Tuned solutions in dynamic neural fields as building blocks for extended EEG models

The most prominent functional property of cortical neurons in sensory areas are their tuned receptive fields which provide specific responses of the neurons to external stimuli. Tuned neural firing indeed reflects the most basic and best worked out level of cognitive representations. Tuning properties can be dynamic on a short time-scale of fractions of a second. Such dynamic effects have been modeled by localised solutions (also called “bumps” or “peaks”) in dynamic neural fields. In the present work we develop an approximation method to reduce the dynamics of localised activation peaks in systems of n coupled nonlinear d-dimensional neural fields with transmission delays to a small set of delay differential equations for the peak amplitudes and widths only. The method considerably simplifies the analysis of peaked solutions as demonstrated for a two-dimensional example model of neural feature selectivity in the brain. The reduced equations describe the effective interaction between pools of local neurons of several (n) classes that participate in shaping the dynamic receptive field responses. To lowest order they resemble neural mass models as they often form the base of EEG-models. Thereby they provide a link between functional small-scale receptive field models and more coarse-grained EEG-models. More specifically, they connect the dynamics in feature-selective cortical microcircuits to the more abstract local elements used in coarse-grained models. However, beside amplitudes the reduced equations also reflect the sharpness of tuning of the activity in a d-dimensional feature space in response to localised stimuli.     

Artificial neural networks modeling gene-environment interaction

ABSTRACT: BACKGROUND: Gene-environment interactions play an important role in the etiological pathway of complex diseases. An appropriate statistical method for handling a wide variety of complex situations involving interactions between variables is still lacking, especially when continuous variables are involved. The aim of this paper is to explore the ability of neural networks to model different structures of gene-environment interactions. A simulation study is set up to compare neural networks with standard logistic regression models. Eight different structures of gene-environment interactions are investigated. These structures are characterized by penetrance functions that are based on sigmoid functions or on combinations of linear and non-linear effects of a continuous environmental factor and a genetic factor with main effect or with a masking effect only. RESULTS: In our simulation study, neural networks are more successful in modeling gene-environment interactions than logistic regression models. This outperfomance is especially pronounced when modeling sigmoid penetrance functions, when distinguishing between linear and nonlinear components, and when modeling masking effects of the genetic factor. CONCLUSION: Our study shows that neural networks are a promising approach for analyzing gene-environment interactions. Especially, if no prior knowledge of the correct nature of the relationship between co-variables and response variable is present, neural networks provide a valuable alternative to regression methods that are limited to the analysis of linearly separable data.     

Nonlinear modeling of dynamic interactions within neuronal ensembles using Principal Dynamic Modes

A methodology for nonlinear modeling of multi-input multi-output (MIMO) neuronal systems is presented that utilizes the concept of Principal Dynamic Modes (PDM). The efficacy of this new methodology is demonstrated in the study of the dynamic interactions between neuronal ensembles in the Pre-Frontal Cortex (PFC) of a behaving non-human primate (NHP) performing a Delayed Match-to-Sample task. Recorded spike trains from Layer-2 and Layer-5 neurons were viewed as the “inputs” and “outputs”, respectively, of a putative MIMO system/model that quantifies the dynamic transformation of multi-unit neuronal activity between Layer-2 and Layer-5 of the PFC. Model prediction performance was evaluated by means of computed Receiver Operating Characteristic (ROC) curves. The PDM-based approach seeks to reduce the complexity of MIMO models of neuronal ensembles in order to enable the practicable modeling of large-scale neural systems incorporating hundreds or thousands of neurons, which is emerging as a preeminent issue in the study of neural function. The “scaling-up” issue has attained critical importance as multi-electrode recordings are increasingly used to probe neural systems and advance our understanding of integrated neural function. The initial results indicate that the PDM-based modeling methodology may greatly reduce the complexity of the MIMO model without significant degradation of performance. Furthermore, the PDM-based approach offers the prospect of improved biological/physiological interpretation of the obtained MIMO models.     

Robust spatial working memory through homeostatic synaptic scaling in heterogeneous cortical networks

The concept of bell-shaped persistent neural activity represents a cornerstone of the theory for the internal representation of analog quantities, such as spatial location or head direction. Previous models, however, relied on the unrealistic assumption of network homogeneity. We investigate this issue in a network model where fine tuning of parameters is destroyed by heterogeneities in cellular and synaptic properties. Heterogeneities result in the loss of stored spatial information in a few seconds. Accurate encoding is recovered when a homeostatic mechanism scales the excitatory synapses to each cell to compensate for the heterogeneity in cellular excitability and synaptic inputs. Moreover, the more realistic model produces a wide diversity of tuning curves, as commonly observed in recordings from prefrontal neurons. We conclude that recurrent attractor networks in conjunction with appropriate homeostatic mechanisms provide a robust, biologically plausible theoretical framework for understanding the neural circuit basis of spatial working memory.     

Duration tuning in the auditory midbrain of echolocating and non-echolocating vertebrates

Neurons tuned for stimulus duration were first discovered in the auditory midbrain of frogs. Duration-tuned neurons (DTNs) have since been reported from the central auditory system of both echolocating and non-echolocating mammals, and from the central visual system of cats. We hypothesize that the functional significance of auditory duration tuning likely varies between species with different evolutionary histories, sensory ecologies, and bioacoustic constraints. For example, in non-echolocating animals such as frogs and mice the temporal filtering properties of auditory DTNs may function to discriminate species-specific communication sounds. In echolocating bats duration tuning may also be used to create cells with highly selective responses for specific rates of frequency modulation and/or pulse-echo delays. The ability to echolocate appears to have selected for high temporal acuity in the duration tuning curves of inferior colliculus neurons in bats. Our understanding of the neural mechanisms underlying sound duration selectivity has improved substantially since DTNs were first discovered almost 50 years ago, but additional research is required for a comprehensive understanding of the functional role and the behavioral significance that duration tuning plays in sensory systems.     

Astrocyte- neuron interaction as a mechanism responsible for generation of neural synchrony: a study based on modeling and experiments

Neural synchronization is considered as an important mechanism for information processing. In addition, based on recent neurophysiologic findings, it is believed that astrocytes regulate the synaptic transmission of neuronal networks. Therefore, the present study focused on determining the functional contribution of astrocytes in neuronal synchrony using both computer simulations and extracellular field potential recordings. For computer simulations, as a first step, a minimal network model is constructed by connecting two Morris-Lecar neuronal models. In this minimal model, astrocyte-neuron interactions are considered in a functional-based procedure. Next, the minimal network is extended and a biologically plausible neuronal population model is developed which considers functional outcome of astrocyte-neuron interactions too. The employed structure is based on the physiological and anatomical network properties of the hippocampal CA1 area. Utilizing these two different levels of modeling, it is demonstrated that astrocytes are able to change the threshold value of transition from synchronous to asynchronous behavior among neurons. In this way, variations in the interaction between astrocytes and neurons lead to the emergence of synchronous/asynchronous patterns in neural responses. Furthermore, population spikes are recorded from CA1 pyramidal neurons in rat hippocampal slices to validate the modeling results. It demonstrates that astrocytes play a primary role in neuronal firing synchronicity and synaptic coordination. These results may offer a new insight into understanding the mechanism by which astrocytes contribute to stabilizing neural activities.     

锐化蝙蝠听皮层神经元频率调谐的柱特征

用双声刺激和多管电极方法在 6只大棕蝠 (bigbrownbat,Eptesicusfuscus)的 98个神经元上研究了锐化 (sharpening)蝙蝠听皮层 (primaryauditorycortex ,AC)神经元频率调谐的柱特征。结果发现 ,电极直插在 1个电极通道内连续记录到多个神经元时 ,它们锐化频率调谐的抑制性调谐曲线或抑制区基本相似。电极与AC表面呈 45°斜向推入使其跨越多个功能柱时 ,可观察到锐化频率调谐的抑制区构成也随电极进入不同的功能柱而发生相应的改变。两种不同的电极插入方式均证明锐化AC神经元频率调谐的神经抑制呈柱状组构。这些神经元组合起来排列在同一听觉功能柱内 ,构成AC频率分析的基本功能组构单位“微频率处理器”。实验中还观察到多峰频率调谐曲线神经元 ,它们在声通讯和声定位中不同波谱区域的时间匹配中起作用。此外 ,也有理由认为多峰调谐神经元亦被用于作为复杂波谱信息的“高级调谐预处理器” ,从而极大地提高了神经元对频率分析的能力。为研究锐化频率调谐的神经抑制机制 ,用多管电极电泳γ -氨基丁酸 (γ aminobutyricacid ,GA BA)能a受体拮抗剂荷包牡丹碱 (bicuculline ,Bic)至所记录的神经元 ,发现能大部分或几乎全部取消抑制区 ,从而表明在正常情况下GABA能抑制参与构成锐化AC神经元频率调谐的抑制区 ,     

Unscented Kalman Filter for Brain-Machine Interfaces

Brain machine interfaces (BMIs) are devices that convert neural signals into commands to directly control artificial actuators, such as limb prostheses. Previous real-time methods applied to decoding behavioral commands from the activity of populations of neurons have generally relied upon linear models of neural tuning and were limited in the way they used the abundant statistical information contained in the movement profiles of motor tasks. Here, we propose an n-th order unscented Kalman filter which implements two key features: (1) use of a non-linear (quadratic) model of neural tuning which describes neural activity significantly better than commonly-used linear tuning models, and (2) augmentation of the movement state variables with a history of n-1 recent states, which improves prediction of the desired command even before incorporating neural activity information and allows the tuning model to capture relationships between neural activity and movement at multiple time offsets simultaneously. This new filter was tested in BMI experiments in which rhesus monkeys used their cortical activity, recorded through chronically implanted multielectrode arrays, to directly control computer cursors. The 10th order unscented Kalman filter outperformed the standard Kalman filter and the Wiener filter in both off-line reconstruction of movement trajectories and real-time, closed-loop BMI operation.     

Dynamic updating of distributed neural representations using forward models

In this paper, we present a continuous attractor network model that we hypothesize will give some suggestion of the mechanisms underlying several neural processes such as velocity tuning to visual stimulus, sensory discrimination, sensorimotor transformations, motor control, motor imagery, and imitation. All of these processes share the fundamental characteristic of having to deal with the dynamic integration of motor and sensory variables in order to achieve accurate sensory prediction and/or discrimination. Such principles have already been described in the literature by other high-level modeling studies (Decety and Sommerville in Trends Cogn Sci 7:527–533, 2003; Oztop et al. in Neural Netw 19(3):254–271, 2006; Wolpert et al. in Philos Trans R Soc 358:593–602, 2003). With respect to these studies, our work is more concerned with biologically plausible neural dynamics at a population level. Indeed, we show that a relatively simple extension of the classical neural field models can endow these networks with additional dynamic properties for updating their internal representation using external commands. Moreover, an analysis of the interactions between our model and external inputs also shows interesting properties, which we argue are relevant for a better understanding of the neural processes of the brain.     

Effect of steady-state response versus excitatory/inhibitory balance on spiking synchronization in neural networks with log-normal synaptic weight distribution

Synchronization of neural activity, especially at the gamma band, contributes to perceptual functions. In several psychiatric disorders, deficits of perceptual functions are reflected in synchronization abnormalities. Plausible cause of this impairment is an alteration in the balance between excitation and inhibition (E/I balance); a disruption in the E/I balance leads to abnormal neural interactions reminiscent of pathological states. Moreover, the local lateral excitatory-excitatory synaptic connections in the cortex exhibit excitatory postsynaptic potentials (EPSPs) that follow a log-normal amplitude distribution. This long-tailed distribution is considered an important factor for the emergence of spatiotemporal neural activity. In this context, we hypothesized that manipulating the EPSP distribution under abnormal E/I balance conditions would provide insights into psychiatric disorders characterized by deficits in perceptual functions, potentially revealing the mechanisms underlying pathological neural behaviors. In this study, we evaluated the synchronization of neural activity with external periodic stimuli in spiking neural networks in cases of both E/I balance and imbalance with or without a long-tailed EPSP amplitude distribution. The results showed that external stimuli of a high frequency lead to a decrease in the degree of synchronization with an increasing ratio of excitatory to inhibitory neurons in the presence, but not in the absence, of high-amplitude EPSPs. This monotonic reduction can be interpreted as an autonomous, strong-EPSP-dependent spiking activity selectively interfering with the responses to external stimuli. This observation is consistent with pathological findings. Thus, our modeling approach has potential to improve the understanding of the steady-state response in both healthy and pathological states.     

Chemical conversion of human and mouse fibroblasts into motor neurons

Transplantation of motor neurons can provide long-term functional benefits in animal models of neurodegenerative motor neuron diseases such as amyotrophic lateral sclerosis and traumatic spinal cord injury. Although embryonic stem cells can differentiate into motor neurons, alternative sources of motor neurons may be controllable for disease modeling and transplantation. Here, we show that human and mouse fibroblasts can be efficiently and directly converted into motor neurons by a cocktail of five small molecules, without the involvement of the neural progenitor stage. The chemically-induced motor neurons display the distinct neuronal morphology and express motor neuron markers. Interestingly, when the same chemical compounds were soaked in beads and implanted in the hypodermis of the back skins of mice, surrounding cells begin to express motor neuron markers, indicating in vivo motor neuron reprogramming. Taken together, we provide an efficient approach for chemically converting human and mouse fibroblasts into motor neurons suitable for cell replacement therapy and neurodegenerative disease modeling.     

Structure of Spike Count Correlations Reveals Functional Interactions between Neurons in Dorsolateral Prefrontal Cortex Area 8a of Behaving Primates

Neurons within the primate dorsolateral prefrontal cortex (dlPFC) are clustered in microcolumns according to their visuospatial tuning. One issue that remains poorly investigated is how this anatomical arrangement influences functional interactions between neurons during behavior. To investigate this question we implanted 4 mm×4 mm multielectrode arrays in two macaques'' dlPFC area 8a and measured spike count correlations (r_sc) between responses of simultaneously recorded neurons when animals maintained stationary gaze. Positive and negative r_sc were significantly higher than predicted by chance across a wide range of inter-neuron distances (from 0.4 to 4 mm). Positive r_sc were stronger between neurons with receptive fields (RFs) separated by ≤90° of angular distance and progressively decreased as a function of inter-neuron physical distance. Negative r_sc were stronger between neurons with RFs separated by >90° and increased as a function of inter-neuron distance. Our results show that short- and long-range functional interactions between dlPFC neurons depend on the physical distance between them and the relationship between their visuospatial tuning preferences. Neurons with similar visuospatial tuning show positive r_sc that decay with inter-neuron distance, suggestive of excitatory interactions within and between adjacent microcolumns. Neurons with dissimilar tuning from spatially segregated microcolumns show negative r_sc that increase with inter-neuron distance, suggestive of inhibitory interactions. This pattern of results shows that functional interactions between prefrontal neurons closely follow the pattern of connectivity reported in anatomical studies. Such interactions may be important for the role of the prefrontal cortex in the allocation of attention to targets in the presence of competing distracters.     

How behavioral constraints may determine optimal sensory representations

The sensory-triggered activity of a neuron is typically characterized in terms of a tuning curve, which describes the neuron's average response as a function of a parameter that characterizes a physical stimulus. What determines the shapes of tuning curves in a neuronal population? Previous theoretical studies and related experiments suggest that many response characteristics of sensory neurons are optimal for encoding stimulus-related information. This notion, however, does not explain the two general types of tuning profiles that are commonly observed: unimodal and monotonic. Here I quantify the efficacy of a set of tuning curves according to the possible downstream motor responses that can be constructed from them. Curves that are optimal in this sense may have monotonic or nonmonotonic profiles, where the proportion of monotonic curves and the optimal tuning-curve width depend on the general properties of the target downstream functions. This dependence explains intriguing features of visual cells that are sensitive to binocular disparity and of neurons tuned to echo delay in bats. The numerical results suggest that optimal sensory tuning curves are shaped not only by stimulus statistics and signal-to-noise properties but also according to their impact on downstream neural circuits and, ultimately, on behavior.     

Motor learning with unstable neural representations

It is often assumed that learning takes place by changing an otherwise stable neural representation. To test this assumption, we studied changes in the directional tuning of primate motor cortical neurons during reaching movements performed in familiar and novel environments. During the familiar task, tuning curves exhibited slow random drift. During learning of the novel task, random drift was accompanied by systematic shifts of tuning curves. Our analysis suggests that motor learning is based on a surprisingly unstable neural representation. To explain these results, we propose that motor cortex is a redundant neural network, i.e., any single behavior can be realized by multiple configurations of synaptic strengths. We further hypothesize that synaptic modifications underlying learning contain a random component, which causes wandering among synaptic configurations with equivalent behaviors but different neural representations. We use a simple model to explore the implications of these assumptions.     

The Sign Rule and Beyond: Boundary Effects,Flexibility, and Noise Correlations in Neural Population Codes

Over repeat presentations of the same stimulus, sensory neurons show variable responses. This “noise” is typically correlated between pairs of cells, and a question with rich history in neuroscience is how these noise correlations impact the population''s ability to encode the stimulus. Here, we consider a very general setting for population coding, investigating how information varies as a function of noise correlations, with all other aspects of the problem – neural tuning curves, etc. – held fixed. This work yields unifying insights into the role of noise correlations. These are summarized in the form of theorems, and illustrated with numerical examples involving neurons with diverse tuning curves. Our main contributions are as follows. (1) We generalize previous results to prove a sign rule (SR) — if noise correlations between pairs of neurons have opposite signs vs. their signal correlations, then coding performance will improve compared to the independent case. This holds for three different metrics of coding performance, and for arbitrary tuning curves and levels of heterogeneity. This generality is true for our other results as well. (2) As also pointed out in the literature, the SR does not provide a necessary condition for good coding. We show that a diverse set of correlation structures can improve coding. Many of these violate the SR, as do experimentally observed correlations. There is structure to this diversity: we prove that the optimal correlation structures must lie on boundaries of the possible set of noise correlations. (3) We provide a novel set of necessary and sufficient conditions, under which the coding performance (in the presence of noise) will be as good as it would be if there were no noise present at all.     

Functional model of biological neural networks

A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.     

Glomerular interactions in olfactory processing channels of the antennal lobes

An open question in olfactory coding is the extent of interglomerular connectivity: do olfactory glomeruli and their neurons regulate the odorant responses of neurons innervating other glomeruli? In the olfactory system of the moth Manduca sexta, the response properties of different types of antennal olfactory receptor cells are known. Likewise, a subset of antennal lobe glomeruli has been functionally characterized and the olfactory tuning of their innervating neurons identified. This provides a unique opportunity to determine functional interactions between glomeruli of known input, specifically, (1) glomeruli processing plant odors and (2) glomeruli activated by antennal stimulation with pheromone components of conspecific females. Several studies describe reciprocal inhibitory effects between different types of pheromone-responsive projection neurons suggesting lateral inhibitory interactions between pheromone component-selective glomerular neural circuits. Furthermore, antennal lobe projection neurons that respond to host plant volatiles and innervate single, ordinary glomeruli are inhibited during antennal stimulation with the female’s sex pheromone. The studies demonstrate the existence of lateral inhibitory effects in response to behaviorally significant odorant stimuli and irrespective of glomerular location in the antennal lobe. Inhibitory interactions are present within and between olfactory subsystems (pheromonal and non-pheromonal subsystems), potentially to enhance contrast and strengthen odorant discrimination.     

Cognition and the single neuron: How cell types construct the dynamic computations of frontal cortex

Frontal cortex is thought to underlie many advanced cognitive capacities, from self-control to long term planning. Reflecting these diverse demands, frontal neural activity is notoriously idiosyncratic, with tuning properties that are correlated with endless numbers of behavioral and task features. This menagerie of tuning has made it difficult to extract organizing principles that govern frontal neural activity. Here, we contrast two successful yet seemingly incompatible approaches that have begun to address this challenge. Inspired by the indecipherability of single-neuron tuning, the first approach casts frontal computations as dynamical trajectories traversed by arbitrary mixtures of neurons. The second approach, by contrast, attempts to explain the functional diversity of frontal activity with the biological diversity of cortical cell-types. Motivated by the recent discovery of functional clusters in frontal neurons, we propose a consilience between these population and cell-type-specific approaches to neural computations, advancing the conjecture that evolutionarily inherited cell-type constraints create the scaffold within which frontal population dynamics must operate.