Population Coding Research Articles

Chemogenetic Modulation and Single-Photon Calcium Imaging in Anterior Cingulate Cortex Reveal a Mechanism for Effort-Based Decisions.

The ACC is implicated in effort exertion and choices based on effort cost, but it is still unclear how it mediates this cost-benefit evaluation. Here, male rats were trained to exert effort for a high-value reward (sucrose pellets) in a progressive ratio lever-pressing task. Trained rats were then tested in two conditions: a no-choice condition where lever-pressing for sucrose was the only available food option, and a choice condition where a low-value reward (lab chow) was freely available as an alternative to pressing for sucrose. Disruption of ACC, via either chemogenetic inhibition or excitation, reduced lever-pressing in the choice, but not in the no-choice, condition. We next looked for value coding cells in ACC during effortful behavior and reward consumption phases during choice and no-choice conditions. For this, we used in vivo miniaturized fluorescence microscopy to reliably track responses of the same cells and compare how ACC neurons respond during the same effortful behavior where there was a choice versus when there was no-choice. We found that lever-press and sucrose-evoked responses were significantly weaker during choice compared with no-choice sessions, which may have rendered them more susceptible to chemogenetic disruption. Together, findings from our interference experiments and neural recordings suggest that a mechanism by which ACC mediates effortful decisions is in the discrimination of the utility of available options. ACC regulates these choices by providing a stable population code for the relative value of different options.SIGNIFICANCE STATEMENT The ACC is implicated in effort-based decision-making. Here, we used chemogenetics and in vivo calcium imaging to explore its mechanism. Rats were trained to lever press for a high-value reward and tested in two conditions: a no-choice condition where lever-pressing for the high-value reward was the only option, and a choice condition where a low-value reward was also available. Inhibition or excitation of ACC reduced effort toward the high-value option, but only in the choice condition. Neural responses in ACC were weaker in the choice compared with the no-choice condition. A mechanism by which ACC regulates effortful decisions is in providing a stable population code for the discrimination of the utility of available options.

A Comparison of Rotating and Binary Stellar Evolution Models: Effects on Massive Star Populations

Abstract Both rotation and interactions with binary companions can significantly affect massive star evolution, altering interior and surface abundances, mass loss rates and mechanisms, observed temperatures and luminosities, and their ultimate core-collapse fates. The Geneva and the Binary Population and Spectral Synthesis (BPASS) stellar evolution codes include detailed treatments of the effects of rotation and binary evolution, respectively, and can illustrate the impact of these phenomena on massive stars and stellar populations. However, a direct comparison of these two widely used codes is vital if we hope to use their predictions for interpreting observations. In particular, rotating and binary models will predict different young stellar populations, impacting the outputs of stellar population synthesis (SPS) and the resulting interpretation of large samples of massive stars based on commonly used tools such as star count ratios. Here we compare the Geneva and BPASS evolutionary models, using an interpolated SPS scheme introduced in our previous work and a novel Bayesian framework to present the first in-depth direct comparison of massive stellar populations produced from single, rotating, and binary nonrotating evolution models. We calculate both models’ predicted values of star count ratios and compare the results to observations of massive stars in Westerlund 1, h + χ Persei, and both Magellanic Clouds. We also consider the limitations of both the observations and the models, and how to quantitatively include observational completeness limits in SPS models. We demonstrate that the methods presented here, when combined with robust stellar evolutionary models, offer a potential means of estimating the physical properties of massive stars in large stellar populations.

GCaMP as an indirect measure of electrical activity in rat trigeminal ganglion neurons

While debate continues over whether somatosensory information is transmitted via labeled line, population coding, frequency coding, or some combination therein, researchers have begun to address this question at the level of the primary afferent by using optical approaches that enable the assessment of neural activity in hundreds to even thousands of neurons simultaneously. However, with limited availability of tools to optically assess electrical activity in large populations of neurons, researchers have turned to genetically encoded Ca2+ indicators (GECIs) including GCaMP to enable the detection of increases in cytosolic Ca2+ concentrations as a correlate for neuronal activity. One of the most widely used GECIs is GCaMP6, which is available in three different versions tuned for sensitivity (GCaMP6s), speed (GCaMP6f), or a balance of the two (GCaMP6m). In order to determine if these issues were unique to GCaMP6 itself, or if they were inherent to more than one generation of GCaMP, we also characterized jGCaMP7. In the present study, we sought to determine the utility of the three GCaMP6 isoforms to detect changes in activity in primary afferents at frequencies ranging from 0.1–30 Hz. Given the heterogeneity of sensory neurons, we also compared the performance of each GCaMP6 isoform in subpopulations of neurons defined by properties used to identify putative nociceptive afferents: cell body size, isolectin B4 (IB4) binding, and capsaicin sensitivity. Finally, we compared results generated with GCaMP6 with that generated from neurons expressing the next generation of GCaMP, jGCaMP7s and jGCaMP7f. A viral approach, with AAV9-CAG-GCaMP6s/m/f, was used to drive GECI expression in acutely dissociated rat trigeminal ganglion (TG) neurons, and neural activity was driven by electrical field stimulation. Infection efficiency with the AAV serotype was high >95 %, and the impact of GCaMP6 expression in TG neurons over the period of study (<10 days) on the regulation of intracellular Ca2+, as assessed with fura-2, was minimal. Having confirmed that the field stimulation evoked Ca2+ transients were dependent on Ca2+ influx secondary to the activation of action potentials and voltage-gated Ca2+ channels, we also confirmed that the signal-to-noise ratio for each of the isoforms was excellent, enabling detection of a single spike in>90% of neurons. However, the utility of the GCaMP6 isoforms to enable an assessment of the firing frequency let alone changes in firing frequency of each neuron was relatively limited and isoform specific: GCaMP6s and 6m had the lowest resolution, enabling detection of spikes at 3 Hz in 15% and 32% of neurons respectively, but it was possible to resolve discrete single spikes up to 10 Hz in 36% of GCaMP6f neurons. Unfortunately, using other parameters of the Ca2+ transient, such as magnitude of the transient or the rate of rise, did not improve the range over which these indicators could be used to assess changes in spike number or firing frequency. Furthermore, in the presence of ongoing neural activity, it was even more difficult to detect a change in firing frequency. The frequency response relationship for the increase in Ca2+ was highly heterogeneous among sensory neurons and was influenced by both the GCaMP6 isoform used to assess it, the timing between the delivery of stimulation trains (inter-burst interval), and afferent subpopulation. Notably, the same deficiencies were observed with jGCaMP7s and 7f in resolving the degree of activity as were present for the GCaMP6 isoforms. Together, these data suggest that while both GCaMP6 and jGCaMP7 are potentially useful tools in sensory neurons to determine the presence or absence of neural activity, the ability to discriminate changes in firing frequency ≥ 3 Hz is extremely limited. As a result, GECIs should probably not be used in sensory neurons to assess changes in activity within or between subpopulations of neurons.

Role of synaptic nonlinearity in persistent firing rate shifts caused by external periodic forcing

Information storage and processing in the brain largely relies on the neural population coding principle. In this framework, information is reflected in the population firing rate that reflects asynchronous irregular spiking of its constituent neurons. Periodic modulations of neural activity can lead to neural activity oscillations. Data indicate that such oscillations are ubiquitous in brain activity and are modulated, in frequency and amplitude, in a functionally meaningful manner. The relationship between oscillations and the population rate code remains an open issue. While ample works show how changes in the mean firing rate may alter neural oscillations, the reverse connection is unclear. One notable possibility is that oscillatory activity impinging on a neural population modulates its mean firing rate, thereby impacting information processing. We suggest that such modulation requires nonlinearities and propose nonlinear excitatory coupling via slow N-methyl-D-aspartate (NMDA) receptors as the prevalent mechanism. The aim of our paper is to theoretically explore to what extent the NMDA-related mechanism could account for oscillation-induced mean firing rate changes. We consider a mean-field model of a neural circuit containing an excitatory and an inhibitory population with linear transfer functions. Along with NMDA excitation, the model included fast recurrent excitatory and inhibitory connectivity. To explicitly study the effects of impinging oscillation on the rate dynamics, we subjected the circuit to a sinusoidal input signal imitating an input from distant brain regions or from a larger network into which the circuit is embedded. Using time-scale separation and time-averaging techniques, we developed a geometric method to determine the oscillation-induced mean firing rate shifts and validated it by numeric simulations of the model. Our results indicate that a large-amplitude stable firing rate shift requires nonlinear NMDA synapses on both the excitatory and the inhibitory populations. Our results delineate specific neural synaptic properties that enable neural oscillations to act as flexible modulators of the population rate code.

Heterogeneous Synaptic Weighting Improves Neural Coding in the Presence of Common Noise.

Simultaneous recordings from the cortex have revealed that neural activity is highly variable and that some variability is shared across neurons in a population. Further experimental work has demonstrated that the shared component of a neuronal population's variability is typically comparable to or larger than its private component. Meanwhile, an abundance of theoretical work has assessed the impact that shared variability has on a population code. For example, shared input noise is understood to have a detrimental impact on a neural population's coding fidelity. However, other contributions to variability, such as common noise, can also play a role in shaping correlated variability. We present a network of linear-nonlinear neurons in which we introduce a common noise input to model-for instance, variability resulting from upstream action potentials that are irrelevant to the task at hand. We show that by applying a heterogeneous set of synaptic weights to the neural inputs carrying the common noise, the network can improve its coding ability as measured by both Fisher information and Shannon mutual information, even in cases where this results in amplification of the common noise. With a broad and heterogeneous distribution of synaptic weights, a population of neurons can remove the harmful effects imposed by afferents that are uninformative about a stimulus. We demonstrate that some nonlinear networks benefit from weight diversification up to a certain population size, above which the drawbacks from amplified noise dominate over the benefits of diversification. We further characterize these benefits in terms of the relative strength of shared and private variability sources. Finally, we studied the asymptotic behavior of the mutual information and Fisher information analytically in our various networks as a function of population size. We find some surprising qualitative changes in the asymptotic behavior as we make seemingly minor changes in the synaptic weight distributions.

Stimulus-choice (mis)alignment in primate area MT.

For stimuli near perceptual threshold, the trial-by-trial activity of single neurons in many sensory areas is correlated with the animal's perceptual report. This phenomenon has often been attributed to feedforward readout of the neural activity by the downstream decision-making circuits. The interpretation of choice-correlated activity is quite ambiguous, but its meaning can be better understood in the light of population-wide correlations among sensory neurons. Using a statistical nonlinear dimensionality reduction technique on single-trial ensemble recordings from the middle temporal (MT) area during perceptual-decision-making, we extracted low-dimensional latent factors that captured the population-wide fluctuations. We dissected the particular contributions of sensory-driven versus choice-correlated activity in the low-dimensional population code. We found that the latent factors strongly encoded the direction of the stimulus in single dimension with a temporal signature similar to that of single MT neurons. If the downstream circuit were optimally utilizing this information, choice-correlated signals should be aligned with this stimulus encoding dimension. Surprisingly, we found that a large component of the choice information resides in the subspace orthogonal to the stimulus representation inconsistent with the optimal readout view. This misaligned choice information allows the feedforward sensory information to coexist with the decision-making process. The time course of these signals suggest that this misaligned contribution likely is feedback from the downstream areas. We hypothesize that this non-corrupting choice-correlated feedback might be related to learning or reinforcing sensory-motor relations in the sensory population.

Aging Affects Fine and Coarse Coding of Orientation Information in Macaque Primary Visual Cortex

Human visual function degrades with age. Previous studies of visual perception have shown that aged people have worse performance in the coding of orientation information. However, the neuronal mechanism still remains elusive. In this study, we performed in vivo extracellular single-unit recording in the primary visual cortex of senescent and young monkeys, and we used the Chernoff distance to quantify the encoded information of neurons for fine and coarse orientation difference. Our results showed that the Chernoff distance for fine orientation difference in senescent monkeys is significantly smaller than that in young monkeys. In contrast, the Chernoff distance for the coarse coding was comparable in young and old groups. Meanwhile, increased spontaneous response and maximum evoked response was also observed. Further investigation of neuronal correlation showed higher noise and signal correlations in aging monkeys than that in young monkeys. These correlation changes predicted a detrimental effect on the efficiency of population coding of orientation information. Taken together, our results suggest that the information coding efficiency of orientation information is impaired during aging and might account for the degradation of performance in human fine orientation discrimination task.

Randomly connected networks generate emergent selectivity and predict decoding properties of large populations of neurons.

Modern recording methods enable sampling of thousands of neurons during the performance of behavioral tasks, raising the question of how recorded activity relates to theoretical models. In the context of decision making, functional connectivity between choice-selective cortical neurons was recently reported. The straightforward interpretation of these data suggests the existence of selective pools of inhibitory and excitatory neurons. Computationally investigating an alternative mechanism for these experimental observations, we find that a randomly connected network of excitatory and inhibitory neurons generates single-cell selectivity, patterns of pairwise correlations, and the same ability of excitatory and inhibitory populations to predict choice, as in experimental observations. Further, we predict that, for this task, there are no anatomically defined subpopulations of neurons representing choice, and that choice preference of a particular neuron changes with the details of the task. We suggest that distributed stimulus selectivity and functional organization in population codes could be emergent properties of randomly connected networks.

Valence and State-Dependent Population Coding in Dopaminergic Neurons in the Fly Mushroom Body

Neuromodulation permits flexibility of synapses, neural circuits, and ultimately behavior. One neuromodulator, dopamine, has been studied extensively in its role as a reward signal during learning and memory across animal species. Newer evidence suggests that dopaminergic neurons (DANs) can modulate sensory perception acutely, thereby allowing an animal to adapt its behavior and decision making to its internal and behavioral state. In addition, some data indicate that DANs are not homogeneous but rather convey different types of information as a heterogeneous population. We have investigated DAN population activity and how it could encode relevant information about sensory stimuli and state by taking advantage of the confined anatomy of DANs innervating the mushroom body (MB) of the fly Drosophila melanogaster. Using invivo calcium imaging and a custom 3D image registration method, we found that the activity of the population of MB DANs encodes innate valence information of an odor or taste as well as the physiological state of the animal. Furthermore, DAN population activity is strongly correlated with movement, consistent with a role of dopamine in conveying behavioral state to the MB. Altogether, our data and analysis suggest that DAN population activities encode innate odor and taste valence, movement, and physiological state in a MB-compartment-specific manner. We propose that dopamine shapes innate perception through combinatorial population coding of sensory valence, physiological, and behavioral context.

In vivo large-scale analysis of Drosophila neuronal calcium traces by automated tracking of single somata

How does the concerted activity of neuronal populations shape behavior? Impediments to address this question are primarily due to critical experimental barriers. An integrated perspective on large scale neural information processing requires an in vivo approach that can combine the advantages of exhaustively observing all neurons dedicated to a given type of stimulus, and simultaneously achieve a resolution that is precise enough to capture individual neuron activity. Current experimental data from in vivo observations are either restricted to a small fraction of the total number of neurons, or are based on larger brain volumes but at a low spatial and temporal resolution. Consequently, fundamental questions as to how sensory information is represented on a population scale remain unanswered. In Drosophila melanogaster, the mushroom body (MB) represents an excellent model to analyze sensory coding and memory plasticity. In this work, we present an experimental setup coupled with a dedicated computational method that provides in vivo measurements of the activity of hundreds of densely packed somata uniformly spread in the MB. We exploit spinning-disk confocal 3D imaging over time of the whole MB cell body layer in vivo while it is exposed to olfactory stimulation. Importantly, to derive individual signal from densely packed somata, we have developed a fully automated image analysis procedure that takes advantage of the specificities of our data. After anisotropy correction, our approach operates a dedicated spot detection and registration over the entire time sequence to transform trajectories to identifiable clusters. This enabled us to discard spurious detections and reconstruct missing ones in a robust way. We demonstrate that this approach outperformed existing methods in this specific context and made possible high-throughput analysis of approximately 500 single somata uniformly spread over the MB in various conditions. Applying this approach, we find that learned experiences change the population code of odor representations in the MB. After long-term memory (LTM) formation, we quantified an increase in responsive somata count and a stable single neuron signal. We predict that this method, which should further enable studying the population pattern of neuronal activity, has the potential to uncover fine details of sensory processing and memory plasticity.

Gastric Distension‐induced Nodose Ganglionic Cell Responses Using a High‐throughput Multi‐electrode Array in the Ferret

The nodose ganglion (NG) contains cell bodies of gastrointestinal afferents, which play critical roles in metabolic and feeding disorders and gastrointestinal and inflammatory diseases. To further understand the population coding of nodose ganglionic signals, we used a 32‐chan multi‐electrode array (MEA; 4×8, 400 μm pitch; Blackrock Microsystems) to record single‐unit responses with 3 main objectives: (1) determine the stability of multiple single‐unit responses during several hours of in vivo electrophysiological recording; (2) measure the activation patterns of these neurons to prototypical stimulation, such as gastric distension; and (3) investigate the phenotypes of nodose neurons using mechanical probing and vagus nerve electrical stimulation to determine conduction velocities. Acute experiments were performed in anesthetized ferrets using inhalational isoflurane (~2.5%) anesthesia. MEAs were inserted in the nodose ganglion using a pneumatic inserter, and a balloon‐catheter was advanced into the stomach through a small incision on the lateral edge of the gastric fundus. Two volumes of saline, 10 and 20 ml, were infused into the balloon to provide distension. Nerve cuff electrodes were placed on the abdominal vagus trunk to determine conduction velocity. Ganglionic single‐unit activity was successfully recorded from the NG for up to 5.3 h. In our preliminary analysis using spike sorting on each channel (MKsort, https://github.com/ripple‐neuro/mksort; Matlab), we were able to identify a total of 233 units in 6 animals (16 to 55 units per animal). Gastric balloon distension produced &gt;20% increase in neuronal activity in an average of 48% of units to 10 ml inflation and 56% of units to 20 ml inflation. These results demonstrate the feasibility to record multiple single‐unit responses from the ferret NG to provide insight into gastric‐related population coding. Future effort could focus on delineating the physiological roles of these gastric neurons for the design of neuromodulation devices, which could be used to treat diseases involving vagal sensory input such as obesity and the chronic nausea associated with gastroparesis.Support or Funding InformationThis research was supported by funding from the NIH SPARC Common Fund Program (award #U18TR002205).

Composite neonatal morbidity indicators using hospital discharge data: A systematic review.

Neonatal morbidity is associated with lifelong impairments, but the absence of a consensual definition and the need for large data sets limit research. To inform initiatives to define standard outcomes for research, we reviewed composite neonatal morbidity indicators derived from routine hospital discharge data. PubMed (updated on October 12, 2018). The search algorithm was based on three components: "morbidity," "neonatal," and "hospital discharge data." Studies investigating neonatal morbidity using a composite indicator based on hospital discharge data were included. Indicators defined for specific conditions (eg congenital anomalies, maternal addictions) were excluded. The target population, objectives, component morbidities, diagnosis and procedure codes, validation methods, and prevalence of morbidity were extracted. For each study, we assessed construct validity by describing the methods used to select the indicator components and evaluated whether the authors assessed internal and external validity. We also calculated confidence intervals for the prevalence of the morbidity composite. Seventeen studies fulfilled inclusion criteria. Indicators targeted all (n=4), low-/moderate-risk (n=9), and very preterm (VPT, n=4) infants. Components were similar for VPT infants, but domains and diagnosis codes within domains varied widely for all and low-/moderate-risk infants. Component selection was described for 8/17 indicators and some form of validation reported for 12/17. Neonatal morbidity prevalence ranged from 4.6% to 9.0% of all infants, 0.4% to 8.0% of low-/moderate-risk infants, and 17.8% to 61.0% of VPT infants. Multiple neonatal morbidity indicators based on hospital discharge data have been used for research, but their heterogeneity limits comparisons between studies. Standard neonatal outcome measures are needed for benchmarking and synthesis of research results.

Learning to represent signals spike by spike.

Networks based on coordinated spike coding can encode information with high efficiency in the spike trains of individual neurons. These networks exhibit single-neuron variability and tuning curves as typically observed in cortex, but paradoxically coincide with a precise, non-redundant spike-based population code. However, it has remained unclear whether the specific synaptic connectivities required in these networks can be learnt with local learning rules. Here, we show how to learn the required architecture. Using coding efficiency as an objective, we derive spike-timing-dependent learning rules for a recurrent neural network, and we provide exact solutions for the networks' convergence to an optimal state. As a result, we deduce an entire network from its input distribution and a firing cost. After learning, basic biophysical quantities such as voltages, firing thresholds, excitation, inhibition, or spikes acquire precise functional interpretations.

Clustering of Neural Activity: A Design Principle for Population Codes.

We propose that correlations among neurons are generically strong enough to organize neural activity patterns into a discrete set of clusters, which can each be viewed as a population codeword. Our reasoning starts with the analysis of retinal ganglion cell data using maximum entropy models, showing that the population is robustly in a frustrated, marginally sub-critical, or glassy, state. This leads to an argument that neural populations in many other brain areas might share this structure. Next, we use latent variable models to show that this glassy state possesses well-defined clusters of neural activity. Clusters have three appealing properties: (i) clusters exhibit error correction, i.e., they are reproducibly elicited by the same stimulus despite variability at the level of constituent neurons; (ii) clusters encode qualitatively different visual features than their constituent neurons; and (iii) clusters can be learned by downstream neural circuits in an unsupervised fashion. We hypothesize that these properties give rise to a “learnable” neural code which the cortical hierarchy uses to extract increasingly complex features without supervision or reinforcement.

Effect of Stimulus-Dependent Spike Timing on Population Coding of Sound Location in the Owl's Auditory Midbrain.

In the auditory system, the spectrotemporal structure of acoustic signals determines the temporal pattern of spikes. Here, we investigated this effect in neurons of the barn owl’s auditory midbrain (Tyto furcata) that are selective for auditory space and whether it can influence the coding of sound direction. We found that in the nucleus where neurons first become selective to combinations of sound localization cues, reproducibility of spike trains across repeated trials of identical sounds, a metric of across-trial temporal fidelity of spiking patterns evoked by a stimulus, was maximal at the sound direction that elicited the highest firing rate. We then tested the hypothesis that this stimulus-dependent patterning resulted in rate co-modulation of cells with similar frequency and spatial selectivity, driving stimulus-dependent synchrony of population responses. Tetrodes were used to simultaneously record multiple nearby units in the optic tectum (OT), where auditory space is topographically represented. While spiking of neurons in OT showed lower reproducibility across trials compared with upstream nuclei, spike-time synchrony between nearby OT neurons was highest for sounds at their preferred direction. A model of the midbrain circuit explained the relationship between stimulus-dependent reproducibility and synchrony, and demonstrated that this effect can improve the decoding of sound location from the OT output. Thus, stimulus-dependent spiking patterns in the auditory midbrain can have an effect on spatial coding. This study reports a functional connection between spike patterning elicited by spectrotemporal features of a sound and the coding of its location.

Circuit and Cellular Mechanisms Facilitate the Transformation from Dense to Sparse Coding in the Insect Olfactory System.

Transformations between sensory representations are shaped by neural mechanisms at the cellular and the circuit level. In the insect olfactory system, the encoding of odor information undergoes a transition from a dense spatiotemporal population code in the antennal lobe to a sparse code in the mushroom body. However, the exact mechanisms shaping odor representations and their role in sensory processing are incompletely identified. Here, we investigate the transformation from dense to sparse odor representations in a spiking model of the insect olfactory system, focusing on two ubiquitous neural mechanisms: spike frequency adaptation at the cellular level and lateral inhibition at the circuit level. We find that cellular adaptation is essential for sparse representations in time (temporal sparseness), while lateral inhibition regulates sparseness in the neuronal space (population sparseness). The interplay of both mechanisms shapes spatiotemporal odor representations, which are optimized for the discrimination of odors during stimulus onset and offset. Response pattern correlation across different stimuli showed a nonmonotonic dependence on the strength of lateral inhibition with an optimum at intermediate levels, which is explained by two counteracting mechanisms. In addition, we find that odor identity is stored on a prolonged timescale in the adaptation levels but not in the spiking activity of the principal cells of the mushroom body, providing a testable hypothesis for the location of the so-called odor trace.

Coding with transient trajectories in recurrent neural networks.

Following a stimulus, the neural response typically strongly varies in time and across neurons before settling to a steady-state. While classical population coding theory disregards the temporal dimension, recent works have argued that trajectories of transient activity can be particularly informative about stimulus identity and may form the basis of computations through dynamics. Yet the dynamical mechanisms needed to generate a population code based on transient trajectories have not been fully elucidated. Here we examine transient coding in a broad class of high-dimensional linear networks of recurrently connected units. We start by reviewing a well-known result that leads to a distinction between two classes of networks: networks in which all inputs lead to weak, decaying transients, and networks in which specific inputs elicit amplified transient responses and are mapped onto output states during the dynamics. Theses two classes are simply distinguished based on the spectrum of the symmetric part of the connectivity matrix. For the second class of networks, which is a sub-class of non-normal networks, we provide a procedure to identify transiently amplified inputs and the corresponding readouts. We first apply these results to standard randomly-connected and two-population networks. We then build minimal, low-rank networks that robustly implement trajectories mapping a specific input onto a specific orthogonal output state. Finally, we demonstrate that the capacity of the obtained networks increases proportionally with their size.

Neural Synchrony Gives Rise to Amplitude- and Duration-Invariant Encoding Consistent With Perception of Natural Communication Stimuli.

When confronted with a highly variable environment, it remains poorly understood how neural populations encode and classify natural stimuli to give rise to appropriate and consistent behavioral responses. Here we investigated population coding of natural communication signals with different attributes (i.e., amplitude and duration) in the electrosensory system of the weakly electric fish Apteronotus leptorhynchus. Our results show that, while single peripheral neurons encode the detailed timecourse of different stimulus waveforms, measures of population synchrony are effectively unchanged because of coordinated increases and decreases in activity. A phenomenological mathematical model reproduced this invariance and shows that this can be explained by considering homogeneous populations whose responses are solely determined by single neuron firing properties. Moreover, recordings from downstream central neurons reveal that synchronous afferent activity is actually decoded and thus most likely transmitted to higher brain areas. Finally, we demonstrate that the associated behavioral responses at the organism level are invariant. Our results provide a mechanism by which amplitude- and duration-invariant coding of behaviorally relevant sensory input emerges across successive brain areas thereby presumably giving rise to invariant behavioral responses. Such mechanisms are likely to be found in other systems that share anatomical and functional features with the electrosensory system (e.g., auditory, visual, vestibular).

Hoki: Making BPASS accessible through Python

We now know that a large number of stars are born in multiple systems. Additionally, more than 70% of massive stars are found in close binary systems, meaning that they will interact over the course of their lifetime. This has strong implications for their evolution as well as the transients (e.g supernovae) and the potential gravitational wave progenitors they produce. Therefore, in order to understand and correctly interpret astronomical observations of stellar populations, we must use theoretical models able to account for the effects of binary stars. This is the case of the Binary Population and Spectral Synthesis code (BPASS), which has been a staple of the field for over 10 years. As is the case for most other theoretical models, the data products of BPASS are large, varied and complex. As a result, their use requires a level of expertise that is not immediately accessible to a wider community that may hold key observational data. The goal of hoki is to bridge the gap between observation and theory, by providing a set of tools to make BPASS data easily accessible and facilitate analysis. The use of Python is deliberate as it is a ubiquitous language within Astronomy. This allows BPASS results to be used naturally within the pre-existing workflow of most astronomers.

Unique Spatial Integration in Mouse Primary Visual Cortex and Higher Visual Areas.

Neurons in the visual system integrate over a wide range of spatial scales. This diversity is thought to enable both local and global computations. To understand how spatial information is encoded across the mouse visual system, we use two-photon imaging to measure receptive fields (RFs) and size-tuning in primary visual cortex (V1) and three downstream higher visual areas (HVAs: LM (lateromedial), AL (anterolateral), and PM (posteromedial)) in mice of both sexes. Neurons in PM, compared with V1 or the other HVAs, have significantly larger RF sizes and less surround suppression, independent of stimulus eccentricity or contrast. To understand how this specialization of RFs arises in the HVAs, we measured the spatial properties of V1 inputs to each area. Spatial integration of V1 axons was remarkably similar across areas and significantly different from the tuning of neurons in their target HVAs. Thus, unlike other visual features studied in this system, specialization of spatial integration in PM cannot be explained by specific projections from V1 to the HVAs. Further, the differences in RF properties could not be explained by differences in convergence of V1 inputs to the HVAs. Instead, our data suggest that distinct inputs from other areas or connectivity within PM may support the area's unique ability to encode global features of the visual scene, whereas V1, LM, and AL may be more specialized for processing local features.SIGNIFICANCE STATEMENT Surround suppression is a common feature of visual processing whereby large stimuli are less effective at driving neuronal responses than smaller stimuli. This is thought to enhance efficiency in the population code and enable higher-order processing of visual information, such as figure-ground segregation. However, this comes at the expense of global computations. Here we find that surround suppression is not equally represented across mouse visual areas: primary visual cortex has substantially more surround suppression than higher visual areas, and one higher area has significantly less suppression than two others examined, suggesting that these areas have distinct functional roles. Thus, we have identified a novel dimension of specialization in the mouse visual cortex that may enable both local and global computations.