Biological pattern generation: the cellular and computational logic of networks in motion

In 1900, Ramón y Cajal advanced the neuron doctrine, defining the neuron as the fundamental signaling unit of the nervous system. Over a century later, neurobiologists address the circuit doctrine: the logic of the core units of neuronal circuitry that control animal behavior. These are circuits that can be called into action for perceptual, conceptual, and motor tasks, and we now need to understand whether there are coherent and overriding principles that govern the design and function of these modules. The discovery of central motor programs has provided crucial insight into the logic of one prototypic set of neural circuits: those that generate motor patterns. In this review, I discuss the mode of operation of these pattern generator networks and consider the neural mechanisms through which they are selected and activated. In addition, I will outline the utility of computational models in analysis of the dynamic actions of these motor networks.     

Learning-related synaptic plasticity: LTP and LTD.

The past several years have seen studies of synaptic plasticity in both invertebrate and vertebrate nervous systems come of age and lead to important new findings. In particular, current evidence points to a possible presynaptic site for long-term potentiation and the involvement of a retrograde messenger from the postsynaptic neuron. Recent advances in both cerebellar and cortical forms of long-term depression are also discussed.     

Nervous system development in the fairy shrimp Branchinella sp. (Crustacea: Branchiopoda: Anostraca): Insights into the development and evolution of the branchiopod brain and its sensory organs

Using immunohistochemical labeling against acetylated a‐tubulin and serotonin in combination with confocal laser scanning microscopy and 3D‐reconstruction, we investigated the temporary freshwater pond inhabitant Branchinella sp. (Crustacea: Branchiopoda: Anostraca) for the first time to provide detailed data on the development of the anostracan nervous system. Protocerebral sense organs such as the nauplius eye and frontal filament organs are present as early as the hatching stage L0. In the postnaupliar region, two terminal pioneer neurons grow from posterior to anterior to connect the mandibular neuromeres. The first protocerebral neuropil to emerge is not part of the central complex but represents the median neuropil, and begins to develop from L0+ onwards. In stage L3, the first evidence of developing compound eyes is visible. This is followed by the formation of the visual neuropils and the neuropils of the central complex in the protocerebrum. From the deutocerebral lobes, the projecting neuron tract proceeds to both sides of the lateral protocerebrum, forming a chiasma just behind the central body. In the postnaupliar region, the peripheral nervous system, commissures and connectives develop along an anterior–posterior gradient after the fasciculation of the terminal pioneer neurons with the mandibular neuromere. The peripheral nervous system in the thoracic segments consists of two longitudinal neurite bundles on each side which connect the intersegmental nerves, together with the ventral nervous system forming an orthogon‐like network. Here, we discuss, among other things, the evidence of a fourth nauplius eye nerve and decussating projecting neuron tract found in Branchinella sp., and provide arguments to support our view that the crustacean frontal filament (organ) and onychophoran primary antenna are homologous. J. Morphol. 277:1423–1446, 2016. © 2016 Wiley Periodicals, Inc.     

Glycolipid-Enriched Caveolae and Caveolae-Like Domains in the Nervous System

Abstract: Recent years have been characterized by a booming interest in research on caveolae and caveolae-like membrane domains. The interest in this subject grew further, when their involvement in fundamental membrane-associated events, such as signal transmission and lipid/protein sorting, was postulated. Substantial progress has been reached in understanding the biological role of membrane domains in eukaryotic cells. The neuron, however, which perhaps represents one of the greatest challenges to research on membrane traffic and function, has only been partially investigated. The purpose of the present review is to survey this issue in the nervous system. We confine ourselves to the presence of membrane domains in the nervous system and discuss this in the context of three facts: first, glycolipids are peculiarly enriched in both caveolae and caveolae-like domains and are particularly abundant in the nervous system; second, the neuron is characterized by a basic dual polarity, similar in this respect to other polarized cells, where the role of glycolipid-enriched domains for lipid/protein sorting has been better ascertained; and third, neurons evolved from, and are related to, simpler eukaryotic cells, allowing us to find analogies with more investigated nonneuronal cells.     

A Neuron Model with Spatially Distributed Synaptic Input

I have assembled a neuron model simulating contiguous patches of nerve cell membrane. With this model I have examined the functional significance of different spatial and temporal distributions of synaptic inputs. The model consists of two terminal electronic analogue circuits with inputs controlled by a LINC computer. One terminal represents the inside of a membrane patch, the other represents the outside. Two circuit designs are used: one simulates spike-generating regions of the neuron, the other simulates subthreshold activity in inexcitable regions. To simulate a neuron, patches are assembled in various spatial arrangements by suitable connection to the “intracellular” nodes. Thus the relation of neuron geometry to aspects of spatiotemporal summation of synaptic inputs can be investigated readily. Performance of the model is assessed by comparison with results from microelectrode studies in the cochlear nucleus of the cat. In particular, the peristimulus time (PST) histogram and averaged membrane potential are used for quantitative comparison. The model suggests that the geometry of the neuron's receptive surface can account for a wide variety of physiologically observed behavior, particularly in response to dynamic stimuli.     

Modulation of stomatogastric rhythms

Neuromodulation by peptides and amines is a primary source of plasticity in the nervous system as it adapts the animal to an ever-changing environment. The crustacean stomatogastric nervous system is one of the premier systems to study neuromodulation and its effects on motor pattern generation at the cellular level. It contains the extensively modulated central pattern generators that drive the gastric mill (chewing) and pyloric (food filtering) rhythms. Neuromodulators affect all stages of neuronal processing in this system, from membrane currents and synaptic transmission in network neurons to the properties of the effector muscles. The ease with which distinct neurons are identified and their activity is recorded in this system has provided considerable insight into the mechanisms by which neuromodulators affect their target cells and modulatory neuron function. Recent evidence suggests that neuromodulators are involved in homeostatic processes and that the modulatory system itself is under modulatory control, a fascinating topic whose surface has been barely scratched. Future challenges include exploring the behavioral conditions under which these systems are activated and how their effects are regulated.     

Emergence of neuron types

Neuron types are the building blocks of the nervous system, and therefore, of functional circuits. Understanding the origin of neuronal diversity has always been an essential question in neuroscience and developmental biology. While knowledge on the molecular control of their diversification has largely increased during the last decades, it is now possible to reveal the dynamic mechanisms and the actual stepwise molecular changes occurring at single-cell level with the advent of single-cell omics technologies and analysis with high temporal resolution. Here, we focus on recent advances in the field and in technical and analytical tools that enable detailed insights into the emergence of neuron types in the central and peripheral nervous systems.     

Introduction to the Symposium: What is Computational Neurobiology?

SYNOPSIS. Current advances in computational biology are derivedfrom two sets of ideas established in the work of Santiago Ramóny Cajal. One is the neuron doctrine, which holds that neuronsare the functional units of the nervous system, and has ledto detailed models of neuronal properties based on increasinginformation on the physical and chemical properties of neurons.The second is the idea of networks of neurons with specificpatterns of interconnections that has led to a variety of mathematicalmethods of analyzing such networks. Future work in computationalneurobiology promises to be a blend of these two modeling approaches.     

Effect of Structure on Function in Model Nerve Nets

A theoretical analysis has been made on the effect of the pattern of interneuronal connectivity in model nerve nets on the activity of these nets. Two types of nets have been investigated: one in which the likelihood of a connection between a given neuron and any other element in the net is given by a Poisson probability distribution, and a second type in which the pattern of interconnection follows a Gaussian distribution. An analytical treatment is presented of the equations for noiseless nets in these two conditions. The principal result is that nets with Poisson connectivity law are activated by extraneous firing of a single neuron and continue in spontaneous activity indefinitely. On the other hand, similar nets in which the connections are, however, distributed according to a normal connectivity law, exhibit a definite threshold and produce spontaneous activity only subsequent to extraneous activation of a substantial fraction of the population. Moreover, spontaneous activity in Gaussian nets, but not in Poisson nets, becomes extinguished if the number of active neurons falls below the critical threshold. Some neuroanatomical implications are discussed which suggest that the pyramidal system of the cerebral cortex and other neuronal systems histologically characterized by large numbers of synapses per neuron may incorporate a Gaussian connectivity law, whereas a Poisson law may be characteristic of these cortical layers and nuclei primarily containing granule cells.     

Calbindin-D32k is localized to a subpopulation of neurons in the nervous system of the sea cucumber Holothuria glaberrima (Echinodermata)

Members of the calbindin subfamily serve as markers of subpopulations of neurons within the vertebrate nervous system. Although markers of these proteins are widely available and used, their application to invertebrate nervous systems has been very limited. In this study we investigated the presence and distribution of members of the calbindin subfamily in the sea cucumber Holothuria glaberrima (Selenka, 1867). Immunohistological experiments with antibodies made against rat calbindin 1, parvalbumin, and calbindin 2, showed that these antibodies labeled cells and fibers within the nervous system of H. glaberrima. Most of the cells and fibers were co-labeled with the neural-specific marker RN1, showing their neural specificity. These were distributed throughout all of the nervous structures, including the connective tissue plexi of the body wall and podia. Bioinformatics analyses of the possible antigen recognized by these markers showed that a calbindin 2-like protein present in the sea urchin Strongylocentrotus purpuratus, corresponded to the calbindin-D32k previously identified in other invertebrates. Western blots with anti-calbindin 1 and anti-parvalbumin showed that these markers recognized an antigen of approximately 32 kDa in homogenates of radial nerve cords of H. glaberrima and Lytechinus variegatus. Furthermore, immunoreactivity with anti-calbindin 1 and anti-parvalbumin was obtained to a fragment of calbindin-D32k of H. glaberrima. Our findings suggest that calbindin-D32k is present in invertebrates and its sequence is more similar to the vertebrate calbindin 2 than to calbindin 1. Thus, characterization of calbindin-D32k in echinoderms provides an important view of the evolution of this protein family and represents a valuable marker to study the nervous system of invertebrates.     

Towards a general theory of neural computation based on prediction by single neurons

Although there has been tremendous progress in understanding the mechanics of the nervous system, there has not been a general theory of its computational function. Here I present a theory that relates the established biophysical properties of single generic neurons to principles of Bayesian probability theory, reinforcement learning and efficient coding. I suggest that this theory addresses the general computational problem facing the nervous system. Each neuron is proposed to mirror the function of the whole system in learning to predict aspects of the world related to future reward. According to the model, a typical neuron receives current information about the state of the world from a subset of its excitatory synaptic inputs, and prior information from its other inputs. Prior information would be contributed by synaptic inputs representing distinct regions of space, and by different types of non-synaptic, voltage-regulated channels representing distinct periods of the past. The neuron's membrane voltage is proposed to signal the difference between current and prior information ("prediction error" or "surprise"). A neuron would apply a Hebbian plasticity rule to select those excitatory inputs that are the most closely correlated with reward but are the least predictable, since unpredictable inputs provide the neuron with the most "new" information about future reward. To minimize the error in its predictions and to respond only when excitation is "new and surprising," the neuron selects amongst its prior information sources through an anti-Hebbian rule. The unique inputs of a mature neuron would therefore result from learning about spatial and temporal patterns in its local environment, and by extension, the external world. Thus the theory describes how the structure of the mature nervous system could reflect the structure of the external world, and how the complexity and intelligence of the system might develop from a population of undifferentiated neurons, each implementing similar learning algorithms.     

Further characterization of the PW peptide family that inhibits neuron differentiation in <Emphasis Type="Italic">Hydra</Emphasis>

From an evolutionary point of view, Hydra has one of the most primitive nervous systems among metazoans. Two different groups of peptides that affect neuron differentiation were identified in a systematic screening of peptide signaling molecules in Hydra. Within the first group of peptides, a neuropeptide, Hym-355, was previously shown to positively regulate neuron differentiation. The second group of peptides encompasses the PW family of peptides that negatively regulate neuron differentiation. In this study, we identified the gene encoding PW peptide preprohormone. Moreover, we made the antibody that specifically recognizes LPW. In situ hybridization and immunohistochemical analyses showed that the PW peptides and the gene encoding them were expressed in ectodermal epithelial cells throughout the body except for the basal disk. The PW peptides are produced by epithelial cells and are therefore termed “epitheliopeptides.” Together with Hym-355, the PW family peptides mediate communication between neurons and epithelial cells and thereby maintain a specific density of neurons in Hydra. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users. Toshio Takahashi, Osamu Koizumi equally contributed to this study.     

Analytical and Simulation Results for Stochastic Fitzhugh-Nagumo Neurons and Neural Networks

An analytical approach is presented for determining the response of a neuron or of the activity in a network of connected neurons, represented by systems of nonlinear ordinary stochastic differential equations—the Fitzhugh-Nagumo system with Gaussian white noise current. For a single neuron, five equations hold for the first- and second-order central moments of the voltage and recovery variables. From this system we obtain, under certain assumptions, five differential equations for the means, variances, and covariance of the two components. One may use these quantities to estimate the probability that a neuron is emitting an action potential at any given time. The differential equations are solved by numerical methods. We also perform simulations on the stochastic Fitzugh-Nagumo system and compare the results with those obtained from the differential equations for both sustained and intermittent deterministic current inputs withsuperimposed noise. For intermittent currents, which mimic synaptic input, the agreement between the analytical and simulation results for the moments is excellent. For sustained input, the analytical approximations perform well for small noise as there is excellent agreement for the moments. In addition, the probability that a neuron is spiking as obtained from the empirical distribution of the potential in the simulations gives a result almost identical to that obtained using the analytical approach. However, when there is sustained large-amplitude noise, the analytical method is only accurate for short time intervals. Using the simulation method, we study the distribution of the interspike interval directly from simulated sample paths. We confirm that noise extends the range of input currents over which (nonperiodic) spike trains may exist and investigate the dependence of such firing on the magnitude of the mean input current and the noise amplitude. For networks we find the differential equations for the means, variances, and covariances of the voltage and recovery variables and show how solving them leads to an expression for the probability that a given neuron, or given set of neurons, is firing at time t. Using such expressions one may implement dynamical rules for changing synaptic strengths directly without sampling. The present analytical method applies equally well to temporally nonhomogeneous input currents and is expected to be useful for computational studies of information processing in various nervous system centers.     

The neuronal ensembles of the brain

The conclusion about availability of two types of neuron ensembles in the central nervous structures was made on the basis of proper experimental studies and appropriate literature data analysis. The first is presented by neurons functional cooperation (Hebbian ensembles) formed during learning process on the basis of synchronization by high-frequency oscillator activity. The second is presented by morphological-and-functional structures (Kogan's neuron ensembles) formed during neurogenesis process and genetically destined for biologically valuable patterns recognition. The control of functional state of the brain neuron networks and occurred information processes is realized by non-specific systems either restrict the neurons capability of joining in the rate of global synchronization by synchronization and desynchronization actions or form and maintain the common rhythm of activity in considerable neuron populations. The latter block single elements ability to enter into specific interactions.     

Neural and Pavlovian influences on immunity

The traditional view that the nervous and immune systems are functionally independent (aside from general stress effects and autoimmune disorders of the nervous system) is being challenged by a new view that the nervous system regulates the activity of the immune system. If this is true, it should be possible to change the activity of the immune system by means of Pavlovian conditioning, just as it is possible to condition other physiological events influenced by the autonomic nervous system or neuroendocrine substances. Evidence for autonomic and neuroendocrine modulation of immune activity is briefly reviewed; and, the various studies reporting conditioned immune effects, the physiological mechanisms most likely involved, and their possible significance are discussed.     

Gliarin and macrolin,two novel intermediate filament proteins specifically expressed in sets and subsets of glial cells in leech central nervous system

Using monoclonal antibodies, we have identified two novel intermediate filament (IF) proteins, Gliarin and Macrolin, which are specifically expressed in the central nervous system of an invertebrate. The two proteins both contain the coiled‐coil rod domain typical of the superfamily of IF proteins flanked by unique N‐ and C‐terminal domains. Gliarin was found in all glial cells including macro‐ and microglial cells, whereas Macrolin was expressed in only a single pair of giant connective glial cells. The identification of Macrolin and Gliarin together with the characterization of the strictly neuronal IF protein Filarin in leech central nervous system demonstrate that multiple neuron‐ and glial‐specific IFs are not unique to the vertebrate nervous system but are also found in invertebrates. Interestingly, phylogenetic analysis based on maximum parsimony indicated that the presence of neuron‐ and glial cell–specific IFs in coelomate protostomes as well as in vertebrates is not of monophyletic origin, but rather represents convergent evolution and appears to have arisen independently. © 1999 John Wiley & Sons, Inc. J Neurobiol 40: 244–253, 1999     

Selectivity of chemoreceptor neuron

Discriminating ability (selectivity) of chemoreceptor neuron is compared with that of its receptor proteins. The process of neuronal triggering is expected to be cooperative and threshold-type in a sense that the neuron can fire if and only if the number of its receptor proteins, which are bound with odor molecules, is above a definite threshold. Both deterministic and stochastic pictures are considered. The stochastic case is treated based on birth and death stochastic process and first passage technique. In both pictures, it is shown that a chemoreceptor neuron can have much a higher selectivity than its individual receptor proteins, provided the chemical stimuli are presented at low concentrations, and the threshold is high enough. This is in agreement with a preliminary estimate based on simplified probabilistic reasoning (Vidybida, A.K., 1999. Cooperative mechanism for improving the discriminating ability in the chemoreceptive neuron. Binomial case. Biol. Cybern. 81, 469-473). The mechanism of selectivity improvement is similar to that described before in cooperative chemical systems. A possibility for this mechanism to be valid at higher stages of processing of chemical signals, as well as in other sensory systems is discussed.     

Metabolic regulation of neuron functions

A short analytical review of Soviet and foreign studies devoted to metabolic regulation of neuron functioning is presented. It is shown that the electrical activity of neuron is accompanied by structural changes and an increase in RNA content. It is assumed that these changes are initiated by calcium ions, coming to the neuron during its excitation. The principles of metabolic regulation of functioning of electro- and chemoexcitable neuronal membranes are considered. It is shown that the metabolic reactions can be an intermediate between the activated chemoreceptor and the electrical response of the cell. Evidence is presented that the electroexcitable and chemosensitive structures in the membrane modulate each other. The role of intracellular messengers in regulating the activity of receptors and channels is considered. Possible mechanisms for their modifications under the influence of cellular factors are discussed. It is shown that the work of neuron is controlled by several metabolic regulatory systems simultaneously.     

Experimental ‘omics’ data in tree research: facing complexity

High-throughput experimental technology has provided insight into the inner functioning of plants. The current experimental technology facilitates the study of plant systems in a holistic manner, measuring observables from the genome, proteome, and metabolome up to the level of the ecosystem. The call for a systemic view in plant research is being made from multiple research fields. Although not yet fully developed for tree research, data sources are also rapidly growing in this area. Nevertheless, there are challenges and pitfalls in dealing with such increases in data. Some of these difficulties are deeply rooted in the complexity of the evolutionary systems. The lessons from complexity theory are rooted in studies performed several decades ago. Honouring principles that were formulated before bioinformatics and systems biology had been introduced facilitates the derivation of analytical methods with the potential to overcome these challenges in several ways.