Genetic manipulation of the odor-evoked distributed neural activity in the Drosophila mushroom body

Odor-induced neural activity was recorded by Ca2+ imaging in the cell body region of the Drosophila mushroom body (MB), which is the second relay of the olfactory central nervous system. The signals recorded are mainly from the cell layers on the brain surface because of the limited penetration of Ca2+-sensitive dyes. The densely packed cell bodies and their accessibility allow visualization of odor-induced population neural activity. It is revealed that odors evoke diffused neural activities in the MB. Although the signals cannot be attributed to individual neurons, patterns of the population neural activity can be analyzed. The activity pattern, but not the amplitude, of an odor-induced population response is specific for the chemical identity of an odor and its concentration. The distribution pattern of neural activity can be altered specifically by genetic manipulation of an odor binding protein and this alteration is closely associated with a behavioral defect of odor preference. These results suggest that the spatial pattern of the distributed neural activity may contribute to coding of odor information at the second relay of the olfactory system.     

Mesoscopic neurodynamics: From neuron to brain

Intelligent behavior is characterized by flexible and creative pursuit of endogenously defined goals. Intentionality is a key concept by which to link neuron and brain to goal-directed behavior through brain dynamics. An archetypal form of intentional behavior is an act of observation in space-time, by which information is sought for the guidance of future action to explore unpredictable and ever-changing environments. These acts are based in the brain dynamics that creates spatiotemporal patterns of neural activity, serving as images of goals, of command sequences by which to act to reach goals, and of expected changes in sensory input resulting from intended actions. Prediction of the sensory consequences of intended action and evaluation of performance is by reafference. An intentional act is completed upon modification of the system by itself through learning. These principles are well known among psychologists and philosophers. What is new is the development of nonlinear mesoscopic brain dynamics, by which the theory of chaos can be used to understand and simulate the constructions of meaningful patterns of neural activity that implement the process of observation. The design of neurobiological experiments, analysis of the resulting data, and synthesis of explanatory models require an understanding of the hierarchical nature of brain organization, here conceived as single neurons and neural networks at the microscopic level; clinically defined cortical and subcortical systems studied by brain imaging (for example, fMRI) at the macroscopic level, and self-organizing neural populations at an intermediate mesoscopic level, at which synaptic interactions create novel activity patterns through nonlinear state transitions. The constructive neurodynamics of sensory cortices, when they are engaged in pattern recognition, is revealed by learning-dependent spatial patterns of amplitude modulation and by newly discovered radially symmetric spatial gradients of the phase of aperiodic carrier waves in multichannel subdural EEG recordings.     

Visual pattern recognition based on spatio-temporal patterns of retinal ganglion cells’ activities

Neural information is processed based on integrated activities of relevant neurons. Concerted population activity is one of the important ways for retinal ganglion cells to efficiently organize and process visual information. In the present study, the spike activities of bullfrog retinal ganglion cells in response to three different visual patterns (checker-board, vertical gratings and horizontal gratings) were recorded using multi-electrode arrays. A measurement of subsequence distribution discrepancy (MSDD) was applied to identify the spatio-temporal patterns of retinal ganglion cells' activities in response to different stimulation patterns. The results show that the population activity patterns were different in response to different stimulation patterns, such difference in activity pattern was consistently detectable even when visual adaptation occurred during repeated experimental trials. Therefore, the stimulus pattern can be reliably discriminated according to the spatio-temporal pattern of the neuronal activities calculated using the MSDD algorithm.     

Exploring Memory Representations with Activity-Based Genetics

The brain is thought to represent specific memories through the activity of sparse and distributed neural ensembles. In this review, we examine the use of immediate early genes (IEGs), genes that are induced by neural activity, to specifically identify and genetically modify neurons activated naturally by environmental experience. Recent studies using this approach have identified cellular and molecular changes specific to neurons activated during learning relative to their inactive neighbors. By using opto- and chemogenetic regulators of neural activity, the neurons naturally recruited during learning can be artificially reactivated to directly test their role in coding external information. In contextual fear conditioning, artificial reactivation of learning-induced neural ensembles in the hippocampus or neocortex can substitute for the context itself. That is, artificial stimulation of these neurons can apparently cause the animals to “think” they are in the context. This represents a powerful approach to testing the principles by which the brain codes for the external world and how these circuits are modified with learning.A central feature of nervous systems is that, to function properly, specific neurons must become active in response to specific stimuli. The nature of this selective activation and its modification with experience is the focus of much neuroscience research, ranging from studies of sensory processing in experimental animals to disorders of thought such as schizophrenia in humans. The central dogma of neuroscience is that perceptions, memories, thoughts, and higher mental functions arise from the pattern and timing of the activity in neural ensembles in specific parts of the brain at specific points in time. Until quite recently, the investigation of these “circuit”-based questions has primarily been limited to observational techniques, such as single unit recording, functional magnetic resonance imagery (fMRI), and calcium imaging, to document the patterns of neural activity evoked by sensory experience or even complex psychological contingencies in human fMRI studies. These techniques have been enormously successful and created a framework for understanding information processing in the brain. For example, recordings in the visual system have indicated that, in the primary visual cortex, neurons are tuned to the orientation of linear stimuli (Hubel and Wiesel 1962). In contrast, neurons in higher brain areas can respond to discrete items. The most striking example of this specificity comes from in vivo recording in the human medial temporal lobe in which single units have been identified that respond to photos of the actress Halle Berry as well as her written name (Quiroga et al. 2005). This highly selective tuning of neural activity is suggestive of function, but how can this be directly tested? What would be the effect of stimulating just this rare population of neurons, a memory of the actress, a sensory illusion of her image? How does this type of specific firing arise? Do these neurons differ from their nonresponsive neighbors in terms of biochemistry, cell biology, or connectivity? Do they undergo molecular alterations when new information is learned about this individual and are these changes required for the learning? These types of questions have recently become accessible to study in mice through the use of activity-based genetic manipulation, in which neurons that are activated by a specific sensory stimulus can be altered to express any gene of experimental interest. These studies and approaches will be the focus of this work.     

The construction of a simultaneous functional order in nervous systems

The functional order of a collection of nervous elements is available to the system itself, as opposed to the anatomical geometrical order which exists only for external observers. It has been shown before (Part I) that covariances or coincidences in the signal activity of a neural net can be used in the construction of a simultaneous functional order in which a modality is represented as a concatenation of districts with a lattice structure. In this paper we will show how the resulting functional order in a nervous net can be related to the geometry of the underlying detector array. In particular, we will present an algorithm to construct an abstract geometrical complex from this functional order. The algebraic structure of this complex reflects the topological and geometrical structure of the underlying detector array. We will show how the activated subcomplexes of a complex can be related to segments of the detector array that are activated by the projection of a stimulus pattern. The homology of an abstract complex (and therefore of all of its subcomplexes) can be obtained from simple combinatorial operations on its coincidence scheme. Thus, both the geometry of a detector array and the topology of projections of stimulus patterns may have an objective existence for the neural system itself.     

Neural crest cells provide species-specific patterning information in the developing branchial skeleton

The skeletal elements of the branchial region are made by neural crest cells, following tissue interactions with the pharyngeal endoderm. Previous transplantation experiments have claimed that the cranial neural crest is morphogenetically prespecified in respect of its branchial skeletal derivatives, that is, that information for the number, size, shape, and position of its individual elements is already determined in these cells when they are still in the neural folds. This positional information would somehow be preserved during delamination from the neural tube and migration into the branchial arches, before being read out as a spatial pattern of chondrogenesis and osteogenesis. However, it now appears that signals from the endoderm are able to specify not only the histogenic differentiation state of neural crest cells but also the identity and orientation of the branchial skeletal elements. It is therefore important to ask whether fine details of branchial skeletal pattern such as those that exist between different species are also governed by extrinsic factors, such as the endoderm, or by the neural crest itself. We have grafted neural crest between duck and quail embryos and show that the shape and size of the resulting skeletal elements is donor derived. The ability to form species-specific patterns of craniofacial skeletal tissue thus appears to be an inherent property of the neural crest, expressed as species-specific responses to endodermal signals.     

A template matching model for pattern recognition: Self-organization of templates and template matching by a disinhibitory neural network

A template matching model for pattern recognition is proposed. By following a previouslyproposed algorithm for synaptic modification (Hirai, 1980), the template of a stimulus pattern is selforganized as a spatial distribution pattern of matured synapses on the cells receiving modifiable synapses. Template matching is performed by the disinhibitory neural network cascaded beyond the neural layer composed of the cells receiving the modifiable synapses. The performance of the model has been simulated on a digital computer. After repetitive presentations of a stimulus pattern, a cell receiving the modifiable synapses comes to have the template of that pattern. And the cell in the latter layer of the disinhibitory bitory neural network that receives the disinhibitory input from that cell becomes electively sensitive to that pattern. Learning patterns are not restricted by previously learned ones. They can be subset or superset patterns of the ones previously learned. If an unknown pattern is presented to the model, no cell beyond the disinhibitory neural network will respond. However, if previously learned patterns are embedded in that pattern, the cells which have the templates of those patterns respond and are assumed to transmit the information to higher center. The computer simulation also shows that the model can organize a clean template under a noisy environment.     

A self-organizing model of walking patterns of insects

It is well known that the motor systems of animals are controlled by a hierarchy consisting of a brain, central pattern generator, and effector organs. An animal's walking patterns change depending on its walking velocities, even when it has been decerebrated, which indicates that the walking patterns may, in fact, be generated in the subregions of the neural systems of the central pattern generator and the effector organs. In order to explain the self-organization of the walking pattern in response to changing circumstances, our model incorporates the following ideas: (1) the brain sends only a few commands to the central pattern generator (CPG) which act as constraints to self-organize the walking patterns in the CPG; (2) the neural network of the CPG is composed of oscillating elements such as the KYS oscillator, which has been shown to simulate effectively the diversity of the neural activities; and (3) we have introduced a rule to coordinate leg movement, in which the excitatory and inhibitory interactions among the neurons act to optimize the efficiency of the energy transduction of the effector organs. We describe this mechanism as the least dissatisfaction for the greatest number of elements, which is a self-organization rule in the generation of walking patterns. By this rule, each leg tends to share the load as efficiently as possible under any circumstances. Using this self-organizing model, we discuss the control mechanism of walking patterns.     

Defecting or not defecting: how to "read" human behavior during cooperative games by EEG measurements

Understanding the neural mechanisms responsible for human social interactions is difficult, since the brain activities of two or more individuals have to be examined simultaneously and correlated with the observed social patterns. We introduce the concept of hyper-brain network, a connectivity pattern representing at once the information flow among the cortical regions of a single brain as well as the relations among the areas of two distinct brains. Graph analysis of hyper-brain networks constructed from the EEG scanning of 26 couples of individuals playing the Iterated Prisoner's Dilemma reveals the possibility to predict non-cooperative interactions during the decision-making phase. The hyper-brain networks of two-defector couples have significantly less inter-brain links and overall higher modularity--i.e., the tendency to form two separate subgraphs--than couples playing cooperative or tit-for-tat strategies. The decision to defect can be "read" in advance by evaluating the changes of connectivity pattern in the hyper-brain network.     

A symbiotic brain-machine interface through value-based decision making

Background

In the development of Brain Machine Interfaces (BMIs), there is a great need to enable users to interact with changing environments during the activities of daily life. It is expected that the number and scope of the learning tasks encountered during interaction with the environment as well as the pattern of brain activity will vary over time. These conditions, in addition to neural reorganization, pose a challenge to decoding neural commands for BMIs. We have developed a new BMI framework in which a computational agent symbiotically decoded users'' intended actions by utilizing both motor commands and goal information directly from the brain through a continuous Perception-Action-Reward Cycle (PARC).

Methodology

The control architecture designed was based on Actor-Critic learning, which is a PARC-based reinforcement learning method. Our neurophysiology studies in rat models suggested that Nucleus Accumbens (NAcc) contained a rich representation of goal information in terms of predicting the probability of earning reward and it could be translated into an evaluative feedback for adaptation of the decoder with high precision. Simulated neural control experiments showed that the system was able to maintain high performance in decoding neural motor commands during novel tasks or in the presence of reorganization in the neural input. We then implanted a dual micro-wire array in the primary motor cortex (M1) and the NAcc of rat brain and implemented a full closed-loop system in which robot actions were decoded from the single unit activity in M1 based on an evaluative feedback that was estimated from NAcc.

Conclusions

Our results suggest that adapting the BMI decoder with an evaluative feedback that is directly extracted from the brain is a possible solution to the problem of operating BMIs in changing environments with dynamic neural signals. During closed-loop control, the agent was able to solve a reaching task by capturing the action and reward interdependency in the brain.     

Tissue specific DNA methylation of CpG islands in normal human adult somatic tissues distinguishes neural from non-neural tissues

Although most CpG islands are generally thought to remain unmethylated in all adult somatic tissues, recent genome-wide approaches have found that some CpG islands have distinct methylation patterns in various tissues, with most differences being seen between germ cells and somatic tissues. Few studies have addressed this among human somatic tissues and fewer still have studied the same sets of tissues from multiple individuals. In the current study, we used Restriction Landmark Genomic Scanning to study tissue specific methylation patterns in a set of 12 human tissues collected from multiple individuals. We identified 34 differentially methylated CpG islands among these tissues, many of which showed consistent patterns in multiple individuals. Of particular interest were striking differences in CpG island methylation, not only among brain regions, but also between white and grey matter of the same region. These findings were confirmed for selected loci by quantitative bisulfite sequencing. Cluster analysis of the RLGS data indicated that several tissues clustered together, but the strongest clustering was in brain. Tissues from different brain regions clustered together, and, as a group, brain tissues were distinct from either mesoderm or endoderm derived tissues which demonstrated limited clustering. These data demonstrate consistent tissue specific methylation for certain CpG islands, with clear differences between white and grey matter of the brain. Furthermore, there was an overall pattern of tissue specifically methylated CpG islands that distinguished neural tissues from non-neural.Key words: Tissue specific methylation, CpG island methylation, neural, brain tissue, grey matter, white matter     

Multi-Stability and Pattern-Selection in Oscillatory Networks with Fast Inhibition and Electrical Synapses

A model or hybrid network consisting of oscillatory cells interconnected by inhibitory and electrical synapses may express different stable activity patterns without any change of network topology or parameters, and switching between the patterns can be induced by specific transient signals. However, little is known of properties of such signals. In the present study, we employ numerical simulations of neural networks of different size composed of relaxation oscillators, to investigate switching between in-phase (IP) and anti-phase (AP) activity patterns. We show that the time windows of susceptibility to switching between the patterns are similar in 2-, 4- and 6-cell fully-connected networks. Moreover, in a network (N = 4, 6) expressing a given AP pattern, a stimulus with a given profile consisting of depolarizing and hyperpolarizing signals sent to different subpopulations of cells can evoke switching to another AP pattern. Interestingly, the resulting pattern encodes the profile of the switching stimulus. These results can be extended to different network architectures. Indeed, relaxation oscillators are not only models of cellular pacemakers, bursting or spiking, but are also analogous to firing-rate models of neural activity. We show that rules of switching similar to those found for relaxation oscillators apply to oscillating circuits of excitatory cells interconnected by electrical synapses and cross-inhibition. Our results suggest that incoming information, arriving in a proper time window, may be stored in an oscillatory network in the form of a specific spatio-temporal activity pattern which is expressed until new pertinent information arrives.     

Factors limiting peripheral pattern discrimination

Previous reports indicate that some foveally discriminable compound gratings are indiscriminable in peripheral vision, even when they are scaled by the ratio of peripheral to foveal grating acuity. To determine the stimulus properties that limit peripheral discrimination, we used Gaussian derivatives of various orders. These patterns are spatially localized and have intrinsic even or odd symmetry. Our results show that certain odd symmetric patterns are discriminable in the periphery, while others are not. Furthermore, certain even symmetric patterns are not peripherally discriminable. These data are consistent with three limitations on peripheral pattern discrimination: (1) Patterns that produce different maximum neural responses will be peripherally discriminable. (2) Positional uncertainty and undersampling degrade discrimination of high spatial frequency patterns in the periphery. (3) Patterns generating substantial neural activity within a constrained region are processed as textures in peripheral vision so that pattern details within that region are no longer available for discrimination. A neural model incorporating inhibition of simple cells by complex cells implements a transition between contour analysis and texture analysis in peripheral vision and explains the experimental data.     

Graded,Dynamically Routable Information Processing with Synfire-Gated Synfire Chains

Coherent neural spiking and local field potentials are believed to be signatures of the binding and transfer of information in the brain. Coherent activity has now been measured experimentally in many regions of mammalian cortex. Recently experimental evidence has been presented suggesting that neural information is encoded and transferred in packets, i.e., in stereotypical, correlated spiking patterns of neural activity. Due to their relevance to coherent spiking, synfire chains are one of the main theoretical constructs that have been appealed to in order to describe coherent spiking and information transfer phenomena. However, for some time, it has been known that synchronous activity in feedforward networks asymptotically either approaches an attractor with fixed waveform and amplitude, or fails to propagate. This has limited the classical synfire chain’s ability to explain graded neuronal responses. Recently, we have shown that pulse-gated synfire chains are capable of propagating graded information coded in mean population current or firing rate amplitudes. In particular, we showed that it is possible to use one synfire chain to provide gating pulses and a second, pulse-gated synfire chain to propagate graded information. We called these circuits synfire-gated synfire chains (SGSCs). Here, we present SGSCs in which graded information can rapidly cascade through a neural circuit, and show a correspondence between this type of transfer and a mean-field model in which gating pulses overlap in time. We show that SGSCs are robust in the presence of variability in population size, pulse timing and synaptic strength. Finally, we demonstrate the computational capabilities of SGSC-based information coding by implementing a self-contained, spike-based, modular neural circuit that is triggered by streaming input, processes the input, then makes a decision based on the processed information and shuts itself down.     

Olfactory coding in the insect brain: molecular receptive ranges, spatial and temporal coding

Odor information is coded in the insect brain in a sequence of steps, ranging from the receptor cells, via the neural network in the antennal lobe, to higher order brain centers, among which the mushroom bodies and the lateral horn are the most prominent. Across all of these processing steps, coding logic is combinatorial, in the sense that information is represented as patterns of activity across a population of neurons, rather than in individual neurons. Because different neurons are located in different places, such a coding logic is often termed spatial, and can be visualized with optical imaging techniques. We employ in vivo calcium imaging in order to record odor‐evoked activity patterns in olfactory receptor neurons, different populations of local neurons in the antennal lobes, projection neurons linking antennal lobes to the mushroom bodies, and the intrinsic cells of the mushroom bodies themselves, the Kenyon cells. These studies confirm the combinatorial nature of coding at all of these stages. However, the transmission of odor‐evoked activity patterns from projection neuron dendrites via their axon terminals onto Kenyon cells is accompanied by a progressive sparsening of the population code. Activity patterns also show characteristic temporal properties. While a part of the temporal response properties reflect the physical sequence of odor filaments, another part is generated by local neuron networks. In honeybees, γ‐aminobutyric acid (GABA)‐ergic and histaminergic neurons both contribute inhibitory networks to the antennal lobe. Interestingly, temporal properties differ markedly in different brain areas. In particular, in the antennal lobe odor‐evoked activity develops over slow time courses, while responses in Kenyon cells are phasic and transient. The termination of an odor stimulus is reflected by a decrease in activity within most glomeruli of the antennal lobe and an off‐response in some glomeruli, while in the mushroom bodies about half of the odor‐activated Kenyon cells also exhibit off‐responses.     

Neural population codes

In many regions of the brain, information is represented by patterns of activity occurring over populations of neurons. Understanding the encoding of information in neural population activity is important both for grasping the fundamental computations underlying brain function, and for interpreting signals that may be useful for the control of prosthetic devices. We concentrate on the representation of information in neurons with Poisson spike statistics, in which information is contained in the average spike firing rate. We analyze the properties of population codes in terms of the tuning functions that describe individual neuron behavior. The discussion centers on three computational questions: first, what information is encoded in a population; second, how does the brain compute using populations; and third, when is a population optimal? To answer these questions, we discuss several methods for decoding population activity in an experimental setting. We also discuss how computation can be performed within the brain in networks of interconnected populations. Finally, we examine questions of optimal design of population codes that may help to explain their particular form and the set of variables that are best represented. We show that for population codes based on neurons that have a Poisson distribution of spike probabilities, the behavior and computational properties of the code can be understood in terms of the tuning properties of individual cells.     

Functions of microglia in the central nervous system--beyond the immune response

Microglia cells are the immune cells of the central nervous system and consequently play important roles in brain infections and inflammation. Recent in vivo imaging studies have revealed that in the resting healthy brain, microglia are highly dynamic, moving constantly to actively survey the brain parenchyma. These active microglia can rapidly respond to pathological insults, becoming activated to induce a range of effects that may contribute to both pathogenesis, or to confer neuronal protection. However, interactions between microglia and neurons are being recognized as important in shaping neural circuit activity under more normal, physiological conditions. During development and neurogenesis, microglia interactions with neurons help to shape the final patterns of neural circuits important for behavior and with implications for diseases. In the mature brain, microglia can respond to changes in sensory activity and can influence neuronal activity acutely and over the long term. Microglia seem to be particularly involved in monitoring the integrity of synaptic function. In this review, we discuss some of these new insights into the involvement of microglia in neural circuits.     

How do features of sensory representations develop?

Sensory representations in the brainstem and cortex have a number of features that support the idea that neural activity patterns are important in their development. Many of these features vary across species in ways that could result from perturbances in the balance of the effects of activity patterns and position-dependent gene expression. (1) Most notably, disruptions or septa in sensory maps often reflect actual discontinuities in the receptor sheet, and the discontinuities may be reflected in a series of interconnected maps. Species with different disruption patterns in sensory sheets have different matching disruption patterns in the sensory maps and variant individuals and strains of the same species have matching variations in the receptor disruption patterns and their sensory maps. (2) In addition, mutations that misdirect some of the retinal afferents from one side of the brain to the other create new sensory maps that preserve continuities in the altered pattern of input, while creating new structural discontinuities. (3) Furthermore, functionally different classes of afferents that are mixed in the receptor sheet often segregate to activate separate populations of target cells. (4) Finally, early developing portions of receptor sheets may gain more than their share of territory in sensory maps. These and other variable features of sensory maps are most readily accommodated by theories that involve roles for instruction by evoked and spontaneous neural activity patterns.     

A teleosemantic approach to information in the brain

The brain is often taken to be a paradigmatic example of a signaling system with semantic and representational properties, in which neurons are senders and receivers of information carried in action potentials. A closer look at this picture shows that it is not as appealing as it might initially seem in explaining the function of the brain. Working from several sender-receiver models within the teleosemantic framework, I will first argue that two requirements must be met for a system to support genuine semantic information: 1. The receiver must be competent—that is, it must be able to extract rewards from its environment on the basis of the signals that it receives. 2. The receiver must have some flexibility of response relative to the signal received. In the second part of the paper, this initial framework will be applied to neural processes, pointing to the surprising conclusion that signaling at the single-neuron level is only weakly semantic at best. Contrary to received views, neurons will have little or no access to semantic information (though their patterns of activity may carry plenty of quantitative, correlational information) about the world outside the organism. Genuine representation of the world requires an organism-level receiver of semantic information, to which any particular set of neurons makes only a small contribution.