Single Neurons Research Articles

Electro-hydraulic servo position control of bending machine based on Single Neuron PID

Abstract Due to the fixed parameters, inadequate dynamic performance, and significant tracking error of the traditional PID control algorithm, it is challenging to satisfy the increasingly stringent precision demands for position control of the slider in the hydraulic servo system of the bending machine. Given the significant impact of external load on the position accuracy of the bending machine’s slider, a single neuron network is employed to dynamically adjust parameters in response to external load interference, thereby compensating for the limitations of traditional algorithms. Consequently, this paper proposes the design of a single neuron PID (SN-PID) controller to enhance system control accuracy. Through multiple simulation comparisons, it is evident that the SN-PID controller system exhibits superior anti-interference capabilities, enhanced adaptability, and reduced following error compared to traditional PID control algorithms in achieving slider position accuracy.

Deep learning-based automated bone age estimation for Saudi patients on hand radiograph images: a retrospective study

PurposeIn pediatric medicine, precise estimation of bone age is essential for skeletal maturity evaluation, growth disorder diagnosis, and therapeutic intervention planning. Conventional techniques for determining bone age depend on radiologists’ subjective judgments, which may lead to non-negligible differences in the estimated bone age. This study proposes a deep learning-based model utilizing a fully connected convolutional neural network(CNN) to predict bone age from left-hand radiographs.MethodsThe data set used in this study, consisting of 473 patients, was retrospectively retrieved from the PACS (Picture Achieving and Communication System) of a single institution. We developed a fully connected CNN consisting of four convolutional blocks, three fully connected layers, and a single neuron as output. The model was trained and validated on 80% of the data using the mean-squared error as a cost function to minimize the difference between the predicted and reference bone age values through the Adam optimization algorithm. Data augmentation was applied to the training and validation sets yielded in doubling the data samples. The performance of the trained model was evaluated on a test data set (20%) using various metrics including, the mean absolute error (MAE), median absolute error (MedAE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). The code of the developed model for predicting the bone age in this study is available publicly on GitHub at https://github.com/afiosman/deep-learning-based-bone-age-estimation.ResultsExperimental results demonstrate the sound capabilities of our model in predicting the bone age on the left-hand radiographs as in the majority of the cases, the predicted bone ages and reference bone ages are nearly close to each other with a calculated MAE of 2.3 [1.9, 2.7; 0.95 confidence level] years, MedAE of 2.1 years, RMAE of 3.0 [1.5, 4.5; 0.95 confidence level] years, and MAPE of 0.29 (29%) on the test data set.ConclusionThese findings highlight the usability of estimating the bone age from left-hand radiographs, helping radiologists to verify their own results considering the margin of error on the model. The performance of our proposed model could be improved with additional refining and validation.

Irrational choices via a curvilinear representational geometry for value

We make decisions by comparing values, but it is not yet clear how value is represented in the brain. Many models assume, if only implicitly, that the representational geometry of value is linear. However, in part due to a historical focus on noisy single neurons, rather than neuronal populations, this hypothesis has not been rigorously tested. Here, we examine the representational geometry of value in the ventromedial prefrontal cortex (vmPFC), a part of the brain linked to economic decision-making, in two male rhesus macaques. We find that values are encoded along a curved manifold in vmPFC. This curvilinear geometry predicts a specific pattern of irrational decision-making: that decision-makers will make worse choices when an irrelevant, decoy option is worse in value, compared to when it is better. We observe this type of irrational choices in behavior. Together, these results not only suggest that the representational geometry of value is nonlinear, but that this nonlinearity could impose bounds on rational decision-making.

Single-mitochondrion sequencing uncovers distinct mutational patterns and heteroplasmy landscape in mouse astrocytes and neurons

BackgroundMitochondrial (mt) heteroplasmy can cause adverse biological consequences when deleterious mtDNA mutations accumulate disrupting “normal” mt-driven processes and cellular functions. To investigate the heteroplasmy of such mtDNA changes, we developed a moderate throughput mt isolation procedure to quantify the mt single-nucleotide variant (SNV) landscape in individual mouse neurons and astrocytes. In this study, we amplified mt-genomes from 1645 single mitochondria isolated from mouse single astrocytes and neurons to (1) determine the distribution and proportion of mt-SNVs as well as mutation pattern in specific target regions across the mt-genome, (2) assess differences in mtDNA SNVs between neurons and astrocytes, and (3) study co-segregation of variants in the mouse mtDNA.Results(1) The data show that specific sites of the mt-genome are permissive to SNV presentation while others appear to be under stringent purifying selection. Nested hierarchical analysis at the levels of mitochondrion, cell, and mouse reveals distinct patterns of inter- and intra-cellular variation for mt-SNVs at different sites. (2) Further, differences in the SNV incidence were observed between mouse neurons and astrocytes for two mt-SNV 9027:G > A and 9419:C > T showing variation in the mutational propensity between these cell types. Purifying selection was observed in neurons as shown by the Ka/Ks statistic, suggesting that neurons are under stronger evolutionary constraint as compared to astrocytes. (3) Intriguingly, these data show strong linkage between the SNV sites at nucleotide positions 9027 and 9461.ConclusionsThis study suggests that segregation as well as clonal expansion of mt-SNVs is specific to individual genomic loci, which is important foundational data in understanding of heteroplasmy and disease thresholds for mutation of pathogenic variants.

Drosophila HCN mediates gustatory homeostasis by preserving sensillar transepithelial potential in sweet environments.

Establishing transepithelial ion disparities is crucial for sensory functions in animals. In insect sensory organs called sensilla, a transepithelial potential, known as the sensillum potential (SP), arises through active ion transport across accessory cells, sensitizing receptor neurons such as mechanoreceptors and chemoreceptors. Because multiple receptor neurons are often co-housed in a sensillum and share SP, niche-prevalent overstimulation of single sensory neurons can compromise neighboring receptors by depleting SP. However, how such potential depletion is prevented to maintain sensory homeostasis remains unknown. Here, we find that the Ih-encoded hyperpolarization-activated cyclic nucleotide-gated (HCN) channel bolsters the activity of bitter-sensing gustatory receptor neurons (bGRNs), albeit acting in sweet-sensing GRNs (sGRNs). For this task, HCN maintains SP despite prolonged sGRN stimulation induced by the diet mimicking their sweet feeding niche, such as overripe fruit. We present evidence that Ih-dependent demarcation of sGRN excitability is implemented to throttle SP consumption, which may have facilitated adaptation to a sweetness-dominated environment. Thus, HCN expressed in sGRNs serves as a key component of a simple yet versatile peripheral coding that regulates bitterness for optimal food intake in two contrasting ways: sweet-resilient preservation of bitter aversion and the previously reported sweet-dependent suppression of bitter taste.

Chromatin plasticity predetermines neuronal eligibility for memory trace formation.

Memories are encoded by sparse populations of neurons but how such sparsity arises remains largely unknown. We found that a neuron's eligibility to be recruited into the memory trace depends on its epigenetic state prior to encoding. Principal neurons in the mouse lateral amygdala display intrinsic chromatin plasticity, which when experimentally elevated favors neuronal allocation into the encoding ensemble. Such chromatin plasticity occurred at genomic regions underlying synaptic plasticity and was accompanied by increased neuronal excitability in single neurons in real time. Lastly, optogenetic silencing of the epigenetically altered neurons prevented memory expression, revealing a cell-autonomous relationship between chromatin plasticity and memory trace formation. These results identify the epigenetic state of a neuron as a key factor enabling information encoding.

Dynamic spatial representation of self and others’ actions in the macaque frontal cortex

Modulation of neuronal firing rates by the spatial locations of physical objects is a widespread phenomenon in the brain. However, little is known about how neuronal responses to the actions of biological entities are spatially tuned and whether such spatially tuned responses are affected by social contexts. These issues are of key importance for understanding the neural basis of embodied social cognition, such as imitation and perspective-taking. Here, we show that spatial representation of actions can be dynamically changed depending on others' social relevance and agents of action. Monkeys performed a turn-taking choice task with a real monkey partner sitting face-to-face or a filmed partner in prerecorded videos. Three rectangular buttons (left, center, and right) were positioned in front of the subject and partner as their choice targets. We recorded from single neurons in two frontal nodes in the social brain, the ventral premotor cortex (PMv) and the medial prefrontal cortex (MPFC). When the partner was filmed rather than real, spatial preference for partner-actions was markedly diminished in MPFC, but not PMv, neurons. This social context-dependent modulation in the MPFC was also evident for self-actions. Strikingly, a subset of neurons in both areas switched their spatial preference between self-actions and partner-actions in a diametrically opposite manner. This observation suggests that these cortical areas are associated with coordinate transformation in ways consistent with an actor-centered perspective-taking coding scheme. The PMv may subserve such functions in context-independent manners, whereas the MPFC may do so primarily in social contexts.

Mathematical Modeling of Neuron Using Hodgkin-Huxley Model

The growing rate of mental illnesses has brought the idea to cure anxiety disorders through altering heart rate variability using sympathetic and parasympathetic stimulations. The parasympathetic activity in the sinoatrial node being responsible for arrythmia has been considered in the already existing cardiovascular model. This study presents a mathematical model of a single neuron and simulated its activity for a single burst to be amalgamated in a closed-loop system of the heart and uses heart rate variability as a control variable to treat anxiety disorders and decrease the rate of increasing mental disorders in the world population. The proposed study shows the results that rises from 7mV and goes up to 107mV and then again falls back to 7mV depicting a single burst. These results is being validated by comparing the model of neuron presented in classical Hodgkin-Huxley model using MATLAB.

Integration of valence and conflict processing through cellular-field interactions in human subgenual cingulate during emotional face processing in treatment-resistant depression.

The subgenual anterior cingulate cortex (sgACC) has been identified as a key brain area involved in various cognitive and emotional processes. While the sgACC has been implicated in both emotional valuation and emotional conflict monitoring, it is still unclear how this area integrates multiple functions. We characterized both single neuron and local field oscillatory activity in 14 patients undergoing sgACC deep brain stimulation for treatment-resistant depression. During recording, patients were presented with a modified Stroop task containing emotional face images that varied in valence and congruence. We further analyzed spike-field interactions to understand how network dynamics influence single neuron activity in this area. Most single neurons responded to both valence and congruence, revealing that sgACC neuronal activity can encode multiple processes within the same task, indicative of multifunctionality. During peak neuronal response, we observed increased spectral power in low frequency oscillations, including theta-band synchronization (4-8 Hz), as well as desynchronization in beta-band frequencies (13-30 Hz). Theta activity was modulated by current trial congruency with greater increases in spectral power following non-congruent stimuli, while beta desynchronizations occurred regardless of emotional valence. Spike-field interactions revealed that local sgACC spiking was phase-locked most prominently to the beta band, whereas phase-locking to the theta band occurred in fewer neurons overall but was modulated more strongly for neurons that were responsive to task. Our findings provide the first direct evidence of spike-field interactions relating to emotional cognitive processing in the human sgACC. Furthermore, we directly related theta oscillatory dynamics in human sgACC to current trial congruency, demonstrating it as an important regulator during conflict detection. Our data endorse the sgACC as an integrative hub for cognitive emotional processing through modulation of beta and theta network activity.

Myelin dystrophy impairs signal transmission and working memory in a multiscale model of the aging prefrontal cortex.

Normal aging leads to myelin alterations in the rhesus monkey dorsolateral prefrontal cortex (dlPFC), which are positively correlated with degree of cognitive impairment. It is hypothesized that remyelination with shorter and thinner myelin sheaths partially compensates for myelin degradation, but computational modeling has not yet explored these two phenomena together systematically. Here, we used a two-pronged modeling approach to determine how age-related myelin changes affect a core cognitive function: spatial working memory. First, we built a multicompartment pyramidal neuron model fit to monkey dlPFC empirical data, with an axon including myelinated segments having paranodes, juxtaparanodes, internodes, and tight junctions. This model was used to quantify conduction velocity (CV) changes and action potential (AP) failures after demyelination and subsequent remyelination. Next, we incorporated the single neuron results into a spiking neural network model of working memory. While complete remyelination nearly recovered axonal transmission and network function to unperturbed levels, our models predict that biologically plausible levels of myelin dystrophy, if uncompensated by other factors, can account for substantial working memory impairment with aging. The present computational study unites empirical data from ultrastructure up to behavior during normal aging, and has broader implications for many demyelinating conditions, such as multiple sclerosis or schizophrenia.

Two-layered microwell-array device for preparation of single-neuron culture samples

When a single neuron is cultured in isolation from other neurons, its axon connects with its own dendrites to form a simple, independent network with no synaptic inputs from other neurons. This culture system enables detailed analysis of synaptic function and morphology change in neurites at the single-neuron level, which is useful for elucidating the pathogenesis of neurological diseases and for evaluating the efficacy of therapeutic drugs for them. However, there was previously no device technology capable of simultaneously forming multiple single-neuron samples while allowing co-culture with astrocytes, which is essential for culture of a single neuron isolated from other neurons. In this study, we propose a novel microwell-array device for preparing single-neuron samples. The device consists of an upper layer for cell seeding and a lower layer for cell culture. Each layer has 16 × 16 microwells, and the bottom of each well is made of a 1 μm thick silicon nitride membrane. The membrane of the upper well has one microhole for seeding a single neuron, and the lower membrane has multiple microholes for interaction between a single neuron and astrocytes which are co-cultured back-to-back on both sides of the membrane. When neurons are seeded into the upper well, only one of them passes through the microhole in the upper membrane and falls onto the lower membrane. We evaluated a seeding efficiency of single neurons by changing seeding hole diameter and seeding density. The results showed that the yield of more than 20% was obtained regardless of the seeding density when the seeding hole diameter was 13 μm. We also confirmed that single neurons seeded in this manner and co-cultured with astrocytes developed neurites and formed synapses. These results demonstrated the usefulness of this device for the preparation of single-neuron culture samples.

Persistent activity during working memory maintenance predicts long-term memory formation in the human hippocampus.

Working Memory (WM) and Long-Term Memory (LTM) are often viewed as separate cognitive systems. Little is known about how these systems interact when forming memories. We recorded single neurons in the human medial temporal lobe while patients maintained novel items in WM and a subsequent recognition memory test for the same items. In the hippocampus but not the amygdala, the level of WM content-selective persist activity during WM maintenance was predictive of whether the item was later recognized with high confidence or forgotten. In contrast, visually evoked activity in the same cells was not predictive of LTM formation. During LTM retrieval, memory-selective neurons responded more strongly to familiar stimuli for which persistent activity was high while they were maintained in WM. Our study suggests that hippocampal persistent activity of the same cell supports both WM maintenance and LTM encoding, thereby revealing a common single-neuron component of these two memory systems.

Detection of Memory Engrams in Mammalian Neuronal Circuits.

It has long been assumed that activity patterns persist in neuronal circuits after they are first experienced, as part of the process of information processing and storage by the brain. However, these "reverberations" of current activity have not been directly observed on a single-neuron level in a mammalian system. Here we demonstrate that specific induced activity patterns are retained in mature cultured hippocampal neuronal networks. Neurons within the network are induced to fire at a single frequency or in a more complex pattern containing two distinct frequencies. After the stimulation was stopped, the subsequent neuronal activity of hundreds of neurons in the network was monitored. In the case of single-frequency stimulation, it was observed that many of the neurons continue to fire at the same frequency that they were stimulated to fire at. Using a recurrent neural network trained to detect specific, more complex patterns, we found that the multiple-frequency stimulation patterns were also retained within the neuronal network. Moreover, it appears that the component frequencies of the more complex patterns are stored in different populations of neurons and neuron subtypes.

Dissociable Representations of Decision Variables within Subdivisions of the Macaque Orbital and Ventrolateral Frontal Cortex.

The ventral frontal cortex (VFC) in macaques is involved in many affective and cognitive processes and has a key role in flexibly guiding reward-based decision-making. VFC is composed of a set of anatomically distinct subdivisions that are within the orbitofrontal cortex, ventrolateral prefrontal cortex, and anterior insula. In part, because prior studies have lacked the resolution to test for differences, it is unclear if neural representations related to decision-making are dissociable across these subdivisions. Here we recorded the activity of thousands of neurons within eight anatomically defined subdivisions of VFC in male macaque monkeys performing a two-choice probabilistic task for different fruit juice outcomes. We found substantial variation in the encoding of decision variables across these eight subdivisions. Notably, ventrolateral Area 12l was unique relative to the other areas that we recorded from as the activity of single neurons integrated multiple attributes when monkeys evaluated the different choice options. Activity within Area 12o, in contrast, more closely represented reward probability and whether reward was received on a given trial. Orbitofrontal Area 11m/l contained more specific representations of the quality of the outcome that could be earned later on. We also found that reward delivery encoding was highly distributed across all VFC subdivisions, while the properties of the reward, such as its flavor, were more strongly represented in Areas 11m/l and 13m. Taken together, our work reveals the diversity of encoding within the various anatomically distinct subdivisions of VFC in primates.

Structured connectivity in the output of the cerebellar cortex

The spatial organization of a neuronal circuit is critically important for its function since the location of neurons is often associated with function. In the cerebellum, the major output of the cerebellar cortex are synapses made from Purkinje cells onto neurons in the cerebellar nuclei, yet little has been known about the spatial organization of these synapses. We explored this question using whole-cell electrophysiology and optogenetics in acute sagittal cerebellar slices to produce spatial connectivity maps of cerebellar cortical output in mice. We observed non-random connectivity where Purkinje cell inputs clustered in cerebellar transverse zones: while many nuclear neurons received inputs from a single zone, several multi-zonal connectivity motifs were also observed. Single neurons receiving input from all four zones were overrepresented in our data. These findings reveal that the output of the cerebellar cortex is spatially structured and represents a locus for multimodal integration in the cerebellum.

Decision-making shapes dynamic inter-areal communication within macaque ventral frontal cortex.

Macaque ventral frontal cortex is comprised of a set of anatomically heterogeneous and highly interconnected areas. Collectively these areas have been implicated in many higher-level affective and cognitive processes, most notably the adaptive control of decision-making. Despite this appreciation, little is known about how subdivisions of ventral frontal cortex dynamically interact with each other during decision-making. Here we assessed functional interactions between areas by analyzing the activity of thousands of single neurons recorded from eight anatomically defined subdivisions of ventral frontal cortex in macaques performing a visually guided two-choice probabilistic task for different fruit juices. We found that the onset of stimuli and reward delivery globally increased communication between all parts of ventral frontal cortex. Inter-areal communication was, however, temporally specific, occurred through unique activity subspaces between areas, and depended on the encoding of decision variables. In particular, areas 12l and 12o showed the highest connectivity with other areas while being more likely to receive information from other parts of ventral frontal cortex than to send it. This pattern of functional connectivity suggests a role for these two areas in integrating diverse sources of information during decision processes. Taken together, our work reveals the specific patterns of interareal communication between anatomically connected subdivisions of ventral frontal cortex that are dynamically engaged during decision-making.

Silicon Optrode with a Micromirror‐Tip Providing Tunable Beam Profile During Infrared Neuromodulation of the Rat Neocortex

AbstractInfrared (IR) neuromodulation holds an increasing potential in brain research, which is fueled by novel neuroengineering approaches facilitating the exploration of the biophysical mechanism in the microscale. The group lays down the fundamentals of spatially controlled optical manipulation of inherently temperature‐sensitive neuronal populations. The concept and in vivo validation of a multifunctional, optical stimulation microdevice is presented, which expands the capabilities of conventional optrodes by coupling IR light through a monolithically integrated parabolic micromirror. Heat distribution in the irradiated volume is experimentally analyzed, and the performance of the integrated electrophysiological recording components of the device is tested in the neocortex of anesthetized rodents. Evoked single‐cell responses upon IR irradiation through the novel microtool are evaluated in multiple trials. The safe operation of the implanted device is also presented using immunohistological methods. The results confirm that shift in temperature distribution in the vicinity of the optrode tip can be controlled by the integrated photonic components, and in parallel with the optical stimulation, the device is suitable to interrogate the evoked electrophysiological activity at the single neuron level. The customizable and scalable optrode system provides a new pathway to tailor the location of the heat maximum during infrared neural stimulation.

Nonlinear fusion is optimal for a wide class of multisensory tasks.

Animals continuously detect information via multiple sensory channels, like vision and hearing, and integrate these signals to realise faster and more accurate decisions; a fundamental neural computation known as multisensory integration. A widespread view of this process is that multimodal neurons linearly fuse information across sensory channels. However, does linear fusion generalise beyond the classical tasks used to explore multisensory integration? Here, we develop novel multisensory tasks, which focus on the underlying statistical relationships between channels, and deploy models at three levels of abstraction: from probabilistic ideal observers to artificial and spiking neural networks. Using these models, we demonstrate that when the information provided by different channels is not independent, linear fusion performs sub-optimally and even fails in extreme cases. This leads us to propose a simple nonlinear algorithm for multisensory integration which is compatible with our current knowledge of multimodal circuits, excels in naturalistic settings and is optimal for a wide class of multisensory tasks. Thus, our work emphasises the role of nonlinear fusion in multisensory integration, and provides testable hypotheses for the field to explore at multiple levels: from single neurons to behaviour.

A pH-sensitive closed-loop nanomachine to control hyperexcitability at the single neuron level

Epilepsy affects 1% of the general population and 30% of patients are resistant to antiepileptic drugs. Although optogenetics is an efficient antiepileptic strategy, the difficulty of illuminating deep brain areas poses translational challenges. Thus, the search of alternative light sources is strongly needed. Here, we develop pH-sensitive inhibitory luminopsin (pHIL), a closed-loop chemo-optogenetic nanomachine composed of a luciferase-based light generator, a fluorescent sensor of intracellular pH (E2GFP), and an optogenetic actuator (halorhodopsin) for silencing neuronal activity. Stimulated by coelenterazine, pHIL experiences bioluminescence resonance energy transfer between luciferase and E2GFP which, under conditions of acidic pH, activates halorhodopsin. In primary neurons, pHIL senses the intracellular pH drop associated with hyperactivity and optogenetically aborts paroxysmal activity elicited by the administration of convulsants. The expression of pHIL in hippocampal pyramidal neurons is effective in decreasing duration and increasing latency of pilocarpine-induced tonic-clonic seizures upon in vivo coelenterazine administration, without affecting higher brain functions. The same treatment is effective in markedly decreasing seizure manifestations in a murine model of genetic epilepsy. The results indicate that pHIL represents a potentially promising closed-loop chemo-optogenetic strategy to treat drug-refractory epilepsy.

Comparative basolateral amygdala connectomics reveals dissociable single-neuron projection patterns to frontal cortex in macaques and mice

Basolateral amygdala (BLA) is a key hub for affect in the brain,1,2,3 and dysfunction within this area contributes to a host of psychiatric disorders.4,5 BLA is extensively and reciprocally interconnected with frontal cortex,6,7,8,9 and some aspects of its function are evolutionarily conserved across rodents, anthropoid primates, and humans.10 Neuron density in BLA is substantially lower in primates compared to murine rodents,11 and frontal cortex (FC) is dramatically expanded in primates, particularly the more anterior granular and dysgranular areas.12,13,14 Yet, how these anatomical differences influence the projection patterns of single BLA neurons to frontal cortex across rodents and primates is unknown. Using a barcoded connectomic approach, we assessed the single BLA neuron connections to frontal cortex in mice and macaques. We found that BLA neurons are more likely to project to multiple distinct parts of FC in mice than in macaques. Further, while single BLA neuron projections to nucleus accumbens were similarly organized in mice and macaques, BLA-FC connections differed substantially. Notably, BLA connections to subcallosal anterior cingulate cortex (scACC) in macaques were least likely to branch to other medial frontal cortex areas compared to perigenual ACC (pgACC). This pattern of connections was reversed in the mouse homologues of these areas, infralimbic and prelimbic cortex (IL and PL), mirroring functional differences between rodents and non-human primates. Taken together, these results indicate that BLA connections to FC are not linearly scaled from mice to macaques and instead the organization of single-neuron BLA connections is distinct between these species.