The search for a hippocampal engram

Understanding the molecular and cellular changes that underlie memory, the engram, requires the identification, isolation and manipulation of the neurons involved. This presents a major difficulty for complex forms of memory, for example hippocampus-dependent declarative memory, where the participating neurons are likely to be sparse, anatomically distributed and unique to each individual brain and learning event. In this paper, I discuss several new approaches to this problem. In vivo calcium imaging techniques provide a means of assessing the activity patterns of large numbers of neurons over long periods of time with precise anatomical identification. This provides important insight into how the brain represents complex information and how this is altered with learning. The development of techniques for the genetic modification of neural ensembles based on their natural, sensory-evoked, activity along with optogenetics allows direct tests of the coding function of these ensembles. These approaches provide a new methodological framework in which to examine the mechanisms of complex forms of learning at the level of the neurons involved in a specific memory.     

A guide into glycosciences: How chemistry,biochemistry and biology cooperate to crack the sugar code

BackgroundThe most demanding challenge in research on molecular aspects within the flow of biological information is posed by the complex carbohydrates (glycan part of cellular glycoconjugates). How the ‘message’ encoded in carbohydrate ‘letters’ is ‘read’ and ‘translated’ can only be unraveled by interdisciplinary efforts.Scope of reviewThis review provides a didactic step-by-step survey of the concept of the sugar code and the way strategic combination of experimental approaches characterizes structure–function relationships, with resources for teaching.Major conclusionsThe unsurpassed coding capacity of glycans is an ideal platform for generating a broad range of molecular ‘messages’. Structural and functional analyses of complex carbohydrates have been made possible by advances in chemical synthesis, rendering production of oligosaccharides, glycoclusters and neoglycoconjugates possible. This availability facilitates to test the glycans as ligands for natural sugar receptors (lectins). Their interaction is a means to turn sugar-encoded information into cellular effects. Glycan/lectin structures and their spatial modes of presentation underlie the exquisite specificity of the endogenous lectins in counterreceptor selection, that is, to home in on certain cellular glycoproteins or glycolipids.General significanceUnderstanding how sugar-encoded ‘messages’ are ‘read’ and ‘translated’ by lectins provides insights into fundamental mechanisms of life, with potential for medical applications.     

Fish as a model in social neuroscience: conservation and diversity in the social brain network

Mechanisms for fish social behaviours involve a social brain network (SBN) which is evolutionarily conserved among vertebrates. However, considerable diversity is observed in the actual behaviour patterns amongst nearly 30000 fish species. The huge variation found in socio-sexual behaviours and strategies is likely generated by a morphologically and genetically well-conserved small forebrain system. Hence, teleost fish provide a useful model to study the fundamental mechanisms underlying social brain functions. Herein we review the foundations underlying fish social behaviours including sensory, hormonal, molecular and neuroanatomical features. Gonadotropin-releasing hormone neurons clearly play important roles, but the participation of vasotocin and isotocin is also highlighted. Genetic investigations of developing fish brain have revealed the molecular complexity of neural development of the SBN. In addition to straightforward social behaviours such as sex and aggression, new experiments have revealed higher order and unique phenomena such as social eavesdropping and social buffering in fish. Finally, observations interpreted as ‘collective cognition’ in fish can likely be explained by careful observation of sensory determinants and analyses using the dynamics of quantitative scaling. Understanding of the functions of the SBN in fish provide clues for understanding the origin and evolution of higher social functions in vertebrates.     

Electrogenic Na⁺/HCO₃⁻ co-transporter-1 is essential for the parathyroid hormone-stimulated intestinal HCO₃⁻ secretion

Acidosis, associated with metabolic disorders, leads to the pathological changes of cognition and behavior in the clinical practices of neurology and psychology. The cellular mechanisms underlying these cerebral dysfunctions remain unclear. By using electrophysiological approach and changing extracellular pH, we have investigated the effects of acidic environment on cortical GABAergic neurons in terms of their abilities of firing spikes and responses to synaptic inputs. Artificial cerebral spinal fluid in low pH impairs the responses to excitatory synaptic inputs and the abilities of encoding sequential spikes at these GABAergic neurons. The impairments of neuronal spiking are associated with the increases of refractory periods and threshold potentials. Our studies reveal that acidosis may impair cortical GABAergic neurons and in turn deteriorate brain functions, in which their final targets are voltage-gated sodium channels and glutamate receptor-channels.     

Neural mechanobiology and neuronal vulnerability to traumatic loading

In order to understand the physical tolerance of neurons to traumatic insults, engineers and neuroscientists have attempted to reproduce the biomechanical environment during a traumatic event using in vitro injury systems with isolated components of the nervous system. This approach allows one to begin to unravel the underlying molecular and biochemical mechanisms that lead to cell dysfunction and death as a function of mechanical inputs. Excess mechanical force and deformation causes structural and functional breakdown, including several key deleterious cellular processes, such as membrane damage, an upset of calcium homeostasis, glutamate release, cell death, and caspase-mediated proteolysis. Understanding of the mechanotransduction events, however, that lead to cellular failure and dysfunction, are not well understood. Mechanically characterized cellular models of traumatic loading are critical to the improved understanding of mechanotransduction in the context of neural injury, the improvement of protective systems, and to provide a controlled setting for testing therapeutic interventions. In this review of the cellular mechanics of traumatic neural loading, we focus on the backdrop and motivation for studying mechanical thresholds in neurons and glial cells and discuss some of the acute responses that may help elucidate improved tolerance criteria and illuminate future research directions.     

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.     

Do Brain Networks Evolve by Maximizing Their Information Flow Capacity?

We propose a working hypothesis supported by numerical simulations that brain networks evolve based on the principle of the maximization of their internal information flow capacity. We find that synchronous behavior and capacity of information flow of the evolved networks reproduce well the same behaviors observed in the brain dynamical networks of Caenorhabditis elegans and humans, networks of Hindmarsh-Rose neurons with graphs given by these brain networks. We make a strong case to verify our hypothesis by showing that the neural networks with the closest graph distance to the brain networks of Caenorhabditis elegans and humans are the Hindmarsh-Rose neural networks evolved with coupling strengths that maximize information flow capacity. Surprisingly, we find that global neural synchronization levels decrease during brain evolution, reflecting on an underlying global no Hebbian-like evolution process, which is driven by no Hebbian-like learning behaviors for some of the clusters during evolution, and Hebbian-like learning rules for clusters where neurons increase their synchronization.     

O-GlcNAc cycling: Emerging roles in development and epigenetics

The nutrient-sensing hexosamine signaling pathway modulates the levels of O-linked N-acetylglucosamine (O-GlcNAc) on key targets impacting cellular signaling, protein turnover and gene expression. O-GlcNAc cycling may be deregulated in neurodegenerative disease, cancer, and diabetes. Studies in model organisms demonstrate that the O-GlcNAc transferase (OGT/Sxc) is essential for Polycomb group (PcG) repression of the homeotic genes, clusters of genes responsible for the adult body plan. Surprisingly, from flies to man, the O-GlcNAcase (OGA, MGEA5) gene is embedded within the NK cluster, the most evolutionarily ancient of three homeobox gene clusters regulated by PcG repression. PcG repression also plays a key role in maintaining stem cell identity, recruiting the DNA methyltransferase machinery for imprinting, and in X-chromosome inactivation. Intriguingly, the Ogt gene resides near the Xist locus in vertebrates and is subject to regulation by PcG-dependent X-inactivation. OGT is also an enzymatic component of the human dosage compensation complex. These ‘evo-devo’ relationships linking O-GlcNAc cycling to higher order chromatin structure provide insights into how nutrient availability may influence the epigenetic regulation of gene expression. O-GlcNAc cycling at promoters and PcG repression represent concrete mechanisms by which nutritional information may be transmitted across generations in the intra-uterine environment. Thus, the nutrient-sensing hexosamine signaling pathway may be a key contributor to the metabolic deregulation resulting from prenatal exposure to famine, or the ‘vicious cycle’ observed in children of mothers with type-2 diabetes and metabolic disease.     

Timing as an intrinsic property of neural networks: evidence from in vivo and in vitro experiments

The discrimination and production of temporal patterns on the scale of hundreds of milliseconds are critical to sensory and motor processing. Indeed, most complex behaviours, such as speech comprehension and production, would be impossible in the absence of sophisticated timing mechanisms. Despite the importance of timing to human learning and cognition, little is known about the underlying mechanisms, in particular whether timing relies on specialized dedicated circuits and mechanisms or on general and intrinsic properties of neurons and neural circuits. Here, we review experimental data describing timing and interval-selective neurons in vivo and in vitro. We also review theoretical models of timing, focusing primarily on the state-dependent network model, which proposes that timing in the subsecond range relies on the inherent time-dependent properties of neurons and the active neural dynamics within recurrent circuits. Within this framework, time is naturally encoded in populations of neurons whose pattern of activity is dynamically changing in time. Together, we argue that current experimental and theoretical studies provide sufficient evidence to conclude that at least some forms of temporal processing reflect intrinsic computations based on local neural network dynamics.     

Modulating effect of cytokines on mechanisms of synaptic plasticity in the brain

After accumulation of data showing that resident brain cells (neurons, astrocytes, and microglia) produce mediators of the immune system, such as cytokines and their receptors under normal physiological conditions, a critical need emerged for investigating the role of these mediators in cognitive processes. The major problem for understanding the functional role of cytokines in the mechanisms of synaptic plasticity, de novo neurogenesis, and learning and memory is the small number of investigated cytokines. Existing concepts are based on data from just three proinflammatory cytokines: interleukin-1 beta, interleukin-6, and tumor necrosis factor-alpha. The amount of information in the literature on the functional role of antiinflammatory cytokines in the mechanisms of synaptic plasticity and cognitive functions of mature mammalian brain is dismally low. However, they are of principle importance for understanding the mechanisms of local information processing in the brain, since they modulate the activity of individual cells and local neural networks, being able to reconstruct the processes of synaptic plasticity and intercellular communication, in general, depending on the local ratio of the levels of different cytokines in certain areas of the brain. Understanding the functional role of cytokines in cellular mechanisms of information processing and storage in the brain would allow developing preventive and therapeutic means for the treatment of neuropathologies related to impairment of these mechanisms.     

Incremental mutual information: a new method for characterizing the strength and dynamics of connections in neuronal circuits

Understanding the computations performed by neuronal circuits requires characterizing the strength and dynamics of the connections between individual neurons. This characterization is typically achieved by measuring the correlation in the activity of two neurons. We have developed a new measure for studying connectivity in neuronal circuits based on information theory, the incremental mutual information (IMI). By conditioning out the temporal dependencies in the responses of individual neurons before measuring the dependency between them, IMI improves on standard correlation-based measures in several important ways: 1) it has the potential to disambiguate statistical dependencies that reflect the connection between neurons from those caused by other sources (e.g. shared inputs or intrinsic cellular or network mechanisms) provided that the dependencies have appropriate timescales, 2) for the study of early sensory systems, it does not require responses to repeated trials of identical stimulation, and 3) it does not assume that the connection between neurons is linear. We describe the theory and implementation of IMI in detail and demonstrate its utility on experimental recordings from the primate visual system.     

Aplysia Ganglia Preparation for Electrophysiological and Molecular Analyses of Single Neurons

A major challenge in neurobiology is to understand the molecular underpinnings of neural circuitry that govern a specific behavior. Once the specific molecular mechanisms are identified, new therapeutic strategies can be developed to treat abnormalities in specific behaviors caused by degenerative diseases or aging of the nervous system. The marine snail Aplysia californica is well suited for the investigations of cellular and molecular basis of behavior because neural circuitry underlying a specific behavior could be easily determined and the individual components of the circuitry could be easily manipulated. These advantages of Aplysia have led to several fundamental discoveries of neurobiology of learning and memory. Here we describe a preparation of the Aplysia nervous system for the electrophysiological and molecular analyses of individual neurons. Briefly, ganglion dissected from the nervous system is exposed to protease to remove the ganglion sheath such that neurons are exposed but retain neuronal activity as in the intact animal. This preparation is used to carry out electrophysiological measurements of single or multiple neurons. Importantly, following the recording using a simple methodology, the neurons could be isolated directly from the ganglia for gene expression analysis. These protocols were used to carry out simultaneous electrophysiological recordings from L7 and R15 neurons, study their response to acetylcholine and quantitating expression of CREB1 gene in isolated single L7, L11, R15, and R2 neurons of Aplysia.