Reliable neuronal systems: the importance of heterogeneity

KETAMINE: Neural- and network-level changes

Ketamine is a widely used clinical drug that has several functional and clinical applications, including its use as an anaesthetic, analgesic, anti-depressive, anti-suicidal agent, among others. Among its diverse behavioral effects, it influences short-term memory and induces psychedelic effects. At the neural level across different brain areas, it modulates neural firing rates, neural tuning, brain oscillations, and modularity, while promoting hypersynchrony and random connectivity between neurons. In our recent studies we demonstrated that topical application of ketamine on the visual cortex alters neural tuning and promotes vigorous connectivity between neurons by decreasing their firing variability. Here, we begin with a brief review of the literature, followed by results from our lab, where we synthesize a dendritic model of neural tuning and network changes following ketamine application. This model has potential implications for focused modulation of cortical networks in clinical settings. Finally, we identify current gaps in research and suggest directions for future studies, particularly emphasizing the need for more animal experiments to establish a platform for effective translation and synergistic therapies combining ketamine with other protocols such as training and adaptation. In summary, investigating ketamine's broader systemic effects, not only provides deeper insight into cognitive functions and consciousness but also paves the way to advance therapies for neuropsychiatric disorders.

Analyzing top-down visual attention in the context of gamma oscillations: a layer- dependent network-of- networks approach

Top-down visual attention is a fundamental cognitive process that allows individuals to selectively attend to salient visual stimuli in the environment. Recent empirical findings have revealed that gamma oscillations participate in the modulation of visual attention. However, computational studies face challenges when analyzing the attentional process in the context of gamma oscillation due to the unstable nature of gamma oscillations and the complexity induced by the layered fashion in the visual cortex. In this study, we propose a layer-dependent network-of-networks approach to analyze such attention with gamma oscillations. The model is validated by reproducing empirical findings on orientation preference and the enhancement of neuronal response due to top-down attention. We perform parameter plane analysis to classify neuronal responses into several patterns and find that the neuronal response to sensory and attention signals was modulated by the heterogeneity of the neuronal population. Furthermore, we revealed a counter-intuitive scenario that the excitatory populations in layer 2/3 and layer 5 exhibit opposite responses to the attentional input. By modification of the original model, we confirmed layer 6 plays an indispensable role in such cases. Our findings uncover the layer-dependent dynamics in the cortical processing of visual attention and open up new possibilities for further research on layer-dependent properties in the cerebral cortex.

The effects of chronic, continuous β-funaltrexamine pre-treatment on lipopolysaccharide-induced inflammation and behavioral deficits in C57BL/6J mice

BACKGROUND

Inflammation and neuroinflammation are integral to the progression and severity of many diseases and are strongly associated with cardiovascular disease, cancer, autoimmune disorders, neurodegenerative disease, and neuropsychiatric disorders. These diseases can be difficult to treat without addressing the underlying inflammation, and, as such, a growing need has arisen for pharmaceutical treatments that target inflammatory mediators and signaling pathways. Our lab has investigated the therapeutic potential of the irreversible µ-opioid antagonist β-funaltrexamine (β-FNA) and discovered that acute treatment ameliorates inflammation in astrocytes in vitro and inhibits central and peripheral inflammation and reduces anxiety- and sickness-like behavior in male C57BL/6J mice. Now, our investigation has expanded to investigate the chronic pre-treatment effects of β-FNA on lipopolysaccharide (LPS)-induced inflammation and behavior in male C57BL/6J mice.

RESULTS

Micro-osmotic drug pumps were surgically inserted into the subcutaneous intrascapular space of male C57BL/6J mice. β-FNA or saline vehicle was continuously administered for seven days. On the sixth day, mice were given intraperitoneal injections of LPS or saline. An elevated plus maze test, followed by a forced swim test, were administered 24 h post-injection to measure sickness-, anxiety- and depressive-like behavior. Immediately after testing, frontal cortex, hippocampus, spleen, and plasma were collected. Levels of inflammatory chemokines C-C motif chemokine ligand 2 (CCL2) and C-X-C motif chemokine ligand 10 (CXCL10) were measured in tissues by enzyme-linked immunosorbent assay (ELISA). Quantitative reverse transcription polymerase chain reaction (RT-qPCR) was used to assess expression of the enzyme indoleamine 2, 3-dioxygenase 1 (IDO1) and the NLR family pyrin domain-containing protein 3 (NRLP3) inflammasome in frontal cortex and spleen tissues. Chronic pre-treatment robustly decreased inflammation in the hippocampus, frontal cortex, and spleen and reduced or abolished anxiety- and sickness-like behavior (e.g., increased time spent motionless, increased time spent in a contracted position, and reduced distance moved). However, treatment with β-FNA alone increased both inflammation in the frontal cortex and anxiety-like behavior.

CONCLUSION

These findings provide novel insights into the anti-inflammatory and behavior-modifying effects of chronic β-FNA pre-treatment and continue to support the therapeutic potential of β-FNA under inflammatory conditions.

Mathematical generation of data-driven hippocampal CA1 pyramidal neurons and interneurons copies via A-GLIF models for large-scale networks covering the experimental variability range

Efficient and accurate large-scale networks are a fundamental tool in modeling brain areas, to advance our understanding of neuronal dynamics. However, their implementation faces two key issues: computational efficiency and heterogeneity. Computational efficiency is achieved using simplified neurons, whereas there are no practical solutions available to solve the problem of reproducing in a large-scale network the experimentally observed heterogeneity of the intrinsic properties of neurons. This is important, because the use of identical nodes in a network can generate artifacts which can hinder an adequate representation of the properties of a real network. To this aim, we introduce a mathematical procedure to generate an arbitrary large number of copies of simplified hippocampal CA1 pyramidal neurons and interneurons models, which exhibit the full range of firing dynamics observed in these cells - including adapting, non-adapting and bursting. For this purpose, we rely on a recently published adaptive generalized leaky integrate-and-fire (A-GLIF) modeling approach, leveraging on its ability to reproduce the rich set of electrophysiological behaviors of these types of neurons under a variety of different stimulation currents. The generation procedure is based on a perturbation of model's parameters related to the initial data, firing block, and internal dynamics, and suitably validated against experimental data to ensure that the firing dynamics of any given cell copy remains within the experimental range. A classification procedure confirmed that the firing behavior of most of the pyramidal/interneuron copies was consistent with the experimental data. This approach allows to obtain heterogeneous copies with mathematically controlled firing properties. A full set of heterogeneous neurons composing the CA1 region of a rat hippocampus (approximately 1.2 million neurons), are provided in a database freely available in the live paper section of the EBRAINS platform. By adapting the underlying A-GLIF framework, it will be possible to extend the numerical approach presented here to create, in a mathematically controlled manner, an arbitrarily large number of non-identical copies of cell populations with firing properties related to other brain areas.

Energy controls wave propagation in a neural network with spatial stimuli

Nervous system has distinct anisotropy and some intrinsic biophysical properties enable neurons present various firing modes in neural activities. In presence of realistic electromagnetic fields, non-uniform radiation activates these neurons with energy diversity. By using a feasible model, energy function is obtained to predict the growth of synaptic connections of these neurons. Distribution of average value of the Hamilton energy function vs. intensity of noisy disturbance can predict the occurrence of coherence resonance, which the neural activities show high regularity by applying noisy disturbance with moderate intensity. From physical viewpoint, the average energy value has similar role average power for the neuron. Non-uniform spatial disturbance is applied and energy is injected into the neural network, statistical synchronization factor is calculated to predict the network synchronization stability and wave propagation. The intensity for field coupling is adaptively controlled by energy diversity between adjacent neurons. Local energy balance will terminate further growth of the coupling intensity; otherwise, heterogeneity is formed in the network due to energy diversity. Furthermore, memristive channel current is introduced into the neuron model for perceiving the effect of electromagnetic induction and radiation, and a memristive neuron is obtained. The circuit implement of memristive circuit depends on the connection to a magnetic flux-controlled memristor into the mentioned neural circuit in an additive branch circuit. The connection and activation of this memristive neural network are controlled under external spatial electromagnetic radiation by capturing enough field energy. Continuous energy collection and exchange generate energy diversity and synaptic connection is created to regulate the synchronous firing patterns and energy balance.

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.

Distinctive biophysical features of human cell-types: insights from studies of neurosurgically resected brain tissue

Electrophysiological characterization of live human tissue from epilepsy patients has been performed for many decades. Although initially these studies sought to understand the biophysical and synaptic changes associated with human epilepsy, recently, it has become the mainstay for exploring the distinctive biophysical and synaptic features of human cell-types. Both epochs of these human cellular electrophysiological explorations have faced criticism. Early studies revealed that cortical pyramidal neurons obtained from individuals with epilepsy appeared to function "normally" in comparison to neurons from non-epilepsy controls or neurons from other species and thus there was little to gain from the study of human neurons from epilepsy patients. On the other hand, contemporary studies are often questioned for the "normalcy" of the recorded neurons since they are derived from epilepsy patients. In this review, we discuss our current understanding of the distinct biophysical features of human cortical neurons and glia obtained from tissue removed from patients with epilepsy and tumors. We then explore the concept of within cell-type diversity and its loss (i.e., "neural homogenization"). We introduce neural homogenization to help reconcile the epileptogenicity of seemingly "normal" human cortical cells and circuits. We propose that there should be continued efforts to study cortical tissue from epilepsy patients in the quest to understand what makes human cell-types "human".

Bilateral widefield calcium imaging reveals circuit asymmetries and lateralized functional activation of the mouse auditory cortex

Coordinated functioning of the two cortical hemispheres is crucial for perception. The human auditory cortex (ACx) shows functional lateralization with the left hemisphere specialized for processing speech, whereas the right analyzes spectral content. In mice, virgin females demonstrate a left-hemisphere response bias to pup vocalizations that strengthens with motherhood. However, how this lateralized function is established is unclear. We developed a widefield imaging microscope to simultaneously image both hemispheres of mice to bilaterally monitor functional responses. We found that global ACx topography is symmetrical and stereotyped. In both male and virgin female mice, the secondary auditory cortex (A2) in the left hemisphere shows larger responses than right to high-frequency tones and adult vocalizations; however, only virgin female mice show a left-hemisphere bias in A2 in response to adult pain calls. These results indicate hemispheric bias with both sex-independent and -dependent aspects. Analyzing cross-hemispheric functional correlations showed that asymmetries exist in the strength of correlations between DM-AAF and A2-AAF, while other ACx areas showed smaller differences. We found that A2 showed lower cross-hemisphere correlation than other cortical areas, consistent with the lateralized functional activation of A2. Cross-hemispheric activity correlations are lower in deaf, otoferlin knockout (OTOF-/-) mice, indicating that the development of functional cross-hemispheric connections is experience dependent. Together, our results reveal that ACx is topographically symmetric at the macroscopic scale but that higher-order A2 shows sex-dependent and independent lateralized responses due to asymmetric intercortical functional connections. Moreover, our results suggest that sensory experience is required to establish functional cross-hemispheric connectivity.

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.

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.

Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST

Spiking neural networks (SNNs) represent the state-of-the-art approach to the biologically realistic modeling of nervous system function. The systematic calibration for multiple free model parameters is necessary to achieve robust network function and demands high computing power and large memory resources. Special requirements arise from closed-loop model simulation in virtual environments and from real-time simulation in robotic application. Here, we compare two complementary approaches to efficient large-scale and real-time SNN simulation. The widely used NEural Simulation Tool (NEST) parallelizes simulation across multiple CPU cores. The GPU-enhanced Neural Network (GeNN) simulator uses the highly parallel GPU-based architecture to gain simulation speed. We quantify fixed and variable simulation costs on single machines with different hardware configurations. As a benchmark model, we use a spiking cortical attractor network with a topology of densely connected excitatory and inhibitory neuron clusters with homogeneous or distributed synaptic time constants and in comparison to the random balanced network. We show that simulation time scales linearly with the simulated biological model time and, for large networks, approximately linearly with the model size as dominated by the number of synaptic connections. Additional fixed costs with GeNN are almost independent of model size, while fixed costs with NEST increase linearly with model size. We demonstrate how GeNN can be used for simulating networks with up to 3.5 · 106 neurons (> 3 · 1012synapses) on a high-end GPU, and up to 250, 000 neurons (25 · 109 synapses) on a low-cost GPU. Real-time simulation was achieved for networks with 100, 000 neurons. Network calibration and parameter grid search can be efficiently achieved using batch processing. We discuss the advantages and disadvantages of both approaches for different use cases.

Heterogeneity in GnRH and kisspeptin neurons and their significance in vertebrate reproductive biology

Vertebrate reproduction is essentially controlled by the hypothalamus-pituitary-gonadal (HPG) axis, which is a central dogma of reproductive biology. Two major hypothalamic neuroendocrine cell groups containing gonadotropin-releasing hormone (GnRH) and kisspeptin are crucial for control of the HPG axis in vertebrates. GnRH and kisspeptin neurons exhibit high levels of heterogeneity including their cellular morphology, biochemistry, neurophysiology and functions. However, the molecular foundation underlying heterogeneities in GnRH and kisspeptin neurons remains unknown. More importantly, the biological and physiological significance of their heterogeneity in reproductive biology is poorly understood. In this review, we first describe the recent advances in the neuroendocrine functions of kisspeptin-GnRH pathways. We then view the recent emerging progress in the heterogeneity of GnRH and kisspeptin neurons using morphological and single-cell transcriptomic analyses. Finally, we discuss our views on the significance of functional heterogeneity of reproductive endocrine cells and their potential relevance to reproductive health.

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.

On the Use of a Multimodal Optimizer for Fitting Neuron Models. Application to the Cerebellar Granule Cell

This article extends a recent methodological workflow for creating realistic and computationally efficient neuron models whilst capturing essential aspects of single-neuron dynamics. We overcome the intrinsic limitations of the extant optimization methods by proposing an alternative optimization component based on multimodal algorithms. This approach can natively explore a diverse population of neuron model configurations. In contrast to methods that focus on a single global optimum, the multimodal method allows directly obtaining a set of promising solutions for a single but complex multi-feature objective function. The final sparse population of candidate solutions has to be analyzed and evaluated according to the biological plausibility and their objective to the target features by the expert. In order to illustrate the value of this approach, we base our proposal on the optimization of cerebellar granule cell (GrC) models that replicate the essential properties of the biological cell. Our results show the emerging variability of plausible sets of values that this type of neuron can adopt underlying complex spiking characteristics. Also, the set of selected cerebellar GrC models captured spiking dynamics closer to the reference model than the single model obtained with off-the-shelf parameter optimization algorithms used in our previous article. The method hereby proposed represents a valuable strategy for adjusting a varied population of realistic and simplified neuron models. It can be applied to other kinds of neuron models and biological contexts.

A Systematic Framework for Olfactory Bulb Signal Transformations

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

Signal Propagation via Open-Loop Intrathalamic Architectures: A Computational Model

Propagation of signals across the cerebral cortex is a core component of many cognitive processes and is generally thought to be mediated by direct intracortical connectivity. The thalamus, by contrast, is considered to be devoid of internal connections and organized as a collection of parallel inputs to the cortex. Here, we provide evidence that "open-loop" intrathalamic pathways involving the thalamic reticular nucleus (TRN) can support propagation of oscillatory activity across the cortex. Recent studies support the existence of open-loop thalamo-reticulo-thalamic (TC-TRN-TC) synaptic motifs in addition to traditional closed-loop architectures. We hypothesized that open-loop structural modules, when connected in series, might underlie thalamic and, therefore cortical, signal propagation. Using a supercomputing platform to simulate thousands of permutations of a thalamocortical network based on physiological data collected in mice, rats, ferrets, and cats and in which select synapses were allowed to vary both by class and individually, we evaluated the relative capacities of closed-loop and open-loop TC-TRN-TC synaptic configurations to support both propagation and oscillation. We observed that (1) signal propagation was best supported in networks possessing strong open-loop TC-TRN-TC connectivity; (2) intrareticular synapses were neither primary substrates of propagation nor oscillation; and (3) heterogeneous synaptic networks supported more robust propagation of oscillation than their homogeneous counterparts. These findings suggest that open-loop, heterogeneous intrathalamic architectures might complement direct intracortical connectivity to facilitate cortical signal propagation.

Enabling Large-Scale Simulations With the GENESIS Neuronal Simulator

In this paper, we evaluate the computational performance of the GEneral NEural SImulation System (GENESIS) for large scale simulations of neural networks. While many benchmark studies have been performed for large scale simulations with leaky integrate-and-fire neurons or neuronal models with only a few compartments, this work focuses on higher fidelity neuronal models represented by 50–74 compartments per neuron. After making some modifications to the source code for GENESIS and its parallel implementation, PGENESIS, particularly to improve memory usage, we find that PGENESIS is able to efficiently scale on supercomputing resources to network sizes as large as 9 × 10⁶ neurons with 18 × 10⁹ synapses and 2.2 × 10⁶ neurons with 45 × 10⁹ synapses. The modifications to GENESIS that enabled these large scale simulations have been incorporated into the May 2019 Official Release of PGENESIS 2.4 available for download from the GENESIS web site (genesis-sim.org).

Neuromorphic Dynamical Synapses With Reconfigurable Voltage-Gated Kinetics

OBJECTIVE

Although biological synapses express a large variety of receptors in neuronal membranes, the current hardware implementation of neuromorphic synapses often rely on simple models ignoring the heterogeneity of synaptic transmission. Our objective is to emulate different types of synapses with distinct properties.

METHODS

Conductance-based chemical and electrical synapses were implemented between silicon neurons on a fully programmable and reconfigurable, biophysically realistic neuromorphic VLSI chip. Different synaptic properties were achieved by configuring on-chip digital parameters for the conductances, reversal potentials, and voltage dependence of the channel kinetics. The measured I-V characteristics of the artificial synapses were compared with biological data.

RESULTS

We reproduced the response properties of five different types of chemical synapses, including both excitatory ( AMPA, NMDA) and inhibitory ( GABA_A, GABA_C, glycine) ionotropic receptors. In addition, electrical synapses were implemented in a small network of four silicon neurons.

CONCLUSION

Our work extends the repertoire of synapse types between silicon neurons, providing greater flexibility for the design and implementation of biologically realistic neural networks on neuromorphic chips.

SIGNIFICANCE

A higher synaptic heterogeneity in neuromorphic chips is relevant for the hardware implementation of energy-efficient population codes as well as for dynamic clamp applications where neural models are implemented in neuromorphic VLSI hardware.

Functional Diversity in the Retina Improves the Population Code

Within a given brain region, individual neurons exhibit a wide variety of different feature selectivities. Here, we investigated the impact of this extensive functional diversity on the population neural code. Our approach was to build optimal decoders to discriminate among stimuli using the spiking output of a real, measured neural population and compare its performance against a matched, homogeneous neural population with the same number of cells and spikes. Analyzing large populations of retinal ganglion cells, we found that the real, heterogeneous population can yield a discrimination error lower than the homogeneous population by several orders of magnitude and consequently can encode much more visual information. This effect increases with population size and with graded degrees of heterogeneity. We complemented these results with an analysis of coding based on the Chernoff distance, as well as derivations of inequalities on coding in certain limits, from which we can conclude that the beneficial effect of heterogeneity occurs over a broad set of conditions. Together, our results indicate that the presence of functional diversity in neural populations can enhance their coding fidelity appreciably. A noteworthy outcome of our study is that this effect can be extremely strong and should be taken into account when investigating design principles for neural circuits.

Enhanced Peptide Detection Toward Single-Neuron Proteomics by Reversed-Phase Fractionation Capillary Electrophoresis Mass Spectrometry

The ability to detect peptides and proteins in single cells is vital for understanding cell heterogeneity in the nervous system. Capillary electrophoresis (CE) nanoelectrospray ionization (nanoESI) provides high-resolution mass spectrometry (HRMS) with trace-level sensitivity, but compressed separation during CE challenges protein identification by tandem HRMS with limited MS/MS duty cycle. Here, we supplemented ultrasensitive CE-nanoESI-HRMS with reversed-phase (RP) fractionation to enhance identifications from protein digest amounts that approximate to a few mammalian neurons. An ~1 to 20 μg neuronal protein digest was fractionated on a RP column (ZipTip), and 1 ng to 500 pg of peptides were analyzed by a custom-built CE-HRMS system. Compared with the control (no fractionation), RP fractionation improved CE separation (theoretical plates ~274,000 versus 412,000 maximum, resp.), which enhanced detection sensitivity (2.5-fold higher signal-to-noise ratio), minimized co-isolation spectral interferences during MS/MS, and increased the temporal rate of peptide identification by up to ~57%. From 1 ng of protein digest (<5 neurons), CE with RP fractionation identified 737 protein groups (1,753 peptides), or ~480 protein groups (~1,650 peptides) on average per analysis. The approach was scalable to 500 pg of protein digest (~a single neuron), identifying 225 protein groups (623 peptides) in technical triplicates, or 141 protein groups on average per analysis. Among identified proteins, 101 proteins were products of genes that are known to be transcriptionally active in single neurons during early development of the brain, including those involved in synaptic transmission and plasticity and cytoskeletal organization. Graphical abstract ᅟ.

A neural network model for familiarity and context learning during honeybee foraging flights

How complex is the memory structure that honeybees use to navigate? Recently, an insect-inspired parsimonious spiking neural network model was proposed that enabled simulated ground-moving agents to follow learned routes. We adapted this model to flying insects and evaluate the route following performance in three different worlds with gradually decreasing object density. In addition, we propose an extension to the model to enable the model to associate sensory input with a behavioral context, such as foraging or homing. The spiking neural network model makes use of a sparse stimulus representation in the mushroom body and reward-based synaptic plasticity at its output synapses. In our experiments, simulated bees were able to navigate correctly even when panoramic cues were missing. The context extension we propose enabled agents to successfully discriminate partly overlapping routes. The structure of the visual environment, however, crucially determines the success rate. We find that the model fails more often in visually rich environments due to the overlap of features represented by the Kenyon cell layer. Reducing the landmark density improves the agents route following performance. In very sparse environments, we find that extended landmarks, such as roads or field edges, may help the agent stay on its route, but often act as strong distractors yielding poor route following performance. We conclude that the presented model is valid for simple route following tasks and may represent one component of insect navigation. Additional components might still be necessary for guidance and action selection while navigating along different memorized routes in complex natural environments.

Note on the coefficient of variations of neuronal spike trains

It is known that many neurons in the brain show spike trains with a coefficient of variation (CV) of the interspike times of approximately 1, thus resembling the properties of Poisson spike trains. Computational studies have been able to reproduce this phenomenon. However, the underlying models were too complex to be examined analytically. In this paper, we offer a simple model that shows the same effect but is accessible to an analytic treatment. The model is a random walk model with a reflecting barrier; we give explicit formulas for the CV in the regime of excess inhibition. We also analyze the effect of probabilistic synapses in our model and show that it resembles previous findings that were obtained by simulation.

A dynamic architecture of life

In recent decades, a profound conceptual transformation has occurred comprising different areas of biological research, leading to a novel understanding of life processes as much more dynamic and changeable. Discoveries in plants and animals, as well as novel experimental approaches, have prompted the research community to reconsider established concepts and paradigms. This development was taken as an incentive to organise a workshop in May 2014 at the Academia Nazionale dei Lincei in Rome. There, experts on epigenetics, regeneration, neuroplasticity, and computational biology, using different animal and plant models, presented their insights on important aspects of a dynamic architecture of life, which comprises all organisational levels of the organism. Their work demonstrates that a dynamic nature of life persists during the entire existence of the organism and permits animals and plants not only to fine-tune their response to particular environmental demands during development, but underlies their continuous capacity to do so. Here, a synthesis of the different findings and their relevance for biological thinking is presented.