A persistent cellular change in a single modulatory neuron contributes to associative long-term memory

Most neuronal models of learning assume that changes in synaptic strength are the main mechanism underlying long-term memory (LTM) formation. However, we show here that a persistent depolarization of membrane potential, a type of cellular change that increases neuronal responsiveness, contributes significantly to a long-lasting associative memory trace. The use of a model invertebrate network with identified neurons and known synaptic connectivity had the advantage that the contribution of this cellular change to memory could be evaluated in a neuron with a known function in the learning circuit. Specifically, we used the well-understood motor circuit underlying molluscan feeding and showed that a key modulatory neuron involved in the initiation of feeding ingestive movements underwent a long-term depolarization following behavioral associative conditioning. This depolarization led to an enhanced single cell and network responsiveness to a previously neutral tactile conditioned stimulus, and the persistence of both matched the time course of behavioral associative memory. The change in the membrane potential of a key modulatory neuron is both sufficient and necessary to initiate a conditioned response in a reduced preparation and underscores its importance for associative LTM.     

Sensory processing and the network mechanisms for reading neuronal population codes

In many neuronal systems, information appears to be represented in the activity of populations of neurons. Such neuronal population codes must also be read out, or interpreted, by downstream networks. Recent studies in both vertebrate and invertebrate systems have begun to elucidate some of the general mechanisms underlying these processes. Directed behaviors, that involve a directional response to a directional sensory input, have been a particularly useful context for these studies because, among other things, their input-output relationship is easily defined and experimentally controlled. We have recently shown that the neuronal network underlying a directed behavior in the medicinal leech utilizes a specific population coding scheme based on a neuronal population vector. A population vector of mechanosensory neuron activity correlates well with behavioral output and the connectivity of the downstream network is well suited for accurately reading out this population code. Accepted: 17 April 1999     

Cellular mechanisms underlying stimulus-dependent gain modulation in primary visual cortex neurons in vivo

Gain modulation is a widespread neuronal phenomenon that modifies response amplitude without changing selectivity. Computational and in vitro studies have proposed cellular mechanisms of gain modulation based on the postsynaptic effects of background synaptic activation, but these mechanisms have not been studied in vivo. Here, we used intracellular recordings from cat primary visual cortex to measure neuronal gain while changing background synaptic activity with visual stimulation. We found that increases in the membrane fluctuations associated with increases in synaptic input do not obligatorily result in gain modulation in vivo. However, visual stimuli that evoked sustained changes in resting membrane potential, input resistance, and membrane fluctuations robustly modulated neuronal gain. The magnitude of gain modulation depended critically on the spatiotemporal properties of the visual stimulus. Gain modulation in vivo may thus be determined on a moment-to-moment basis by sensory context and the consequent dynamics of synaptic activation.     

Diversity of Intrinsic Frequency Encoding Patterns in Rat Cortical Neurons—Mechanisms and Possible Functions

Extracellular recordings of single neurons in primary and secondary somatosensory cortices of monkeys in vivo have shown that their firing rate can increase, decrease, or remain constant in different cells, as the external stimulus frequency increases. We observed similar intrinsic firing patterns (increasing, decreasing or constant) in rat somatosensory cortex in vitro, when stimulated with oscillatory input using conductance injection (dynamic clamp). The underlying mechanism of this observation is not obvious, and presents a challenge for mathematical modelling. We propose a simple principle for describing this phenomenon using a leaky integrate-and-fire model with sinusoidal input, an intrinsic oscillation and Poisson noise. Additional enhancement of the gain of encoding could be achieved by local network connections amongst diverse intrinsic response patterns. Our work sheds light on the possible cellular and network mechanisms underlying these opposing neuronal responses, which serve to enhance signal detection.     

Genetic Control of Startle Behavior in Medaka Fish

Genetic polymorphisms are thought to generate intraspecific behavioral diversities, both within and among populations. The mechanisms underlying genetic control of behavioral properties, however, remain unclear in wild-type vertebrates, including humans. To explore this issue, we used diverse inbred strains of medaka fish (Oryzias latipes) established from the same and different local populations. Medaka exhibit a startle response to a visual stimulus (extinction of illumination) by rapidly bending their bodies (C-start) 20-ms after the stimulus presentation. We measured the rates of the response to repeated stimuli (1-s interval, 40 times) among four inbred strains, HNI-I, HNI-II, HO5, and Hd-rR-II1, and quantified two properties of the startle response: sensitivity (response rate to the first stimulus) and attenuation of the response probability with repeated stimulus presentation. Among the four strains, the greatest differences in these properties were detected between HNI-II and Hd-rR-II1. HNI-II exhibited high sensitivity (approximately 80%) and no attenuation, while Hd-rR-II1 exhibited low sensitivity (approximately 50%) and almost complete attenuation after only five stimulus presentations. Our findings suggested behavioral diversity of the startle response within a local population as well as among different populations. Linkage analysis with F2 progeny between HNI-II and Hd-rR-II1 detected quantitative trait loci (QTL) highly related to attenuation, but not to sensitivity, with a maximum logarithm of odds score of 11.82 on linkage group 16. The three genotypes (homozygous for HNI-II and Hd-rR-II1 alleles, and heterozygous) at the marker nearest the QTL correlated with attenuation. Our findings are the first to suggest that a single genomic region might be sufficient to generate individual differences in startle behavior between wild-type strains. Further identification of genetic polymorphisms that define the behavioral trait will contribute to our understanding of the neural mechanisms underlying behavioral diversity, allowing us to investigate the adaptive significance of intraspecific behavioral polymorphisms of the startle response.     

What we have learned from the study of learning in the leech

The use of invertebrate preparations has contributed greatly to our understanding of the neural basis of learning. The leech is especially useful for studying behavioral changes and their underlying neuronal mechanisms. Learning in the leech is essentially identical to that found in other animals, both vertebrate and invertebrate. Using anatomical and physiological techniques on leeches as they learn, we have begun to characterize the properties of individual neurons and neuronal networks that play a role in learning. We have been able to show two neuronal mechanisms that have not been previously associated with associative conditioning. The first has to do with the importance of contingency: one stimulus [the conditional stimulus (CS)] becomes associated with a second stimulus [the unconditional stimulus, (US)] in proportion to the ability of the CS to predict the US. We have found that important properties for encoding predictability, such as circuit reconfiguration, may lie in the US pathway. The firing of the serotonergic Retzius cells is taken as the US; consistent CS prediction of a US prevents “dropout” of a critical component of one US pathway. Throughout training, predicted USs continue to elicit a barrage of action potentials in these cells. Recurring unpredicted USs degrade both the learning and the response of the Retzius cell to the US. A second insight is that at least two US pathways contribute to learning, the Retzius cell pathway and the nociceptive (N) cell pathway. This second pathway persists after the elimination of the Retzius cell pathway. The observation of multiple US pathways raises a host of issues concerning CS–US convergence and the functional significance of distinct US pathways, and our results are discussed in terms of implications to current models of learning. © 1995 John Wiley & Sons, Inc.     

Criticality enhances the multilevel reliability of stimulus responses in cortical neural networks

Cortical neural networks exhibit high internal variability in spontaneous dynamic activities and they can robustly and reliably respond to external stimuli with multilevel features–from microscopic irregular spiking of neurons to macroscopic oscillatory local field potential. A comprehensive study integrating these multilevel features in spontaneous and stimulus–evoked dynamics with seemingly distinct mechanisms is still lacking. Here, we study the stimulus–response dynamics of biologically plausible excitation–inhibition (E–I) balanced networks. We confirm that networks around critical synchronous transition states can maintain strong internal variability but are sensitive to external stimuli. In this dynamical region, applying a stimulus to the network can reduce the trial-to-trial variability and shift the network oscillatory frequency while preserving the dynamical criticality. These multilevel features widely observed in different experiments cannot simultaneously occur in non-critical dynamical states. Furthermore, the dynamical mechanisms underlying these multilevel features are revealed using a semi-analytical mean-field theory that derives the macroscopic network field equations from the microscopic neuronal networks, enabling the analysis by nonlinear dynamics theory and linear noise approximation. The generic dynamical principle revealed here contributes to a more integrative understanding of neural systems and brain functions and incorporates multimodal and multilevel experimental observations. The E–I balanced neural network in combination with the effective mean-field theory can serve as a mechanistic modeling framework to study the multilevel neural dynamics underlying neural information and cognitive processes.     

Single Medial Prefrontal Neurons Cope with Error

Learning from mistakes is a key feature of human behavior. However, the mechanisms underlying short-term adaptation to erroneous action are still poorly understood. One possibility relies on the modulation of attentional systems after an error. To explore this possibility, we have designed a Stroop-like visuo-motor task in monkeys that favors incorrect action. Using this task, we previously found that single neurons recorded from the anterior cingulate cortex (ACC) were closely tuned to behavioral performance and, more particularly, that the activity of most neurons was biased towards the evaluation of erroneous action. Here we describe single neurons engaged in both error detection and response alertness processing, whose activation is closely associated with the improvement of subsequent behavioral performance. Specifically, we show that the effect of a warning stimulus on neuronal firing is enhanced after an erroneous response rather than a successful one and that this outcome is correlated with an error rate decrease. Our results suggest that the anterior cingulate cortex, which exhibits this activity, serves as a powerful computational locus for rapid behavioral adaptation.     

Deconstructing memory in Drosophila

Unlike most organ systems, which have evolved to maintain homeostasis, the brain has been selected to sense and adapt to environmental stimuli by constantly altering interactions in a gene network that functions within a larger neural network. This unique feature of the central nervous system provides a remarkable plasticity of behavior, but also makes experimental investigations challenging. Each experimental intervention ramifies through both gene and neural networks, resulting in unpredicted and sometimes confusing phenotypic adaptations. Experimental dissection of mechanisms underlying behavioral plasticity ultimately must accomplish an integration across many levels of biological organization, including genetic pathways acting within individual neurons, neural network interactions which feed back to gene function, and phenotypic observations at the behavioral level. This dissection will be more easily accomplished for model systems such as Drosophila, which, compared with mammals, have relatively simple and manipulable nervous systems and genomes. The evolutionary conservation of behavioral phenotype and the underlying gene function ensures that much of what we learn in such model systems will be relevant to human cognition. In this essay, we have not attempted to review the entire Drosophila memory field. Instead, we have tried to discuss particular findings that provide some level of intellectual synthesis across three levels of biological organization: behavior, neural circuitry and biochemical pathways. We have attempted to use this integrative approach to evaluate distinct mechanistic hypotheses, and to propose critical experiments that will advance this field.     

Comparative study of visuo-vestibular conditioning in Lymnaea stagnalis

In this review, we compare the current understanding of visuo-vestibular conditioning in Hermissenda crassicornis and Lymnaea stagnalis on the basis of behavioral, electrophysiologic, and morphologic studies. Paired presentation of a photic conditioned stimulus (CS) and an orbital rotation unconditioned stimulus (US) results in conditioned escape behavior in both species. In Hermissenda, changes in excitability of type B photoreceptors and morphologic modifications at the axon terminals follow conditioning. Caudal hair cells, which detect mechanical turbulence, have reciprocal inhibition with type B photoreceptors. In Lymnaea, the interaction between photoreceptors and hair cells is dependent on statocyst location. Furthermore, the organization of the Lymnaea eye is complex, with more than 100 photoreceptors distributed in a uniquely folded retina. Although the optimal conditions to produce long-term memory (memory persistent for >1 week) are almost identical in Hermissenda and Lymnaea, physiologic and morphologic differences suggest that the neuronal mechanisms underlying learning and memory are distinct.     

Real time computation: zooming in on population codes

Information processing in nervous systems intricately combines computation at the neuronal and network levels. Many computations may be envisioned as sequences of signal processing steps along some pathway. How can information encoded by single cells be mapped onto network population codes, and how do different modules or layers in the computation synchronize their communication and computation? These fundamental questions are particularly severe when dealing with real time streams of inputs. Here we study this problem within the context of a minimal signal perception task. In particular, we encode neuronal information by externally applying a space- and time-localized stimulus to individual neurons within a network. We show that a pulse-coupled recurrent neural network can successfully handle this task in real time, and obeys three key requirements: (i) stimulus dependence, (ii) initial-conditions independence, and (iii) accessibility by a readout mechanism. In particular, we suggest that the network's overall level of activity can be used as a temporal cue for a robust readout mechanism. Within this framework, the network can rapidly map a local stimulus onto a population code that can then be reliably read out during some narrow but well defined window of time.     

Optimality in mono- and multisensory map formation

In the struggle for survival in a complex and dynamic environment, nature has developed a multitude of sophisticated sensory systems. In order to exploit the information provided by these sensory systems, higher vertebrates reconstruct the spatio-temporal environment from each of the sensory systems they have at their disposal. That is, for each modality the animal computes a neuronal representation of the outside world, a monosensory neuronal map. Here we present a universal framework that allows to calculate the specific layout of the involved neuronal network by means of a general mathematical principle, viz., stochastic optimality. In order to illustrate the use of this theoretical framework, we provide a step-by-step tutorial of how to apply our model. In so doing, we present a spatial and a temporal example of optimal stimulus reconstruction which underline the advantages of our approach. That is, given a known physical signal transmission and rudimental knowledge of the detection process, our approach allows to estimate the possible performance and to predict neuronal properties of biological sensory systems. Finally, information from different sensory modalities has to be integrated so as to gain a unified perception of reality for further processing, e.g., for distinct motor commands. We briefly discuss concepts of multimodal interaction and how a multimodal space can evolve by alignment of monosensory maps.     

Transformation of Adaptation and Gain Rescaling along the Whisker Sensory Pathway

Neurons in all sensory systems have a remarkable ability to adapt their sensitivity to the statistical structure of the sensory signals to which they are tuned. In the barrel cortex, firing rate adapts to the variance of a whisker stimulus and neuronal sensitivity (gain) adjusts in inverse proportion to the stimulus standard deviation. To determine how adaptation might be transformed across the ascending lemniscal pathway, we measured the responses of single units in the first and last subcortical stages, the trigeminal ganglion (TRG) and ventral posterior medial thalamic nucleus (VPM), to controlled whisker stimulation in urethane-anesthetized rats. We probed adaptation using a filtered white noise stimulus that switched between low- and high-variance epochs. We found that the firing rate of both TRG and VPM neurons adapted to stimulus variance. By fitting the responses of each unit to a Linear-Nonlinear-Poisson model, we tested whether adaptation changed feature selectivity and/or sensitivity. We found that, whereas feature selectivity was unaffected by stimulus variance, units often exhibited a marked change in sensitivity. The extent of these sensitivity changes increased systematically along the pathway from TRG to barrel cortex. However, there was marked variability across units, especially in VPM. In sum, in the whisker system, the adaptation properties of subcortical neurons are surprisingly diverse. The significance of this diversity may be that it contributes to a rich population representation of whisker dynamics.     

Stochastic Dynamics Underlying Cognitive Stability and Flexibility

Cognitive stability and flexibility are core functions in the successful pursuit of behavioral goals. While there is evidence for a common frontoparietal network underlying both functions and for a key role of dopamine in the modulation of flexible versus stable behavior, the exact neurocomputational mechanisms underlying those executive functions and their adaptation to environmental demands are still unclear. In this work we study the neurocomputational mechanisms underlying cue based task switching (flexibility) and distractor inhibition (stability) in a paradigm specifically designed to probe both functions. We develop a physiologically plausible, explicit model of neural networks that maintain the currently active task rule in working memory and implement the decision process. We simplify the four-choice decision network to a nonlinear drift-diffusion process that we canonically derive from a generic winner-take-all network model. By fitting our model to the behavioral data of individual subjects, we can reproduce their full behavior in terms of decisions and reaction time distributions in baseline as well as distractor inhibition and switch conditions. Furthermore, we predict the individual hemodynamic response timecourse of the rule-representing network and localize it to a frontoparietal network including the inferior frontal junction area and the intraparietal sulcus, using functional magnetic resonance imaging. This refines the understanding of task-switch-related frontoparietal brain activity as reflecting attractor-like working memory representations of task rules. Finally, we estimate the subject-specific stability of the rule-representing attractor states in terms of the minimal action associated with a transition between different rule states in the phase-space of the fitted models. This stability measure correlates with switching-specific thalamocorticostriatal activation, i.e., with a system associated with flexible working memory updating and dopaminergic modulation of cognitive flexibility. These results show that stochastic dynamical systems can implement the basic computations underlying cognitive stability and flexibility and explain neurobiological bases of individual differences.     

How to Find Decision Makers in Neural Networks

Nervous systems often face the problem of classifying stimuli and making decisions based on these classifications. The neurons involved in these tasks can be characterized as sensory or motor, according to their correlation with sensory stimulus or motor response. In this study we define a third class of neurons responsible for making perceptual decisions. Our mathematical formalism enables the weighting of neuronal units according to their contribution to decision making, thus narrowing the field for more detailed studies of underlying mechanisms. We develop two definitions of a contribution to decision making. The first definition states that decision making activity can be found at the points of emergence for behavioral correlations in the system. The second definition involves the study of propagation of noise in the network. The latter definition is shown to be equivalent to the first one in the cases when they can be compared. Our results suggest a new approach to analyzing decision making networks An erratum to this article can be found at     

Toll-like signaling and the cytokine IL-6 regulate histone deacetylase dependent neuronal survival

Histone deacetylase (HDAC) proteins have a role in promoting neuronal survival in vitro, but the mechanism underlying this function has not been identified. Here we provide evidence that components of the neuronal microenvironment, including non-neuronal cells and defined culture media, can mitigate midbrain neuronal cell death induced by HDAC inhibitor treatment. Using microarrays we further identified gene expression changes taking place in non-neuronal cells as a result of HDAC inhibition. This analysis demonstrated that HDAC inhibitor treatment results in the down-regulation of immunity related signaling factors, in particular the Toll-like receptors (TLR). TLR signaling is active in cultured midbrain cells, yet blocking TLR receptors is not sufficient to cause neuronal cell death. In contrast, selective activation of this pathway using TLR ligands can modestly block the effects of HDAC inhibition. Furthermore, we observed that the negative effects of HDAC inhibitor treatment on neuronal survival could be more substantially blocked by the cytokine Interleukin-6 (IL-6), which is a major downstream target of TLR signaling. These data suggest that HDACs function to promote neuronal survival by activating a TLR and IL-6 dependent pathway.     

Dynamics of a memory trace: effects of sleep on consolidation

BACKGROUND: There is evidence that sleep is important for memory consolidation, but the underlying neuronal changes are not well understood. We studied the effect of sleep modulation on memory and on neuronal activity in a memory system of the domestic chick brain after the learning process of imprinting. Neurons in this system become, through imprinting, selectively responsive to a training (imprinting) stimulus and so possess the properties of a memory trace. RESULTS: The proportion of neurons responsive to the training stimulus reaches a maximum the day after training. We demonstrate that sleep is necessary for this maximum to be achieved, that sleep stabilizes the initially unstable, selective responses of neurons to the imprinting stimulus, and that for sleep to be effective, it must occur during a particular period of time after training. During this period, there is a time-dependent increase in EEG activity in the 5-6 Hz band, that is, in the lower range of the theta bandwidth. The effects of sleep disturbance on consolidation cannot be attributed to fatigue or to stress. CONCLUSIONS: We establish that long-term trace consolidation requires sleep within a restricted period shortly after learning. Undisturbed sleep is necessary for the stabilization of long-term memory, measured at the behavioral and neuronal levels, and of long-term but not short-term neuronal responsiveness to the training stimulus.     

A Neural Network Model of Sensorimotor Transformations in the Local Bending Reflex of the Medicinal Leech

SYNOPSIS. In response to a moderate mechanical stimulus, theleech withdraws by forming a U-shaped local bend beneath thestimulus. Interneurons in the local bending reflex receive multiplesensory inputs, and have outputs to many motor neurons, suggestingthat stimulus location on the body is represented in a distributedfashion. However, it is possible that as yet unidentified interneuronsdedicated to the detection of specific spatial locations arenecessary for the full range of the local bending motor output.Using the backpropagation algorithm to optimize connectionsin a model of the reflex, we showed that the distributed processingmodel of sensorimotor integration was consistent with our knowledgeof the connection strengths between identified neurons in thereflex. The model further showed that the spatial and temporalconstraints on the linkage between sensory representations andmotor output can be satisfied by the appropriate connectivitybetween layers of interneurons and motor neurons in the leech,as well as in other systems with similar circuitry.     

Odor similarity does not influence the time needed for odor processing

The brain's link between perception and action involves several steps, which include stimulus transduction, neuronal coding of the stimulus, comparison to a memory template and choice of an appropriate behavioral response. All of these need time, and many studies report that the time needed to compare two stimuli correlates inversely with the perceived distance between them. We developed a behavioral assay in which we tested the time that a honeybee needs to discriminate between odors consisting of mixtures of two components, and included both very similar and very different stimuli spanning four log-concentration ranges. Bees learned to discriminate all odors, including very similar odors and the same odor at different concentrations. Even though discriminating two very similar odors appears to be a more difficult task than discriminating two very distinct substances, we found that the time needed to make a choice for or against an odor was independent of odor similarity. Our data suggest that, irrespective of the nature of the olfactory code, the bee olfactory system evaluates odor quality after a constant interval. This may ensure that odors are only assessed after the olfactory network has optimized its representation.     

Switching neuronal state: optimal stimuli revealed using a stochastically-seeded gradient algorithm

Inducing a switch in neuronal state using energy optimal stimuli is relevant to a variety of problems in neuroscience. Analytical techniques from optimal control theory can identify such stimuli; however, solutions to the optimization problem using indirect variational approaches can be elusive in models that describe neuronal behavior. Here we develop and apply a direct gradient-based optimization algorithm to find stimulus waveforms that elicit a change in neuronal state while minimizing energy usage. We analyze standard models of neuronal behavior, the Hodgkin-Huxley and FitzHugh-Nagumo models, to show that the gradient-based algorithm: 1) enables automated exploration of a wide solution space, using stochastically generated initial waveforms that converge to multiple locally optimal solutions; and 2) finds optimal stimulus waveforms that achieve a physiological outcome condition, without a priori knowledge of the optimal terminal condition of all state variables. Analysis of biological systems using stochastically-seeded gradient methods can reveal salient dynamical mechanisms underlying the optimal control of system behavior. The gradient algorithm may also have practical applications in future work, for example, finding energy optimal waveforms for therapeutic neural stimulation that minimizes power usage and diminishes off-target effects and damage to neighboring tissue.