Mutual inhibition of lateral inhibition: a network motif for an elementary computation in the brain

A competitive disinhibitory network for robust optic flow processing in Drosophila

Many animals navigate using optic flow, detecting rotational image velocity differences between their eyes to adjust direction. Forward locomotion produces strong symmetric translational optic flow that can mask these differences, yet the brain efficiently extracts these binocular asymmetries for course control. In Drosophila melanogaster, monocular horizontal system neurons facilitate detection of binocular asymmetries and contribute to steering. To understand these functions, we reconstructed horizontal system cells' central network using electron microscopy datasets, revealing convergent visual inputs, a recurrent inhibitory middle layer and a divergent output layer projecting to the ventral nerve cord and deeper brain regions. Two-photon imaging, GABA receptor manipulations and modeling, showed that lateral disinhibition reduces the output's translational sensitivity while enhancing its rotational selectivity. Unilateral manipulations confirmed the role of interneurons and descending outputs in steering. These findings establish competitive disinhibition as a key circuit mechanism for detecting rotational motion during translation, supporting navigation in dynamic environments.

Augmenting flexibility: mutual inhibition between inhibitory neurons expands functional diversity

Recent advances in microcircuit analysis of nervous systems have revealed a plethora of mutual connections between inhibitory interneurons across many different species and brain regions. The abundance of these mutual connections has not been fully explained. Strikingly, we show that neural circuits with mutually inhibitory connections are able to rapidly and flexibly switch between distinct functions. That is, multiple functions coexist for a single set of synaptic weights. Here, we develop a theoretical framework to explain how inhibitory recurrent circuits give rise to this flexibility and show that mutual inhibition doubles the number of cusp bifurcations in small neural circuits. As a concrete example, we study a class of functional motifs we call coupled recurrent inhibitory and recurrent excitatory loops (CRIRELs). These CRIRELs have the advantage of being both multi-functional and controllable, performing a plethora of functions, including decisions, memory, toggle, and so forth. Finally, we demonstrate how mutual inhibition maximizes storage capacity for larger networks.

Adaptive rewiring: a general principle for neural network development

The nervous system, especially the human brain, is characterized by its highly complex network topology. The neurodevelopment of some of its features has been described in terms of dynamic optimization rules. We discuss the principle of adaptive rewiring, i.e., the dynamic reorganization of a network according to the intensity of internal signal communication as measured by synchronization or diffusion, and its recent generalization for applications in directed networks. These have extended the principle of adaptive rewiring from highly oversimplified networks to more neurally plausible ones. Adaptive rewiring captures all the key features of the complex brain topology: it transforms initially random or regular networks into networks with a modular small-world structure and a rich-club core. This effect is specific in the sense that it can be tailored to computational needs, robust in the sense that it does not depend on a critical regime, and flexible in the sense that parametric variation generates a range of variant network configurations. Extreme variant networks can be associated at macroscopic level with disorders such as schizophrenia, autism, and dyslexia, and suggest a relationship between dyslexia and creativity. Adaptive rewiring cooperates with network growth and interacts constructively with spatial organization principles in the formation of topographically distinct modules and structures such as ganglia and chains. At the mesoscopic level, adaptive rewiring enables the development of functional architectures, such as convergent-divergent units, and sheds light on the early development of divergence and convergence in, for example, the visual system. Finally, we discuss future prospects for the principle of adaptive rewiring.

Decreased GABA levels during development result in increased connectivity in the larval zebrafish tectum

γ-aminobutyric acid (GABA) is an abundant neurotransmitter that plays multiple roles in the vertebrate central nervous system (CNS). In the early developing CNS, GABAergic signaling acts to depolarize cells. It mediates several aspects of neural development, including cell proliferation, neuronal migration, neurite growth, and synapse formation, as well as the development of critical periods. Later in CNS development, GABAergic signaling acts in an inhibitory manner when it becomes the predominant inhibitory neurotransmitter in the brain. This behavior switch occurs due to changes in chloride/cation transporter expression. Abnormalities of GABAergic signaling appear to underlie several human neurological conditions, including seizure disorders. However, the impact of reduced GABAergic signaling on brain development has been challenging to study in mammals. Here we take advantage of zebrafish and light sheet imaging to assess the impact of reduced GABAergic signaling on the functional circuitry in the larval zebrafish optic tectum. Zebrafish have three gad genes: two gad1 paralogs known as gad1a and gad1b, and gad2. The gad1b and gad2 genes are expressed in the developing optic tectum. Null mutations in gad1b significantly reduce GABA levels in the brain and increase electrophysiological activity in the optic tectum. Fast light sheet imaging of genetically encoded calcium indicator (GCaMP)-expressing gab1b null larval zebrafish revealed patterns of neural activity that were different than either gad1b-normal larvae or gad1b-normal larvae acutely exposed to pentylenetetrazole (PTZ). These results demonstrate that reduced GABAergic signaling during development increases functional connectivity and concomitantly hyper-synchronization of neuronal networks.

Shifts in attention drive context-dependent subspace encoding in anterior cingulate cortex in mice during decision making

Attention supports decision making by selecting the features that are relevant for decisions. Selective enhancement of the relevant features and inhibition of distractors has been proposed as potential neural mechanisms driving this selection process. Yet, how attention operates when relevance cannot be directly determined, and the attention signal needs to be internally constructed is less understood. Here we recorded from populations of neurons in the anterior cingulate cortex (ACC) of mice in an attention-shifting task where relevance of stimulus modalities changed across blocks of trials. In contrast with V1 recordings, decoding of the irrelevant modality gradually declined in ACC after an initial transient. Our analytical proof and a recurrent neural network model of the task revealed mutually inhibiting connections that produced context-gated suppression as observed in mice. Using this RNN model we predicted a correlation between contextual modulation of individual neurons and their stimulus drive, which we confirmed in ACC but not in V1.

Memory Trace for Fear Extinction: Fragile yet Reinforceable

Fear extinction is a biological process in which learned fear behavior diminishes without anticipated reinforcement, allowing the organism to re-adapt to ever-changing situations. Based on the behavioral hypothesis that extinction is new learning and forms an extinction memory, this new memory is more readily forgettable than the original fear memory. The brain's cellular and synaptic traces underpinning this inherently fragile yet reinforceable extinction memory remain unclear. Intriguing questions are about the whereabouts of the engram neurons that emerged during extinction learning and how they constitute a dynamically evolving functional construct that works in concert to store and express the extinction memory. In this review, we discuss recent advances in the engram circuits and their neural connectivity plasticity for fear extinction, aiming to establish a conceptual framework for understanding the dynamic competition between fear and extinction memories in adaptive control of conditioned fear responses.

Connectivity by the Frontal Aslant Tract (FAT) Explains Local Functional Specialization of the Superior and Inferior Frontal Gyri in Humans When Choosing Predictive over Reactive Strategies: A Tractography-Guided TMS Study

Predictive and reactive behaviors represent two mutually exclusive strategies in a sensorimotor task. Predictive behavior consists in internally estimating timing and features of a target stimulus and relies on a cortical medial frontal system [superior frontal gyrus (SFG)]. Reactive behavior consists in waiting for actual perception of the target stimulus and relies on the lateral frontal cortex [inferior frontal gyrus (IFG)]. We investigated whether SFG-IFG connections by the frontal aslant tract (FAT) can mediate predictive/reactive interactions. In 19 healthy human volunteers, we applied online transcranial magnetic stimulation (TMS) to six spots along the medial and lateral terminations of the FAT, during the set period of a delayed reaction task. Such scenario can be solved using either predictive or reactive strategies. TMS increased the propensity toward reactive behavior if applied to a specific portion of the IFG and increased predictive behavior when applied to a specific SFG spot. The two active spots in the SFG and IFG were directly connected by a sub-bundle of FAT fibers as indicated by diffusion-weighted imaging (DWI) tractography. Since FAT connectivity identifies two distant cortical nodes with opposite functions, we propose that the FAT mediates mutually inhibitory interactions between SFG and IFG to implement a "winner takes all" decisional process. We hypothesize such role of the FAT to be domain-general, whenever competition occurs between internal predictive and external reactive behaviors. Finally, we also show that anatomic connectivity is a powerful factor to explain and predict the spatial distribution of brain stimulation effects.SIGNIFICANCE STATEMENT We interact with sensory cues adopting two main mutually-exclusive strategies: (1) trying to anticipate the occurrence of the cue or (2) waiting for the GO-signal to be manifest and react to it. Here, we showed, by using noninvasive brain stimulation [transcranial magnetic stimulation (TMS)], that two specific cortical regions in the superior frontal gyrus (SFG) and the inferior frontal gyrus (IFG) have opposite roles in facilitating a predictive or a reactive strategy. Importantly these two very distant regions but with highly interconnected functions are specifically connected by a small white matter bundle, which mediates the direct competition and exclusiveness between predictive and reactive strategies. More generally, implementing anatomic connectivity in TMS studies strongly reduces spatial noise.

Shifts in attention drive context-dependent subspace encoding in anterior cingulate cortex during decision making

Attention is a cognitive faculty that selects part of a larger set of percepts, driven by cues such as stimulus saliency, internal goals or priors. The enhancement of the attended representation and inhibition of distractors have been proposed as potential neural mechanisms driving this selection process. Yet, how attention operates when the cue has to be internally constructed from conflicting stimuli, decision rules, and reward contingencies, is less understood. Here we recorded from populations of neurons in the anterior cingulate cortex (ACC), an area implicated in ongoing error monitoring and correction during decision conflicts, in a challenging attention-shifting task. In this task, mice had to attend to the rewarded modality when presented identical auditory and visual stimuli in two contexts without direct external cues. In the ACC, the irrelevant stimulus continuously became less decodable than the relevant stimulus as the trial progressed to the decision point. This contrasted strongly with our previous findings in V1 where both relevant and irrelevant stimuli were equally decodable throughout the trial. Using analytical tools and a recurrent neural network (RNN) model, we found that the linearly independent representation of stimulus modalities in ACC was well suited to context-gated suppression of a stimulus modality. We demonstrated that the feedback structure of lateral connections in the RNN consisted of excitatory interactions between cell ensembles representing the same modality and mutual inhibition between cell ensembles representing distinct stimulus modalities. Using this RNN model showing signatures of context-gated suppression, we predicted that the level of contextual modulation of individual neurons should be correlated with their relative responsiveness to the two stimulus modalities used in the task. We verified this prediction in recordings from ACC neurons but not from recordings from V1 neurons. Therefore, ACC effectively operates on low-dimensional neuronal subspaces to combine stimulus related information with internal cues to drive actions under conflict.

Inhibitory signaling in collective social insect networks, is it indeed uncommon?

Individual entities across levels of biological organization interact to reach collective decisions. In centralized neuronal networks, competing neural populations commonly accumulate information over time while increasing their own activity, and cross-inhibiting other populations until one group passes a given threshold. In social insects, there is good evidence for decisions mediated by positive feedbacks, but we found evidence for similar inhibitory signals only in honey bee (Apis mellifera) stop signals, and Pharaoh's ant- (Monomorium pharaonic) repellent pheromones, with only the former occasionally being used as cross-inhibition. We discuss whether these differences stem from insufficient research effort or represent genuine differences across levels of biological organization.

An early midbrain sensorimotor pathway is involved in the timely initiation and direction of swimming in the hatchling Xenopus laevis tadpole

Vertebrate locomotion is heavily dependent on descending control originating in the midbrain and subsequently influencing central pattern generators in the spinal cord. However, the midbrain neuronal circuitry and its connections with other brainstem and spinal motor circuits has not been fully elucidated. Vertebrates with very simple nervous system, like the hatchling Xenopus laevis tadpole, have been instrumental in unravelling fundamental principles of locomotion and its suspraspinal control. Here, we use behavioral and electrophysiological approaches in combination with lesions of the midbrain to investigate its contribution to the initiation and control of the tadpole swimming in response to trunk skin stimulation. None of the midbrain lesions studied here blocked the tadpole's sustained swim behavior following trunk skin stimulation. However, we identified that distinct midbrain lesions led to significant changes in the latency and trajectory of swimming. These changes could partly be explained by the increase in synchronous muscle contractions on the opposite sides of the tadpole's body and permanent deflection of the tail from its normal position, respectively. We conclude that the tadpole's embryonic trunk skin sensorimotor pathway involves the midbrain, which harbors essential neuronal circuitry to significantly contribute to the appropriate, timely and coordinated selection and execution of locomotion, imperative to the animal's survival.

A Parallel Spiking Neural Network Based on Adaptive Lateral Inhibition Mechanism for Objective Recognition

Spiking neural network (SNN) has attracted extensive attention in the field of machine learning because of its biological interpretability and low power consumption. However, the accuracy of pattern recognition cannot completely surpass deep neural networks (DNNs). The main reason is that the inherent nondifferentiability of spiking neurons makes SNN unable to be trained directly by the gradient descent algorithm, and there is also no unified training algorithm for SNN. Inspired by the biological vision system, this paper proposes a parallel convolution SNN structure combined with an adaptive lateral inhibition mechanism. And, a way of dynamically evolving the time constant with the training of SNN is proposed to ensure the diversity of neurons. This paper verifies the effectiveness of the proposed methods on static datasets and neuromorphic datasets and extends it to the recognition of breast tumors. Experimental results show that the SNN has obvious advantages in dynamical datasets. For breast tumors, it is also an edge-based task, because the edge of a medical image contains the most important information in the image. This kind of information can provide great help for the noninvasive and accurate diagnosis of diseases. The Experimental results show that the proposed method is very close to the recognition results of DNNs on static datasets, and its performance on neuromorphic datasets exceeds that of DNNs.

Cooperative stability renders protein complex formation more robust and controllable

Protein complexes are the fundamental units of many biological functions. Despite their many advantages, one major adverse impact of protein complexes is accumulations of unassembled subunits that may disrupt other processes or exert cytotoxic effects. Synthesis of excess subunits can be inhibited via negative feedback control or they can be degraded more efficiently than assembled subunits, with this latter being termed cooperative stability. Whereas controlled synthesis of complex subunits has been investigated extensively, how cooperative stability acts in complex formation remains largely unexplored. To fill this knowledge gap, we have built quantitative models of heteromeric complexes with or without cooperative stability and compared their behaviours in the presence of synthesis rate variations. A system displaying cooperative stability is robust against synthesis rate variations as it retains high dimer/monomer ratios across a broad range of parameter configurations. Moreover, cooperative stability can alleviate the constraint of limited supply of a given subunit and makes complex abundance more responsive to unilateral upregulation of another subunit. We also conducted an in silico experiment to comprehensively characterize and compare four types of circuits that incorporate combinations of negative feedback control and cooperative stability in terms of eight systems characteristics pertaining to optimality, robustness and controllability. Intriguingly, though individual circuits prevailed for distinct characteristics, the system with cooperative stability alone achieved the most balanced performance across all characteristics. Our study provides theoretical justification for the contribution of cooperative stability to natural biological systems and represents a guideline for designing synthetic complex formation systems with desirable characteristics.

Top-down regulation of motivated behaviors via lateral septum sub-circuits

How does cognition regulate innate behaviors? While the cognitive functions of the cortex have been extensively studied, we know much less about how cognition can regulate innate motivated behaviors to fulfill physiological, safety and social needs. Selection of appropriate motivated behaviors depends on external stimuli and past experiences that helps to scale priorities. With its abundant inputs from neocortical and allocortical regions, the lateral septum (LS) is ideally positioned to integrate perception and experience signals in order to regulate the activity of hypothalamic and midbrain nuclei that control motivated behaviors. In addition, LS receives numerous subcortical modulatory inputs, which represent the animal internal states and also participate in this regulation. In this perspective, we argue that LS sub-circuits regulate distinct motivated behaviors by integrating neural activity from neocortical, allocortical and neuromodulatory inputs. In addition, we propose that lateral inhibition between LS sub-circuits may allow the emergence of functional units that orchestrates competing motivated behaviors.

Magnitude-sensitivity: rethinking decision-making

Magnitude-sensitivity refers to the result that performance in decision-making, across domains and organisms, is affected by the total value of the possible alternatives. This simple result offers a window into fundamental issues in decision-making and has led to a reconsideration of ecological decision-making, prominent computational models of decision-making, and optimal decision-making. Moreover, magnitude-sensitivity has inspired the design of new robotic systems that exploit natural solutions and apply optimal decision-making policies. In this article, we review the key theoretical and empirical results about magnitude-sensitivity and highlight the importance that this phenomenon has for the understanding of decision-making. Furthermore, we discuss open questions and ideas for future research.

Robust rhythmogenesis via spike-timing-dependent plasticity

Rhythmic activity has been observed in numerous animal species ranging from insects to humans, and in relation to a wide range of cognitive tasks. Various experimental and theoretical studies have investigated rhythmic activity. The theoretical efforts have mainly been focused on the neuronal dynamics, under the assumption that network connectivity satisfies certain fine-tuning conditions required to generate oscillations. However, it remains unclear how this fine-tuning is achieved. Here we investigated the hypothesis that spike-timing-dependent plasticity (STDP) can provide the underlying mechanism for tuning synaptic connectivity to generate rhythmic activity. We addressed this question in a modeling study. We examined STDP dynamics in the framework of a network of excitatory and inhibitory neuronal populations that has been suggested to underlie the generation of oscillations in the gamma range. Mean-field Fokker-Planck equations for the synaptic weight dynamics are derived in the limit of slow learning. We drew on this approximation to determine which types of STDP rules drive the system to exhibit rhythmic activity, and we demonstrate how the parameters that characterize the plasticity rule govern the rhythmic activity. Finally, we propose a mechanism that can ensure the robustness of self-developing processes in general, and for rhythmogenesis in particular.

Intercalated amygdala clusters orchestrate a switch in fear state

Adaptive behaviour necessitates the formation of memories for fearful events, but also that these memories can be extinguished. Effective extinction prevents excessive and persistent reactions to perceived threat, as can occur in anxiety and 'trauma- and stressor-related' disorders¹. However, although there is evidence that fear learning and extinction are mediated by distinct neural circuits, the nature of the interaction between these circuits remains poorly understood^2-6. Here, through a combination of in vivo calcium imaging, functional manipulations, and slice physiology, we show that distinct inhibitory clusters of intercalated neurons (ITCs) in the mouse amygdala exert diametrically opposed roles during the acquisition and retrieval of fear extinction memory. Furthermore, we find that the ITC clusters antagonize one another through mutual synaptic inhibition and differentially access functionally distinct cortical- and midbrain-projecting amygdala output pathways. Our findings show that the balance of activity between ITC clusters represents a unique regulatory motif that orchestrates a distributed neural circuitry, which in turn regulates the switch between high- and low-fear states. These findings suggest that the ITCs have a broader role in a range of amygdala functions and associated brain states that underpins the capacity to adapt to salient environmental demands.

Neural circuitry for stimulus selection in the zebrafish visual system

When navigating the environment, animals need to prioritize responses to the most relevant stimuli. Although a theoretical framework for selective visual attention exists, its circuit implementation has remained obscure. Here we investigated how larval zebrafish select between simultaneously presented visual stimuli. We found that a mix of winner-take-all (WTA) and averaging strategies best simulates behavioral responses. We identified two circuits whose activity patterns predict the relative saliencies of competing visual objects. Stimuli presented to only one eye are selected by WTA computation in the inner retina. Binocularly presented stimuli, on the other hand, are processed by reciprocal, bilateral connections between the nucleus isthmi (NI) and the tectum. This interhemispheric computation leads to WTA or averaging responses. Optogenetic stimulation and laser ablation of NI neurons disrupt stimulus selection and behavioral action selection. Thus, depending on the relative locations of competing stimuli, a combination of retinotectal and isthmotectal circuits enables selective visual attention.

Inhibitory interneurons in a brainstem circuit adjust their inhibitory motifs to process multimodal input

KEY POINTS

Inhibitory-interneuron networks, consisting of multiple forms of circuit motifs including reciprocal (inhibitory interneurons inhibiting other interneurons) and feedforward (inhibitory interneurons inhibiting principal neurons) connections, are crucial in processing sensory information. The present study applies a statistical method to in vivo multichannel spike trains of dorsal cochlear nucleus neurons to disentangle reciprocal and feedforward-inhibitory motifs. After inducing input-specific plasticity, reciprocal and feedforward inhibition are found to be differentially regulated, and the combined effect synergistically modulates circuit output. The findings highlight the interplay among different circuit motifs as a key element in neural computation.

ABSTRACT

Inhibitory interneurons play an essential role in neural computations by utilizing a combination of reciprocal (interneurons inhibiting each other) and feedforward (interneuron inhibiting the principal neuron) inhibition to process information. To disentangle the interplay between the two inhibitory-circuit motifs and understand their effects on the circuit output, in vivo recordings were made from the guinea pig dorsal cochlear nucleus, a cerebellar-like brainstem circuit. Spikes from inhibitory interneurons (cartwheel cell) and principal output neurons (fusiform cell) were compared before and after manipulating their common multimodal input. Using a statistical model based on the Cox method of modulated renewal process of spike train influence, reciprocal- and feedforward-inhibition motifs were quantified. In response to altered multimodal input, reciprocal inhibition was strengthened while feedforward inhibition was weakened, and the two motifs combined to modulate fusiform cell output and acoustic-driven responses. These findings reveal the cartwheel cell's role in auditory and multimodal processing, as well as illustrated the balance between different inhibitory-circuit motifs as a key element in neural computation.

An Optical Illusion Pinpoints an Essential Circuit Node for Global Motion Processing

Direction-selective (DS) neurons compute the direction of motion in a visual scene. Brain-wide imaging in larval zebrafish has revealed hundreds of DS neurons scattered throughout the brain. However, the exact population that causally drives motion-dependent behaviors-e.g., compensatory eye and body movements-remains largely unknown. To identify the behaviorally relevant population of DS neurons, here we employ the motion aftereffect (MAE), which causes the well-known "waterfall illusion." Together with region-specific optogenetic manipulations and cellular-resolution functional imaging, we found that MAE-responsive neurons represent merely a fraction of the entire population of DS cells in larval zebrafish. They are spatially clustered in a nucleus in the ventral lateral pretectal area and are necessary and sufficient to steer the entire cycle of optokinetic eye movements. Thus, our illusion-based behavioral paradigm, combined with optical imaging and optogenetics, identified key circuit elements of global motion processing in the vertebrate brain.

Mechanisms of competitive selection: A canonical neural circuit framework

Competitive selection, the transformation of multiple competing sensory inputs and internal states into a unitary choice, is a fundamental component of animal behavior. Selection behaviors have been studied under several intersecting umbrellas including decision-making, action selection, perceptual categorization, and attentional selection. Neural correlates of these behaviors and computational models have been investigated extensively. However, specific, identifiable neural circuit mechanisms underlying the implementation of selection remain elusive. Here, we employ a first principles approach to map competitive selection explicitly onto neural circuit elements. We decompose selection into six computational primitives, identify demands that their execution places on neural circuit design, and propose a canonical neural circuit framework. The resulting framework has several links to neural literature, indicating its biological feasibility, and has several common elements with prominent computational models, suggesting its generality. We propose that this framework can help catalyze experimental discovery of the neural circuit underpinnings of competitive selection.

A Simple, Biologically Plausible Feature Detector for Language Acquisition

Language has a complex grammatical system we still have to understand computationally and biologically. However, some evolutionarily ancient mechanisms have been repurposed for grammar so that we can use insight from other taxa into possible circuit-level mechanisms of grammar. Drawing upon recent evidence for the importance of disinhibitory circuits across taxa and brain regions, I suggest a simple circuit that explains the acquisition of core grammatical rules used in 85% of the world's languages: grammatical rules based on sameness/difference relations. This circuit acts as a sameness detector. “Different” items are suppressed through inhibition, but presenting two “identical” items leads to inhibition of inhibition. The items are thus propagated for further processing. This sameness detector thus acts as a feature detector for a grammatical rule. I suggest that having a set of feature detectors for elementary grammatical rules might make language acquisition feasible based on relatively simple computational mechanisms.

Studying neural circuits of decision-making in Drosophila larva

To study neural circuits underlying decisions, the model organism used for that purpose has to be simple enough to be able to dissect the circuitry neuron by neuron across the nervous system and in the same time complex enough to be able to perform different types of decisions. Here, I lay out the case: (1) that Drosophila larva is an advantageous model system that balances well these two requirements and (2) the insights gained from this model, assuming that circuit principles may be shared across species, can be used to advance our knowledge of neural circuit implementation of decision-making in general, including in more complex brains.

Chronology-based architecture of descending circuits that underlie the development of locomotor repertoire after birth

The emergence of new and increasingly sophisticated behaviors after birth is accompanied by dramatic increase of newly established synaptic connections in the nervous system. Little is known, however, of how nascent connections are organized to support such new behaviors alongside existing ones. To understand this, in the larval zebrafish we examined the development of spinal pathways from hindbrain V2a neurons and the role of these pathways in the development of locomotion. We found that new projections are continually layered laterally to existing neuropil, and give rise to distinct pathways that function in parallel to existing pathways. Across these chronologically layered pathways, the connectivity patterns and biophysical properties vary systematically to support a behavioral repertoire with a wide range of kinematics and dynamics. Such layering of new parallel circuits equipped with systematically changing properties may be central to the postnatal diversification and increasing sophistication of an animal’s behavioral repertoire.

Newborn babies have limited abilities. Indeed, most of our actions shortly after birth are the result of reflexes that serve our most basic need: to stay alive. As we get older, however, our behaviour gradually becomes more sophisticated. During this time, the billions of cells in our brain form new connections to build intricate ‘circuits’ of neurons that allow for more complicated thoughts and actions.

It is clear that the brain circuits that support new behaviours must develop in a way that does not interfere with the existing circuits that are vital for survival. However, the challenge has been to find a way to peer into a brain as it develops to see how these new circuits form.

In recent years, zebrafish have revolutionised research into neuronal circuits in animals. Developing over the course of a few days, these small transparent fish provide a window into the brain during the earliest stages of development. Indeed, the circuits of neurons that descend from the brain and connect to the spinal cord have already been mapped in these animals. Now, Pujala and Koyama have begun to follow the careful development of these ‘descending’ neurons, and relate it to the appearance of new behaviours in young zebrafish.

Time-lapse imaging with a fluorescent protein that is active only in specific descending neurons revealed that new circuits are laid down over existing ones, like the growth rings in a tree. Next, at different timepoints in zebrafish development, Pujala and Koyama traced these neurons backwards from the spine to the brain to identify which connections formed first. This showed that the spinal connections develop one after the other, in the same order that the neurons mature.

Next, Pujala and Koyama asked how the activity of neurons that mature early or late in development relates to specific behaviours in young zebrafish. Early-born circuits connect to neurons that produce powerful, reflex-driven, whole-body movements such as an escape response. The later circuits connect to different neurons through slower, less direct pathways; the late-born neurons also generate the refined movements that are acquired later in a zebrafish’s development and help the fish to explore its environment.

These findings show that descending circuits in zebrafish run parallel to each other, but with distinct connections and properties that allow them to control different kinds of movements. While this study was conducted using an animal model, a better understanding of how such circuits develop and the movements they control may one day aid the treatment of patients with neurodegenerative diseases or injuries where connections have been lost.

Behavioral Role of the Reciprocal Inhibition between a Pair of Mauthner Cells during Fast Escapes in Zebrafish

During many behaviors in vertebrates, the CNS generates asymmetric activities between the left and right sides to produce asymmetric body movements. For asymmetrical activations of the CNS, reciprocal inhibition between the left and right sides is believed to play a key role. However, the complexity of the CNS makes it difficult to identify the reciprocal inhibition circuits at the level of individual cells and the contribution of each neuron to the asymmetric activity. Using larval zebrafish, we examined this issue by investigating reciprocal inhibition circuits between a pair of Mauthner (M) cells, giant reticulospinal neurons that trigger fast escapes. Previous studies have shown that a class of excitatory neurons, called cranial relay neurons, is involved in the reciprocal inhibition pathway between the M cells. Using transgenic fish, in which two of the cranial relay neurons (Ta1 and Ta2) expressed GFP, we showed that Ta1 and Ta2 constitute major parts of the pathway. In larvae in which Ta1/Ta2 were laser-ablated, the amplitude of the reciprocal IPSPs dropped to less than one-third. Calcium imaging and electrophysiological recording showed that the occurrence probability of bilateral M-cell activation upon sound/vibration stimuli was greatly increased in the Ta1/Ta2-ablated larvae. Behavioral experiments revealed that the Ta1/Ta2 ablation resulted in shallower body bends during sound/vibration-evoked escapes, which is consistent with the observation that increased occurrence of bilateral M-cell activation impaired escape performance. Our study revealed major components of the reciprocal inhibition circuits in the M cell system and the behavioral importance of the circuits.SIGNIFICANCE STATEMENT Reciprocal inhibition between the left and right side of the CNS is considered imperative for producing asymmetric movements in animals. It has been difficult, however, to identify the circuits at the individual cell level and their role in behavior. Here, we address this problem by examining the reciprocal inhibition circuits of the hindbrain Mauthner (M) cell system in larval zebrafish. We determined that two paired interneurons play a critical role in the reciprocal inhibition between the paired M cells and that the reciprocal inhibition prevents bilateral firing of the M cells and is thus necessary for the full body bend during M cell-initiated escape. Further, we discussed the cooperation of multiple reciprocal inhibitions working in the hindbrain and spinal cord to ensure high-performance escapes.

Rhythmogenesis evolves as a consequence of long-term plasticity of inhibitory synapses

Brain rhythms are widely believed to reflect numerous cognitive processes. Changes in rhythmicity have been associated with pathological states. However, the mechanism underlying these rhythms remains unknown. Here, we present a theoretical analysis of the evolvement of rhythm generating capabilities in neuronal circuits. We tested the hypothesis that brain rhythms can be acquired via an intrinsic unsupervised learning process of activity dependent plasticity. Specifically, we focused on spike timing dependent plasticity (STDP) of inhibitory synapses. We detail how rhythmicity can develop via STDP under certain conditions that serve as a natural prediction of the hypothesis. We show how global features of the STDP rule govern and stabilize the resultant rhythmic activity. Finally, we demonstrate how rhythmicity is retained even in the face of synaptic variability. This study suggests a role for inhibitory plasticity that is beyond homeostatic processes.