The problem of using correlation methods in brain function]

In a series of publications, the author used the correlation hypothesis to explain the main mechanisms of functioning of bat and dolphin echo locators. A good fit of calculated parameters to experimental data was shown. In this work, by the example of generalization of the recognition problem, the use by the brain of correlation methods for solving more general problems independently on the modality of sensor signals was analyzed. In favor of the hypothesis that correlation methods are widely used in brain functioning are the data presented in the paper, which prove that an associative neuron is a suitable analog computer. It is suited for rapid "computation" of the intercorrelation function of discrete input and reference signals. The set of weighting coefficients of neuron synaptic inputs serve as such signals. The pool of associative neurons determines the values on the correlation function in the required range by changing the "numbers" of inputs from neuron to neuron at which discrete signals arrive.     

Hemispheric Asymmetry in New Neurons in Adulthood Is Associated with Vocal Learning and Auditory Memory

Many brain regions exhibit lateral differences in structure and function, and also incorporate new neurons in adulthood, thought to function in learning and in the formation of new memories. However, the contribution of new neurons to hemispheric differences in processing is unknown. The present study combines cellular, behavioral, and physiological methods to address whether 1) new neuron incorporation differs between the brain hemispheres, and 2) the degree to which hemispheric lateralization of new neurons correlates with behavioral and physiological measures of learning and memory. The songbird provides a model system for assessing the contribution of new neurons to hemispheric specialization because songbird brain areas for vocal processing are functionally lateralized and receive a continuous influx of new neurons in adulthood. In adult male zebra finches, we quantified new neurons in the caudomedial nidopallium (NCM), a forebrain area involved in discrimination and memory for the complex vocalizations of individual conspecifics. We assessed song learning and recorded neural responses to song in NCM. We found significantly more new neurons labeled in left than in right NCM; moreover, the degree of asymmetry in new neuron numbers was correlated with the quality of song learning and strength of neuronal memory for recently heard songs. In birds with experimentally impaired song quality, the hemispheric difference in new neurons was diminished. These results suggest that new neurons may contribute to an allocation of function between the hemispheres that underlies the learning and processing of complex signals.     

Pressure sensation by an anterior pagoda neuron population distributed in multiple ganglia in the leech, Whitmania pigra

Sensory processing of pressure signals in the central nervous system of the leech, Whitmania pigra, was studied through the interaction between pressure sensory neurons and anterior pagoda neurons. The responses of anterior pagoda neurons to one pulse or a train of pulses in pressure sensory neurons were characterized by the latency and amplitude of excitatory postsynaptic potentials. Here we show that each pressure sensory neuron is able to activate all the anterior pagoda neurons throughout the leech central nervous system. The response patterns of all anterior pagoda neurons were appropriate to the pressure location: in the longitudinal direction the anterior pagoda neuron further away from the pressure sensory neuron had a smaller response with longer latency; inside each ganglion, the anterior pagoda neuron on the contralateral side had a larger response with shorter latency than that on the ipsilateral side. All anterior pagoda neurons excited by pressure sensory neurons comprised a parallel system in which each anterior pagoda neuron was independent from the others. The location information of pressure stimuli was represented through the response of all 40 anterior pagoda neurons covering the whole leech body with a specific pattern of latency and amplitude.     

Zn2+, mitochondria and neuronal injury

In addition to its familiar role as a component of metalloproteins, zinc is also sequestered in the presynaptic vesicles in 'zinc-containing' neurons. The best-established physiological role of synaptically released zinc is the tonic modulation of brain excitability through modulation of amino acid receptors; prominent pathological effects include acceleration of plaque deposition in Alzheimer's disease and exacerbation of excitotoxic neuron injury. Synaptically released zinc functions as a conventional synaptic neurotransmitter or neuromodulator being released into the cleft then recycled into the postsynaptic neurons during synaptic events, functioning analogously to calcium in this regard, as a transmembrane neural signal. To stimulate comparisons of zinc signals with calcium signals, we have compiled a list of the important parameters of calcium signals and zinc signals. More speculatively, we hypothesize that zinc signals may loosely mimic phosphate signals in the sense that signal zinc ions may commonly bind to proteins in a lasting manner, as a result changing their structure and function.     

Synaptically released zinc: neuromodulator and toxin

In addition to its familiar role as a component of metalloproteins, zinc is also sequestered in the presynaptic vesicles in ‘zinc‐containing’ neurons. The best‐established physiological role of synaptically released zinc is the tonic modulation of brain excitability through modulation of amino acid receptors; prominent pathological effects include acceleration of plaque deposition in Alzheimer's disease and exacerbation of excitotoxic neuron injury. Synaptically released zinc functions as a conventional synaptic neurotransmitter or neuromodulator being released into the cleft then recycled into the postsynaptic neurons during synaptic events, functioning analogously to calcium in this regard, as a transmembrane neural signal. To stimulate comparisons of zinc signals with calcium signals, we have compiled a list of the important parameters of calcium signals and zinc signals. More speculatively, we hypothesize that zinc signals may loosely mimic phosphate signals in the sense that signal zinc ions may commonly bind to proteins in a lasting manner, as a result changing their structure and function.     

Artificial selection on brain size leads to matching changes in overall number of neurons

Neurons are the basic computational units of the brain, but brain size is the predominant surrogate measure of brain functional capacity in comparative and cognitive neuroscience. This approach is based on the assumption that larger brains harbor higher numbers of neurons and their connections, and therefore have a higher information‐processing capacity. However, recent studies have shown that brain mass may be less strongly correlated with neuron counts than previously thought. Till now, no experimental test has been conducted to examine the relationship between evolutionary changes in brain size and the number of brain neurons. Here, we provide such a test by comparing neuron number in artificial selection lines of female guppies (Poecilia reticulata) with >15% difference in relative brain mass and numerous previously demonstrated cognitive differences. Using the isotropic fractionator, we demonstrate that large‐brained females have a higher overall number of neurons than small‐brained females, but similar neuronal densities. Importantly, this difference holds also for the telencephalon, a key region for cognition. Our study provides the first direct experimental evidence that selection for brain mass leads to matching changes in number of neurons and shows that brain size evolution is intimately linked to the evolution of neuron number and cognition.     

Spatial and non-spatial functions of the parietal cortex

Although the parietal cortex is traditionally associated with spatial attention and sensorimotor integration, recent evidence also implicates it in higher order cognitive functions. We review relevant results from neuron recording studies showing that inferior parietal neurons integrate information regarding target location with a variety of non-spatial signals. Some of these signals are modulatory and alter a stimulus-evoked response according to the action, category, or reward associated with the stimulus. Other non-spatial inputs act independently, encoding the context or rules of a task even before the presentation of a specific target. Despite the ubiquity of non-spatial information in individual neurons, reversible inactivation of the parietal lobe affects only spatial orienting of attention and gaze, but not non-spatial aspects of performance. This suggests that non-spatial signals contribute to an underlying spatial computation, possibly allowing the brain to determine which targets are worthy of attention or action in a given task context.     

Diazepam Binding Inhibitor (DBI) processing: Immunohistochemical studies in the rat brain

Diazepam Binding Inhibitor (DBI) is an endogenous 11-kDa peptide originally isolated from rat brain. In rat brain DBI coexists with at least three different processing products and the members of this peptide family have been shown to displace benzodiazepines and beta carbolines from recognition sites located on the allosteric modulatory centers of GABA_A receptors. Immunocytochemical methods were used to study the location of DBI and two of the processing products, octadecaneuropeptide (ODN) DBI 33-50 and triakontatetraneuropeptide (TTN) DBI 17-50, in rat brain. DBI-LI was found in selected neuronal perikarya and in many glia and glial-like cells. All circumventricular organs displayed a strong DBI like immunoreactivity (LI). The distribution and cellular location of the ODN-LI and TTN-LI differed from that of DBI because they were preferentially associated with DBI in neurons, but not in glia or glial-like cells. The presence of DBI, but not of its processing products, in glial cells, circumventricular organs, and cells of peripheral tissues suggests that the function of this peptide may extend to other yet unknown function in addition to an action on the allosteric modulatory center of GABA_A receptors located in neurons.     

Synaptically released zinc: Physiological functions and pathological effects

In addition to its familiar role as a component of metalloproteins, zinc is also sequestered in the presynaptic vesicles of a specialized type of neurons called `zinc-containing' neurons. Here we review the physiological and pathological effects of the release of zinc from these zinc-containing synaptic terminals. The best-established physiological role of synaptically released zinc is the tonic modulation of brain excitability through modulation of amino acid receptors; prominent pathological effects include acceleration of plaque deposition in Alzheimer's disease and exacerbation of excitotoxic neuron injury. Synaptically released zinc functions as a conventional synaptic neurotransmitter or neuromodulator, being released into the cleft, then recycled into the presynaptic terminal. Beyond this, zinc also has the highly unconventional property that it passes into postsynaptic neurons during synaptic events, functioning analogously to calcium in this regard, as a transmembrane neural signal. To stimulate comparisons of zinc signals with calcium signals, we have compiled a list of the important parameters of calcium signals and zinc signals. More speculatively, we hypothesize that zinc signals may loosely mimic phosphate `signals' in the sense that signal zinc ions may commonly bind to proteins in a lasting manner (i.e., `zincylating' the proteins) with consequential changes in protein structure and function.     

Gamma oscillations as a mechanism for selective information transmission

In the past decades, many studies have focussed on the relation between the input and output of neurons with the aim to understand information processing by neurons. A particular aspect of neuronal information, which has not received much attention so far, concerns the problem of information transfer when a neuron or a population of neurons receives input from two or more (populations of) neurons, in particular when these (populations of) neurons carry different types of information. The aim of the present study is to investigate the responses of neurons to multiple inputs modulated in the gamma frequency range. By a combination of theoretical approaches and computer simulations, we test the hypothesis that enhanced modulation of synchronized excitatory neuronal activity in the gamma frequency range provides an advantage over a less synchronized input for various types of neurons. The results of this study show that the spike output of various types of neurons [i.e. the leaky integrate and fire neuron, the quadratic integrate and fire neuron and the Hodgkin–Huxley (HH) neuron] and that of excitatory–inhibitory coupled pairs of neurons, like the Pyramidal Interneuronal Network Gamma (PING) model, is highly phase-locked to the larger of two gamma-modulated input signals. This implies that the neuron selectively responds to the input with the larger gamma modulation if the amplitude of the gamma modulation exceeds that of the other signals by a certain amount. In that case, the output of the neuron is entrained by one of multiple inputs and that other inputs are not represented in the output. This mechanism for selective information transmission is enhanced for short membrane time constants of the neuron.     

The Quasi-Fractal Structure of Fish Brain Neurons

The box-counting method for calculating the fractal dimension (D) with the ImageJ 1.20s software is used as a tool for quantitative analysis of the neuronal morphology in the fish brain. The fractal dimension was determined for several types of neurons in the brain of two teleost species, Pholidapus dybowskii and Oncorhynchus keta. These results were compared with those obtained for some neurons of the human brain. The fractal (fractional) dimension (D), as a quantitative index of filling of two-dimensional space by the black and white image of a cell, is shown to vary from 1.22 to 1.72 depending on the type of neuron. The fractal dimension reaches its maximum in less specialized neurons that carry out a number of different functions. On the other hand, highly specialized neurons display a relatively low fractal dimension. Thus, the fractal dimension serves as a numerical measure of the spatial complexity of the neuron and correlates with the morphofunctional organization of the cell.     

A correlational analysis of electroencephalograms based on the modeling of biopotentials of the cerebral cortex

Our previous study on the quantitative nonlinear analysis of integral equations of the averaged membrane potentials in excitatory (the EEG analogue) and inhibitory neurons of the neocortex has shown that the characteristic equation has a set of oscillating solutions with negative decrements in the stability region. We have shown that an electroencephalogram can be represented as a convolution of harmonic functions with negative decrements and discrete (uniformly discontinuous) white Gaussian noise. We have suggested methods of decrement calculation in encephalograms using correlation functions and tested them on both modeled processes with preset parameters and actual encephalograms of rats and mice. Investigation of decrements and amplitude-frequency parameters potentially increases the capacity of spectral correlation analysis of electroencephalograms and expands the results of mathematical processing of brain signals.     

Blockade of glutamate excitotoxicity and its clinical applications

Glutamate has long been known to play a vital role in the normal functioning of neurons, serving as the main excitatory neurotransmitter in the central nervous system. The normal function of glutamate, as a means of communication from one neuron to the next, breaks down in certain disease states. Under particular scrutiny has been the etiology of neuronal damage caused by ischemic disease, seen most commonly in cerebrovascular embolic disease, commonly known as a stroke. It has been shown that damage associated with ischemic disease in the brain is not a direct result of hypoxia or deprivation of metabolic intermediates. In fact, the crucial role is played by an excessive efflux of glutamate by ischemic neurons, which then in turn activates pathways in post-synaptic neurons leading to acute cell swelling and later, cell death. An extremely hopeful development in the field of glutamate excitotoxicity has been the application of therapeutic methods aimed at attenuating the damaging action of glutamate, in an effort to decrease morbidity associated with such common diseases as stroke and other neurodegenerative disorders.Special issue dedicated to Dr. Claude Baxter.     

Inside the brain of a neuron

For many decades, neurons were considered to be the elementary computational units of the brain and were assumed to summate incoming signals and elicit action potentials only in response to suprathreshold stimuli. Although modelling studies predicted that single neurons constitute a much more powerful computational entity, able to perform an array of nonlinear calculations, this possibility was not explored experimentally until the discovery of active mechanisms in the dendrites of most neuron types. Here, we review several modelling studies that have addressed information processing in single neurons, starting with those characterizing the arithmetic of different dendritic components, to those tackling neuronal integration at the cell body and, finally, those analysing the computational abilities of the axon. We present modelling predictions along with supporting experimental data in an effort to highlight the significant contribution of modelling work to enhancing our understanding of single-neuron arithmetic.     

The research of constructing dynamic cognition model based on brain network

Estimating the functional interactions and connections between brain regions to corresponding process in cognitive, behavioral and psychiatric domains is a central pursuit for understanding the human connectome. Few studies have examined the effects of dynamic evolution on cognitive processing and brain activation using brain network model in scalp electroencephalography (EEG) data. Aim of this study was to investigate the brain functional connectivity and construct dynamic programing model from EEG data and to evaluate a possible correlation between topological characteristics of the brain connectivity and cognitive evolution processing. Here, functional connectivity between brain regions is defined as the statistical dependence between EEG signals in different brain areas and is typically determined by calculating the relationship between regional time series using wavelet coherence. We present an accelerated dynamic programing algorithm to construct dynamic cognitive model that we found that spatially distributed regions coherence connection difference, the topologic characteristics with which they can transfer information, producing temporary network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time after variation audio stimulation, dynamic programing model gives the dynamic evolution processing at different time and frequency. In this paper, by applying a new construct approach to understand whole brain network dynamics, firstly, brain network is constructed by wavelet coherence, secondly, different time active brain regions are selected by network topological characteristics and minimum spanning tree. Finally, dynamic evolution model is constructed to understand cognitive process by dynamic programing algorithm, this model is applied to the auditory experiment, results showed that, quantitatively, more correlation was observed after variation audio stimulation, the EEG function connection dynamic evolution model on cognitive processing is feasible with wavelet coherence EEG recording.     

A structural and a functional aspect of stable information processing by the brain

Brain is an expert in producing the same output from a particular set of inputs, even from a very noisy environment. In this article a model of neural circuit in the brain has been proposed which is composed of cyclic sub-circuits. A big loop has been defined to be consisting of a feed forward path from the sensory neurons to the highest processing area of the brain and feed back paths from that region back up to close to the same sensory neurons. It has been mathematically shown how some smaller cycles can amplify signal. A big loop processes information by contrast and amplify principle. How a pair of presynaptic and postsynaptic neurons can be identified by an exact synchronization detection method has also been mentioned. It has been assumed that the spike train coming out of a firing neuron encodes all the information produced by it as output. It is possible to extract this information over a period of time by Fourier transforms. The Fourier coefficients arranged in a vector form will uniquely represent the neural spike train over a period of time. The information emanating out of all the neurons in a given neural circuit over a period of time can be represented by a collection of points in a multidimensional vector space. This cluster of points represents the functional or behavioral form of the neural circuit. It has been proposed that a particular cluster of vectors as the representation of a new behavior is chosen by the brain interactively with respect to the memory stored in that circuit and the amount of emotion involved. It has been proposed that in this situation a Coulomb force like expression governs the dynamics of functioning of the circuit and stability of the system is reached at the minimum of all the minima of a potential function derived from the force like expression. The calculations have been done with respect to a pseudometric defined in a multidimensional vector space.     

History-dependent excitability as a single-cell substrate of transient memory for information discrimination

Neurons react differently to incoming stimuli depending upon their previous history of stimulation. This property can be considered as a single-cell substrate for transient memory, or context-dependent information processing: depending upon the current context that the neuron "sees" through the subset of the network impinging on it in the immediate past, the same synaptic event can evoke a postsynaptic spike or just a subthreshold depolarization. We propose a formal definition of History-Dependent Excitability (HDE) as a measure of the propensity to firing in any moment in time, linking the subthreshold history-dependent dynamics with spike generation. This definition allows the quantitative assessment of the intrinsic memory for different single-neuron dynamics and input statistics. We illustrate the concept of HDE by considering two general dynamical mechanisms: the passive behavior of an Integrate and Fire (IF) neuron, and the inductive behavior of a Generalized Integrate and Fire (GIF) neuron with subthreshold damped oscillations. This framework allows us to characterize the sensitivity of different model neurons to the detailed temporal structure of incoming stimuli. While a neuron with intrinsic oscillations discriminates equally well between input trains with the same or different frequency, a passive neuron discriminates better between inputs with different frequencies. This suggests that passive neurons are better suited to rate-based computation, while neurons with subthreshold oscillations are advantageous in a temporal coding scheme. We also address the influence of intrinsic properties in single-cell processing as a function of input statistics, and show that intrinsic oscillations enhance discrimination sensitivity at high input rates. Finally, we discuss how the recognition of these cell-specific discrimination properties might further our understanding of neuronal network computations and their relationships to the distribution and functional connectivity of different neuronal types.     

A novel key–lock mechanism for inactivating amino acid neurotransmitters during transit across extracellular space

There are two kinds of neurotransmissions that occur in brain. One is neuron to neuron at synapses, and the other is neuron to glia via extracellular fluid (ECF), both of which are important for maintenance of proper neuronal functioning. For neuron to neuron communications, several potent amino acid neurotransmitters are used within the confines of synaptic space. However, their presence at elevated concentrations in extra-synaptic space could be detrimental to well organized neuronal functioning. The significance of the synthesis and release of N-acetylaspartylglutamate (NAAG) by neurons has long been a puzzle since glutamate (Glu) itself is the “key” that can interact with all Glu receptors on membranes of all cells. Nonetheless, neurons synthesize this acetylated dipeptide, which cannot be catabolized by neurons, and release it to ECF where its specific physiological target is the Glu metabotropic receptor 3 on the surface of astrocytes. Since Glu is excitotoxic at elevated concentrations, it is proposed that formation and release of NAAG by neurons allows large quantities of Glu to be transported in ECF without the risk of injurious excitotoxic effects. The metabolic mechanism used by neurons is a key–lock system to detoxify Glu during its intercellular transit. This is accomplished by first synthesizing N-acetylaspartate (NAA), and then joining this molecule via a peptide bond to Glu. In this paper, a hypothesis is presented that neurons synthesize a variety of relatively nontoxic peptides and peptide derivatives, including NAA, NAAG, homocarnosine (γ-aminobutyrylhistidine) and carnosine (β-alanylhistidine) from potent excitatory and inhibitory amino acids for the purpose of releasing them to ECF to function as cell-specific neuron-to-glia neurotransmitters.     

Neurons in the inferior colliculus of the big brown bat show maximal amplitude sensitivity at the best duration

The big brown bats, Eptesicus fuscus, emit ultrasonic signals and analyze the returning echoes in multi-parametric domains to extract target features. The variation of different pulse parameters during hunting predicts that analysis of an echo parameter by bats is inevitably affected by other co-varying echo parameters. In this study, we presented data to show that the bat inferior collicular (IC) neurons have maximal amplitude sensitivity at the best duration (BD). A family of rate-amplitude function (RAF) of each IC neuron is plotted with the BD and non-BD sound pulses. The RAF plotted with BD pulses has sharper slope (SL) and smaller dynamic range (DR) than the RAF plotted with non-BD pulses has. All RAFs can be described as monotonic, saturated or non-monotonic. IC neurons with monotonic RAF are mostly recorded at deeper IC and they have the largest average BD, best amplitude (BA) and DR. Conversely, IC neurons with non-monotonic RAF are mostly recorded at upper IC and they have the smallest average BD, BA and DR. Low best frequency (BF) neurons at upper IC have shorter BD, smaller BA and DR than high BF neurons at deeper IC have. These data suggest that IC neurons that tune to an echo duration also have the greatest sensitivity to echo amplitude. These data also suggest that sensitivity in frequency, duration and amplitude appears to be orderly represented along the dorso-ventral axis of the IC.     

The Green’s function formalism as a bridge between single- and multi-compartmental modeling

Neurons are spatially extended structures that receive and process inputs on their dendrites. It is generally accepted that neuronal computations arise from the active integration of synaptic inputs along a dendrite between the input location and the location of spike generation in the axon initial segment. However, many application such as simulations of brain networks use point-neurons—neurons without a morphological component—as computational units to keep the conceptual complexity and computational costs low. Inevitably, these applications thus omit a fundamental property of neuronal computation. In this work, we present an approach to model an artificial synapse that mimics dendritic processing without the need to explicitly simulate dendritic dynamics. The model synapse employs an analytic solution for the cable equation to compute the neuron’s membrane potential following dendritic inputs. Green’s function formalism is used to derive the closed version of the cable equation. We show that by using this synapse model, point-neurons can achieve results that were previously limited to the realms of multi-compartmental models. Moreover, a computational advantage is achieved when only a small number of simulated synapses impinge on a morphologically elaborate neuron. Opportunities and limitations are discussed.