is confirmed by network simulations. Finally we show that heterogeneous networks maximise the information flow in large-scale networks, through recurrent connections. We conclude that neuronal heterogeneity confers different responsiveness to neural networks, which should be taken into account to investigate their information processing capabilities.

Divergence in Population Coding for Space between Dorsal and Ventral CA1

Molecular, anatomic, and behavioral studies show that the hippocampus is structurally and functionally heterogeneous, with dorsal hippocampus implicated in mnemonic processes and spatial navigation and ventral hippocampus involved in affective processes. By performing electrophysiological recordings of large neuronal populations in dorsal and ventral CA1 in head-fixed mice navigating a virtual environment, we found that this diversity resulted in different strategies for population coding of space. Populations of neurons in dorsal CA1 showed more complex patterns of activity, which resulted in a higher dimensionality of neural representations that translated to more information being encoded, as compared ensembles in vCA1. Furthermore, a pairwise maximum entropy model was better at predicting the structure of these global patterns of activity in ventral CA1 as compared with dorsal CA1. Taken together, the different coding strategies we uncovered likely emerge from anatomic and physiological differences along the longitudinal axis of hippocampus and that may, in turn, underpin the divergent ethological roles of dorsal and ventral CA1.

Stimulus Driven Functional Transformations in the Early Olfactory System

Olfactory stimuli are encountered across a wide range of odor concentrations in natural environments. Defining the neural computations that support concentration invariant odor perception, odor discrimination, and odor-background segmentation across a wide range of stimulus intensities remains an open question in the field. In principle, adaptation could allow the olfactory system to adjust sensory representations to the current stimulus conditions, a well-known process in other sensory systems. However, surprisingly little is known about how adaptation changes olfactory representations and affects perception. Here we review the current understanding of how adaptation impacts processing in the first two stages of the vertebrate olfactory system, olfactory receptor neurons (ORNs), and mitral/tufted cells.

Molecular characterization of projection neuron subtypes in the mouse olfactory bulb

Projection neurons (PNs) in the mammalian olfactory bulb (OB) receive input from the nose and project to diverse cortical and subcortical areas. Morphological and physiological studies have highlighted functional heterogeneity, yet no molecular markers have been described that delineate PN subtypes. Here, we used viral injections into olfactory cortex and fluorescent nucleus sorting to enrich PNs for high-throughput single nucleus and bulk RNA deep sequencing. Transcriptome analysis and RNA in situ hybridization identified distinct mitral and tufted cell populations with characteristic transcription factor network topology, cell adhesion, and excitability-related gene expression. Finally, we describe a new computational approach for integrating bulk and snRNA-seq data and provide evidence that different mitral cell populations preferentially project to different target regions. Together, we have identified potential molecular and gene regulatory mechanisms underlying PN diversity and provide new molecular entry points into studying the diverse functional roles of mitral and tufted cell subtypes.

Physiological Diversity Influences Detection of Stimulus Envelope and Fine Structure in Neurons of the Medial Superior Olive

The neurons of the medial superior olive (MSO) of mammals extract azimuthal information from the delays between sounds reaching the two ears [interaural time differences (ITDs)]. Traditionally, all models of sound localization have assumed that MSO neurons represent a single population of cells with specialized and homogeneous intrinsic and synaptic properties that enable the detection of synaptic coincidence on a timescale of tens to hundreds of microseconds. Here, using patch-clamp recordings from large populations of anatomically labeled neurons in brainstem slices from male and female Mongolian gerbils (Meriones unguiculatus), we show that MSO neurons are far more physiologically diverse than previously appreciated, with properties that depend regionally on cell position along the topographic map of frequency. Despite exhibiting a similar morphology, neurons in the MSO exhibit subthreshold oscillations of differing magnitudes that drive action potentials at rates between 100 and 800 Hz. These oscillations are driven primarily by voltage-gated sodium channels and are distinct from resonant properties derived from other active membrane properties. We show that graded differences in these and other physiological properties across the MSO neuron population enable the MSO to duplex the encoding of ITD information in both fast, submillisecond time-varying signals as well as in slower envelopes.SIGNIFICANCE STATEMENT Neurons in the medial superior olive (MSO) encode sound localization cues by detecting microsecond differences in the arrival times of inputs from the left and right ears, and it has been assumed that this computation is made possible by highly stereotyped structural and physiological specializations. Here we report using a large (>400) sample size in which MSO neurons show a strikingly large continuum of functional properties despite exhibiting similar morphologies. We demonstrate that subthreshold oscillations mediated by voltage-gated Na+ channels play a key role in conferring graded differences in firing properties. This functional diversity likely confers capabilities of processing both fast, submillisecond-scale synaptic activity (acoustic "fine structure"), and slow-rising envelope information that is found in amplitude-modulated sounds and speech patterns.

Brain-environment alignment during movie watching predicts fluid intelligence and affective function in adulthood

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Functional brain connectivity (FC) patterns vary with changes in the environment.

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Adult FC variability is linked to age-specific network communication profiles.

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Across adulthood, the younger network interaction profile predicts higher fluid IQ.

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Yoked FC-concrete environmental changes predict poorer fluid IQ and anxiety.

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Brain areas linked to episodic memory underpin FC changes at multiple timescales.

BOLD fMRI studies have provided compelling evidence that the human brain demonstrates substantial moment-to-moment fluctuations in both activity and functional connectivity (FC) patterns. While the role of brain signal variability in fostering cognitive adaptation to ongoing environmental demands is well-documented, the relevance of moment-to-moment changes in FC patterns is still debated. Here, we adopt a graph theoretical approach in order to shed light on the cognitive-affective implications of FC variability and associated profiles of functional network communication in adulthood. Our goal is to identify brain communication pathways underlying FC reconfiguration at multiple timescales, thereby improving understanding of how faster perceptually bound versus slower conceptual processes shape neural tuning to the dynamics of the external world and, thus, indirectly, mold affective and cognitive responding to the environment. To this end, we used neuroimaging and behavioural data collected during movie watching by the Cambridge Center for Ageing and Neuroscience (N = 642, 326 women) and the Human Connectome Project (N = 176, 106 women). FC variability evoked by changes to both the concrete perceptual and the more abstract conceptual representation of an ongoing situation increased from young to older adulthood. However, coupling between variability in FC patterns and concrete environmental features was stronger at younger ages. FC variability (both moment-to-moment/concrete featural and abstract conceptual boundary-evoked) was associated with age-distinct profiles of network communication, specifically, greater functional integration of the default mode network in older adulthood, but greater informational flow across neural networks implicated in environmentally driven attention and control (cingulo-opercular, salience, ventral attention) in younger adulthood. Whole-brain communication pathways anchored in default mode regions relevant to episodic and semantic context creation (i.e., angular and middle temporal gyri) supported FC reconfiguration in response to changes in the conceptual representation of an ongoing situation (i.e., narrative event boundaries), as well as stronger coupling between moment-to-moment fluctuations in FC and concrete environmental features. Fluid intelligence/abstract reasoning was directly linked to levels of brain-environment alignment, but only indirectly associated with levels of FC variability. Specifically, stronger coupling between moment-to-moment FC variability and concrete environmental features predicted poorer fluid intelligence and greater affectively driven environmental vigilance. Complementarily, across the adult lifespan, higher fluid (but not crystallised) intelligence was related to stronger expression of the network communication profile underlying momentary and event boundary-based FC variability during youth. Our results indicate that the adaptiveness of dynamic FC reconfiguration during naturalistic information processing changes across the lifespan due to the associated network communication profiles. Moreover, our findings on brain-environment alignment complement the existing literature on the beneficial consequences of modulating brain signal variability in response to environmental complexity. Specifically, they imply that coupling between moment-to-moment FC variability and concrete environmental features may index a bias towards perceptually-bound, rather than conceptual processing, which hinders affective functioning and strategic cognitive engagement with the external environment.

Diversity amongst human cortical pyramidal neurons revealed via their sag currents and frequency preferences

In the human neocortex coherent interlaminar theta oscillations are driven by deep cortical layers, suggesting neurons in these layers exhibit distinct electrophysiological properties. To characterize this potential distinctiveness, we use in vitro whole-cell recordings from cortical layers 2 and 3 (L2&3), layer 3c (L3c) and layer 5 (L5) of the human cortex. Across all layers we observe notable heterogeneity, indicating human cortical pyramidal neurons are an electrophysiologically diverse population. L5 pyramidal cells are the most excitable of these neurons and exhibit the most prominent sag current (abolished by blockade of the hyperpolarization activated cation current, I_h). While subthreshold resonance is more common in L3c and L5, we rarely observe this resonance at frequencies greater than 2 Hz. However, the frequency dependent gain of L5 neurons reveals they are most adept at tracking both delta and theta frequency inputs, a unique feature that may indirectly be important for the generation of cortical theta oscillations.

Fast odour dynamics are encoded in the olfactory system and guide behaviour

Odours are transported in turbulent plumes, which result in rapid concentration fluctuations^1,2 that contain rich information about the olfactory scenery, such as the composition and location of an odour source^2-4. However, it is unclear whether the mammalian olfactory system can use the underlying temporal structure to extract information about the environment. Here we show that ten-millisecond odour pulse patterns produce distinct responses in olfactory receptor neurons. In operant conditioning experiments, mice discriminated temporal correlations of rapidly fluctuating odours at frequencies of up to 40 Hz. In imaging and electrophysiological recordings, such correlation information could be readily extracted from the activity of mitral and tufted cells-the output neurons of the olfactory bulb. Furthermore, temporal correlation of odour concentrations⁵ reliably predicted whether odorants emerged from the same or different sources in naturalistic environments with complex airflow. Experiments in which mice were trained on such tasks and probed using synthetic correlated stimuli at different frequencies suggest that mice can use the temporal structure of odours to extract information about space. Thus, the mammalian olfactory system has access to unexpectedly fast temporal features in odour stimuli. This endows animals with the capacity to overcome key behavioural challenges such as odour source separation⁵, figure-ground segregation⁶ and odour localization⁷ by extracting information about space from temporal odour dynamics.

Dynamics of Glutamatergic Drive Underlie Diverse Responses of Olfactory Bulb Outputs In Vivo

Mitral/tufted (MT) cells of the olfactory bulb (OB) show diverse temporal responses to odorant stimulation that are thought to encode odor information. Much of this diversity is thought to arise from inhibitory OB circuits, but the dynamics of excitatory input to MT cells, which is driven in a feedforward manner by sensory afferents, may also be important. To examine the contribution of excitatory input dynamics to generating temporal diversity in MT cells, we imaged glutamate signaling onto MT cell dendrites in anesthetized and awake mice. We found surprising diversity in the temporal dynamics of these signals. Inhalation-linked glutamate transients were variable in onset latency and duration, and in awake mice the degree of coupling to inhalation varied substantially with odorant identity and concentration. Successive inhalations of odorant produced nonlinear changes in glutamate signaling that included facilitating, adapting and suppressive responses and which varied with odorant identity and concentration. Dual-color imaging of glutamate and calcium signals from MT cells in the same glomerulus revealed highly correlated presynaptic and postsynaptic signals across these different response types. Suppressive calcium responses in MT cells were nearly always accompanied by suppression in the glutamate signal, providing little evidence for MT cell suppression by lateral or feedforward inhibition. These results indicate a high degree of diversity in the dynamics of excitatory input to MT cells, and suggest that these dynamics may account for much of the diversity in MT cell responses that underlies OB odor representations.

Efficient and robust coding in heterogeneous recurrent networks

Cortical networks show a large heterogeneity of neuronal properties. However, traditional coding models have focused on homogeneous populations of excitatory and inhibitory neurons. Here, we analytically derive a class of recurrent networks of spiking neurons that close to optimally track a continuously varying input online, based on two assumptions: 1) every spike is decoded linearly and 2) the network aims to reduce the mean-squared error between the input and the estimate. From this we derive a class of predictive coding networks, that unifies encoding and decoding and in which we can investigate the difference between homogeneous networks and heterogeneous networks, in which each neurons represents different features and has different spike-generating properties. We find that in this framework, 'type 1' and 'type 2' neurons arise naturally and networks consisting of a heterogeneous population of different neuron types are both more efficient and more robust against correlated noise. We make two experimental predictions: 1) we predict that integrators show strong correlations with other integrators and resonators are correlated with resonators, whereas the correlations are much weaker between neurons with different coding properties and 2) that 'type 2' neurons are more coherent with the overall network activity than 'type 1' neurons.

Neuronal heterogeneity modulates phase synchronization between unidirectionally coupled populations with excitation-inhibition balance

Several experiments and models have highlighted the importance of neuronal heterogeneity in brain dynamics and function. However, how such a cell-to-cell diversity can affect cortical computation, synchronization, and neuronal communication is still under debate. Previous studies have focused on the effect of neuronal heterogeneity in one neuronal population. Here we are specifically interested in the effect of neuronal variability on the phase relations between two populations, which can be related to different cortical communication hypotheses. It has been recently shown that two spiking neuron populations unidirectionally connected in a sender-receiver configuration can exhibit anticipated synchronization (AS), which is characterized by a negative phase lag. This phenomenon has been reported in electrophysiological data of nonhuman primates and human EEG during a visual discrimination cognitive task. In experiments, the unidirectional coupling could be accessed by Granger causality and can be accompanied by either positive or negative phase difference between cortical areas. Here we propose a model of two coupled populations in which the neuronal heterogeneity can determine the dynamical relation between the sender and the receiver and can reproduce phase relations reported in experiments. Depending on the distribution of parameters characterizing the neuronal firing patterns, the system can exhibit both AS and the usual delayed synchronization regime (DS, with positive phase) as well as a zero-lag synchronization regime and phase bistability between AS and DS. Furthermore, we show that our network can present diversity in their phase relations maintaining the excitation-inhibition balance.