Pairs Of Neurons Research Articles

The doublesex gene regulates dimorphic sexual and aggressive behaviors in Drosophila

Most animal species display dimorphic sexual behaviors and male-biased aggressiveness. Current models have focused on the male-specific product from the fruitless (fruM) gene, which controls male courtship and male-specific aggression patterns in fruit flies, and describe a male-specific mechanism underlying sexually dimorphic behaviors. Here we show that the doublesex (dsx) gene, which expresses male-specific DsxM and female-specific DsxF transcription factors, functions in the nervous system to control both male and female sexual and aggressive behaviors. We find that Dsx is not only required in central brain neurons for male and female sexual behaviors, but also functions in approximately eight pairs of male-specific neurons to promote male aggressiveness and approximately two pairs of female-specific neurons to inhibit female aggressiveness. DsxF knockdown females fight more frequently, even with males. Our findings reveal crucial roles of dsx, which is broadly conserved from worms to humans, in a small number of neurons in both sexes to establish dimorphic sexual and aggressive behaviors.

Emergence of local irreversibility in complex interacting systems.

Living systems are fundamentally irreversible, breaking detailed balance and establishing an arrow of time. But how does the evident arrow of time for a whole system arise from the interactions among its multiple elements? We show that the local evidence for the arrow of time, which is the entropy production for thermodynamic systems, can be decomposed. First, it can be split into two components: an independent term reflecting the dynamics of individual elements and an interaction term driven by the dependencies among elements. Adapting tools from nonequilibrium physics, we further decompose the interaction term into contributions from pairs of elements, triplets, and higher-order terms. We illustrate our methods on models of cellular sensing and logical computations, as well as on patterns of neural activity in the retina as it responds to visual inputs. We find that neural activity can define the arrow of time even when the visual inputs do not, and that the dominant contribution to this breaking of detailed balance comes from interactions among pairs of neurons.

Decomposing the Local Arrow of Time in Interacting Systems.

We show that the evidence for a local arrow of time, which is equivalent to the entropy production in thermodynamic systems, can be decomposed. In a system with many degrees of freedom, there is a term that arises from the irreversible dynamics of the individual variables, and then a series of non-negative terms contributed by correlations among pairs, triplets, and higher-order combinations of variables. We illustrate this decomposition on simple models of noisy logical computations, and then apply it to the analysis of patterns of neural activity in the retina as it responds to complex dynamic visual scenes. We find that neural activity breaks detailed balance even when the visual inputs do not, and that this irreversibility arises primarily from interactions between pairs of neurons.

Sustained deep-tissue voltage recording using a fast indicator evolved for two-photon microscopy

SUMMARYGenetically encoded voltage indicators are emerging tools for monitoring voltage dynamics with cell-type specificity. However, current indicators enable a narrow range of applications due to poor performance under two-photon microscopy, a method of choice for deep-tissue recording. To improve indicators, we developed a multiparameter high-throughput platform to optimize voltage indicators for two-photon microscopy. Using this system, we identified JEDI-2P, an indicator that is faster, brighter, and more sensitive and photostable than its predecessors. We demonstrate that JEDI-2P can report light-evoked responses in axonal termini of Drosophila interneurons and the dendrites and somata of amacrine cells of isolated mouse retina. JEDI-2P can also optically record the voltage dynamics of individual cortical neurons in awake behaving mice for more than 30 min using both resonant-scanning and ULoVE random-access microscopy. Finally, ULoVE recording of JEDI-2P can robustly detect spikes at depths exceeding 400 μm and report voltage correlations in pairs of neurons.

Lateral predictive coding revisited: internal model, symmetry breaking, and response time

Predictive coding is a promising theoretical framework in neuroscience for understanding information transmission and perception. It posits that the brain perceives the external world through internal models and updates these models under the guidance of prediction errors. Previous studies on predictive coding emphasized top-down feedback interactions in hierarchical multilayered networks but largely ignored lateral recurrent interactions. We perform analytical and numerical investigations in this work on the effects of single-layer lateral interactions. We consider a simple predictive response dynamics and run it on the MNIST dataset of hand-written digits. We find that learning will generally break the interaction symmetry between peer neurons, and that high input correlation between two neurons does not necessarily bring strong direct interactions between them. The optimized network responds to familiar input signals much faster than to novel or random inputs, and it significantly reduces the correlations between the output states of pairs of neurons.

MiR‐124‐dependent tagging of synapses by synaptopodin enables input‐specific homeostatic plasticity

Homeostatic synaptic plasticity is a process by which neurons adjust their synaptic strength to compensate for perturbations in neuronal activity. Whether the highly diverse synapses on a neuron respond uniformly to the same perturbation remains unclear. Moreover, the molecular determinants that underlie synapse‐specific homeostatic synaptic plasticity are unknown. Here, we report a synaptic tagging mechanism in which the ability of individual synapses to increase their strength in response to activity deprivation depends on the local expression of the spine‐apparatus protein synaptopodin under the regulation of miR‐124. Using genetic manipulations to alter synaptopodin expression or regulation by miR‐124, we show that synaptopodin behaves as a “postsynaptic tag” whose translation is derepressed in a subpopulation of synapses and allows for nonuniform homeostatic strengthening and synaptic AMPA receptor stabilization. By genetically silencing individual connections in pairs of neurons, we demonstrate that this process operates in an input‐specific manner. Overall, our study shifts the current view that homeostatic synaptic plasticity affects all synapses uniformly to a more complex paradigm where the ability of individual synapses to undergo homeostatic changes depends on their own functional and biochemical state.

Hippocampal signals modify orbitofrontal representations to learn new paths

We often remember the consequences of past choices to adapt to changing circumstances. Recalling past events requires the hippocampus (HPC), and using stimuli to anticipate outcome values requires the orbitofrontal cortex (OFC).1-3 Spatial reversal tasks require both structures to navigate newly rewarded paths.4,5 Both HPC place6 and OFC value cells7,8 fire in phase with theta (4-12Hz) oscillations. Both structures are described as cognitive maps: HPC maps space9 and OFC maps task states.10 These similarities imply that OFC-HPC interactions are crucial for using memory to predict outcomes when circumstances change, but the mechanisms remain largely unknown. To investigate possible interactions, we simultaneously recorded ensembles in OFC and CA1 as rats learned spatial reversals in a plus maze. Striking interactions occurred only while rats learned their first reversal: CA1 population vectors predicted changes in OFC activity but not vice versa, OFC spikes phase locked to hippocampal theta oscillations, mixed pairs of CA1 and OFC neurons fired together within single theta cycles, and CA1 led OFC spikes by ∼30ms. After the new contingency became familiar, CA1 ensembles stably represented distinct spatial paths, whereas OFC ensembles developed more generalized goal arm representations in different paths to identical rewards. These frontotemporal interactions, engaged selectively when new task features inform decision-making, suggest a mechanism for linking novel episodes with expected outcomes, when HPC signals trigger "cognitive remapping" by OFC.11.

Spatial Attention Modulates Spike Count Correlations and Granger Causality in the Primary Visual Cortex.

The influence of spatial attention on neural interactions has been revealed even in early visual information processing stages. It resolves the process of competing for sensory information about objects perceived as targets and distractors. However, the attentional modulation of the interaction between pairs of neurons with non-overlapping receptive fields (RFs) is not well known. Here, we investigated the activity of anatomically distant neurons in two behaving monkeys’ primary visual cortex (V1), when they performed a spatial attention task detecting color change. We compared attentional modulation from the perspective of spike count correlations and Granger causality among simple and complex cells. An attention-related increase in spike count correlations and a decrease in Granger causality were found. The results showed that spatial attention significantly influenced only the interactions between rather than within simple and complex cells. Furthermore, we found that the attentional modulation of neuronal interactions changed with neuronal pairs’ preferred directions differences. Thus, we found that spatial attention increased the functional communications and competing connectivities when attending to the neurons’ RFs, which impacts the interactions only between simple and complex cells. Our findings enrich the model of simple and complex cells and further understand the way that attention influences the neurons’ activities.

Context-dependent reversal of odorant preference is driven by inversion of the response in a single sensory neuron type.

The valence and salience of individual odorants are modulated by an animal's innate preferences, learned associations, and internal state, as well as by the context of odorant presentation. The mechanisms underlying context-dependent flexibility in odor valence are not fully understood. Here, we show that the behavioral response of Caenorhabditis elegans to bacterially produced medium-chain alcohols switches from attraction to avoidance when presented in the background of a subset of additional attractive chemicals. This context-dependent reversal of odorant preference is driven by cell-autonomous inversion of the response to these alcohols in the single AWC olfactory neuron pair. We find that while medium-chain alcohols inhibit the AWC olfactory neurons to drive attraction, these alcohols instead activate AWC to promote avoidance when presented in the background of a second AWC-sensed odorant. We show that these opposing responses are driven via engagement of distinct odorant-directed signal transduction pathways within AWC. Our results indicate that context-dependent recruitment of alternative intracellular signaling pathways within a single sensory neuron type conveys opposite hedonic valences, thereby providing a robust mechanism for odorant encoding and discrimination at the periphery.

Maximum entropy models provide functional connectivity estimates in neural networks

Tools to estimate brain connectivity offer the potential to enhance our understanding of brain functioning. The behavior of neuronal networks, including functional connectivity and induced connectivity changes by external stimuli, can be studied using models of cultured neurons. Cultured neurons tend to be active in groups, and pairs of neurons are said to be functionally connected when their firing patterns show significant synchronicity. Methods to infer functional connections are often based on pair-wise cross-correlation between activity patterns of (small groups of) neurons. However, these methods are not very sensitive to detect inhibitory connections, and they were not designed for use during stimulation. Maximum Entropy (MaxEnt) models may provide a conceptually different method to infer functional connectivity. They have the potential benefit to estimate functional connectivity during stimulation, and to infer excitatory as well as inhibitory connections. MaxEnt models do not involve pairwise comparison, but aim to capture probability distributions of sets of neurons that are synchronously active in discrete time bins. We used electrophysiological recordings from in vitro neuronal cultures on micro electrode arrays to investigate the ability of MaxEnt models to infer functional connectivity. Connectivity estimates provided by MaxEnt models correlated well with those obtained by conditional firing probabilities (CFP), an established cross-correlation based method. In addition, stimulus-induced connectivity changes were detected by MaxEnt models, and were of the same magnitude as those detected by CFP. Thus, MaxEnt models provide a potentially powerful new tool to study functional connectivity in neuronal networks.

Functional Dissection of Protein Kinases in Sexual Development and Female Receptivity of Drosophila.

Protein phosphorylation is crucial for a variety of biological functions, but how it is involved in sexual development and behavior is rarely known. In this study, we performed a screen of RNA interference targeting 177 protein kinases in Drosophila and identified 13 kinases involved in sexual development in one or both sexes. We further identified that PKA and CASK promote female sexual behavior while not affecting female differentiation. Knocking down PKA or CASK in about five pairs of pC1 neurons in the central brain affects the fine projection but not cell number of these pC1 neurons and reduces virgin female receptivity. We also found that PKA and CASK signaling is required acutely during adulthood to promote female sexual behavior. These results reveal candidate kinases required for sexual development and behaviors and provide insights into how kinases would regulate neuronal development and physiology to fine tune the robustness of sexual behaviors.

An in silico model for determining the influence of neuronal co-activity on rodent spatial behavior

BackgroundNeuropsychological and neurophysiological analyses focus on understanding how neuronal activity and co-activity predict behavior. Experimental techniques allow for modulation of neuronal activity, but do not control neuronal ensemble spatiotemporal firing patterns, and there are few, if any, sophisticated in silico techniques which accurately reconstruct physiological neural spike trains and behavior using unit co-activity as an input parameter. New MethodOur approach to simulation of neuronal spike trains is based on using state space modeling to estimate a weighted graph of interaction strengths between pairs of neurons along with separate estimations of spiking threshold voltage and neuronal membrane leakage. These parameters allow us to tune a biophysical model which is then employed to accurately reconstruct spike trains from freely behaving animals and then use these spike trains to estimate an animal’s spatial behavior. The reconstructed spatial behavior allows us to confirm the same information is present in both the recorded and simulated spike trains. ResultsOur method reconstructs spike trains (98 ± 0.0013% like original spike trains, mean ± SEM) and animal position (9.468 ± 0.240 cm, mean ± SEM) with high fidelity. Comparison with Existing Method(s)To our knowledge, this is the first method that uses empirically derived network connectivity to constrain biophysical parameters and predict spatial behavior. Together, these methods allow in silico quantification of the contribution of specific unit activity and co-activity to animal spatial behavior. ConclusionsOur novel approach provides a flexible, robust in silico technique for determining the contribution of specific neuronal activity and co-activity to spatial behavior.

Age-related memory vulnerability to interfering stimuli is caused by gradual loss of MAPK-dependent protection in Drosophila.

Age‐related memory impairment (AMI) is a common phenomenon across species. Vulnerability to interfering stimuli has been proposed to be an important cause of AMI. However, the molecular mechanisms underlying this vulnerability‐related AMI remain unknown. Here we show that learning‐activated MAPK signals are gradually lost with age, leading to vulnerability‐related AMI in Drosophila. Young flies (2‐ or 3‐day‐old) exhibited a significant increase in phosphorylated MAPK levels within 15 min after learning, whereas aged flies (25‐day‐old) did not. Compared to 3‐day‐old flies, significant 1 h memory impairments were observed in 15‐, 20‐, and 30‐day‐old flies, but not in 10‐day‐old flies. However, with post‐learning interfering stimuli such as cooling or electric stimuli, 10‐day‐old flies had worse memory performance at 1 h than 3‐day‐old flies, showing a premature AMI phenomenon. Increasing learning‐activated MAPK signals through acute transgene expression in mushroom body (MB) neurons restored physiological trace of 1 h memory in a pair of MB output neurons in aged flies. Decreasing such signals in young flies mimicked the impairment of 1 h memory trace in aged flies. Restoring learning‐activated MAPK signals in MB neurons in aged flies significantly suppressed AMI even with interfering stimuli. Thus, our data suggest that age‐related loss of learning‐activated neuronal MAPK signals causes memory vulnerability to interfering stimuli, thereby leading to AMI.

Making Artificial Brains: Components, Topology, and Optimization.

Making Artificial Brains: Components, Topology, and Optimization.

The impact of pitolisant, an H3 receptor antagonist/inverse agonist, on perirhinal cortex activity in individual neuron and neuronal population levels

Histamine is a neurotransmitter that modulates neuronal activity and regulates various brain functions. Histamine H3 receptor (H3R) antagonists/inverse agonists enhance its release in most brain regions, including the cerebral cortex, which improves learning and memory and exerts an antiepileptic effect. However, the mechanism underlying the effect of H3R antagonists/inverse agonists on cortical neuronal activity in vivo remains unclear. Here, we show the mechanism by which pitolisant, an H3R antagonist/inverse agonist, influenced perirhinal cortex (PRh) activity in individual neuron and neuronal population levels. We monitored neuronal activity in the PRh of freely moving mice using in vivo Ca2+ imaging through a miniaturized one-photon microscope. Pitolisant increased the activity of some PRh neurons while decreasing the activity of others without affecting the mean neuronal activity across neurons. Moreover, it increases neuron pairs with synchronous activity in excitatory-responsive neuronal populations. Furthermore, machine learning analysis revealed that pitolisant altered the neuronal population activity. The changes in the population activity were dependent on the neurons that were excited and inhibited by pitolisant treatment. These findings indicate that pitolisant influences the activity of a subset of PRh neurons by increasing the synchronous activity and modifying the population activity.

Recording Synaptic Transmission from Auditory Mixed Synapses on the Mauthner Cells of Developing Zebrafish.

The Mauthner cells are a pair of large reticulospinal neurons that organize sensory-evoked tail flip responses in fishes. An identifiable group of auditory “mixed” (electrical and chemical) synaptic contacts known as “Large Myelinated Club endings” on these cells have provided a valuable model for the study of synaptic transmission in the vertebrate brain. While most of studies were performed in adult fish, we describe here methods that make possible recording synaptic transmission from these contacts in developing zebrafish, a genetically tractable vertebrate species which is uniquely amenable for combining synaptic physiology with live imaging and behavioral analysis.

Trans-Seq maps a selective mammalian retinotectal synapse instructed by Nephronectin.

The mouse visual system serves as an accessible model to understand mammalian circuit wiring. Despite rich knowledge in retinal circuits, the long-range connectivity map from distinct retinal ganglion cell (RGC) types to diverse brain neuron types remains unknown. In this study, we developed an integrated approach, called Trans-Seq, to map RGCs to superior collicular (SC) circuits. Trans-Seq combines a fluorescent anterograde trans-synaptic tracer, consisting of codon-optimized wheat germ agglutinin fused to mCherry, with single-cell RNA sequencing. We used Trans-Seq to classify SC neuron types innervated by genetically defined RGC types and predicted a neuronal pair from αRGCs to Nephronectin-positive wide-field neurons (NPWFs). We validated this connection using genetic labeling, electrophysiology and retrograde tracing. We then used transcriptomic data from Trans-Seq to identify Nephronectin as a determinant for selective synaptic choice from αRGC to NPWFs via binding to Integrin α8β1. The Trans-Seq approach can be broadly applied for post-synaptic circuit discovery from genetically defined pre-synaptic neurons.

Effects of Intra‐arterial Fentanyl and Codeine on Medullary Respiratory Network Organization

The actions of mu‐opioid agonists to depress breathing are currently controversial, encompassing several proposed mechanisms that are focused on preferential effects in specific locations in the brainstem. We hypothesized that opioids have multiple actions at the respiratory network scale which include loss of synchrony and/or network dynamics. Experiments were conducted in 4 anesthetized, spontaneously breathing and 11 decerebrated, artificially ventilated, paralyzed and vagal intact cats. In spontaneously breathing animals, EMGs were recorded from diaphragm, parasternal, abdominal and upper airway muscles. In paralyzed animals, neurograms were recorded from phrenic and abdominal nerves. Fentanyl or codeine were administered via the vertebral artery (i.a.; fentanyl, 1‐10 ng/kg, codeine 1‐3000 μg/kg) while simultaneously recording from multiple medullary neurons. Analyses included assessment of autocorrelation histograms (ACH), significant features in cross correlation histograms (CCHs), and discharge identity (phenotype of firing frequency of a neuron during the breathing cycle). Qualitatively similar opioid‐induced changes in neuron firing rates, ACHs, CCHs, and discharge identity were observed during either fentanyl or codeine dose responses. Some neurons lost or gained excitability during the dose responses. ACHs of some neurons exhibited changes in spiking periodicity. Features in CCHs changed during administration of opioids including loss of some central or offset peaks. Detectability index (a metric of the magnitude of significant functional interactions) in some neuron pairs increased during opioid administration. Further, the discharge identity of many neurons changed during the dose responses with most of these changes occurring in neurons with tonic discharge during breathing. The extent of these changes strongly suggest that opioids induce significant changes in network organization including reduced synchrony and altered discharge identity of network members. These changes can occur at low doses of opioids that do not profoundly alter the breathing pattern.

Drosulfakinin signaling modulates female sexual receptivity in Drosophila

Female sexual behavior as an innate behavior is of prominent biological importance for survival and reproduction. However, molecular and circuit mechanisms underlying female sexual behavior is not well understood. Here, we identify the Cholecystokinin-like peptide Drosulfakinin (DSK) to promote female sexual behavior in Drosophila. Loss of DSK function reduces female receptivity while overexpressing DSK enhances female receptivity. We identify two pairs of Dsk-expressing neurons in the central brain to promote female receptivity. We find that the DSK peptide acts through one of its receptors, CCKLR-17D3, to modulate female receptivity. Manipulation of CCKLR-17D3 and its expressing neurons alters female receptivity. We further reveal that the two pairs of Dsk-expressing neurons receive input signal from pC1 neurons that integrate sex-related cues and mating status. These results demonstrate how a neuropeptide pathway interacts with a central neural node in the female sex circuitry to modulate sexual receptivity.

Energy analysis of bursting Hindmarsh-Rose neurons with time-delayed coupling

Mathematical modeling is an important tool to study the role of delay in neural systems and to evaluate its effects on the signaling activity of coupled neurons. Models for delayed neurons are often used to represent the dynamics of real neurons, but rarely to assess the energy required to maintain these dynamics. In this work, we address these questions from an energy perspective by considering a pair of Hindmarsh-Rose burst neurons coupled by reciprocal time-delayed coupling with electrical and chemical synapses. We examine the average energy consumption required to maintain cooperative behavior and quantify the contribution of synapses to total energy consumption. We show that unlike electrical coupling, where the time delay appears to reduce the instantaneous average relative weight of the synaptic contribution, in chemical coupling this average synaptic contribution appears to be much higher in delayed coupling than in instantaneous coupling, except at certain values of coupling strength where the instantaneous synaptic contribution is more important.