Sleep restores an optimal computational regime in cortical networks

Sleep is assumed to subserve homeostatic processes in the brain; however, the set point around which sleep tunes circuit computations is unknown. Slow-wave activity (SWA) is commonly used to reflect the homeostatic aspect of sleep; although it can indicate sleep pressure, it does not explain why animals need sleep. This study aimed to assess whether criticality may be the computational set point of sleep. By recording cortical neuron activity continuously for 10-14 d in freely behaving rats, we show that normal waking experience progressively disrupts criticality and that sleep functions to restore critical dynamics. Criticality is perturbed in a context-dependent manner, and waking experience is causal in driving these effects. The degree of deviation from criticality predicts future sleep/wake behavior more accurately than SWA, behavioral history or other neural measures. Our results demonstrate that perturbation and recovery of criticality is a network homeostatic mechanism consistent with the core, restorative function of sleep.

Tauopathy severely disrupts homeostatic set-points in emergent neural dynamics but not in the activity of individual neurons

The homeostatic regulation of neuronal activity is essential for robust computation; key set-points, such as firing rate, are actively stabilized to compensate for perturbations. From this perspective, the disruption of brain function central to neurodegenerative disease should reflect impairments of computationally essential set-points. Despite connecting neurodegeneration to functional outcomes, the impact of disease on set-points in neuronal activity is unknown. Here we present a comprehensive, theory-driven investigation of the effects of tau-mediated neurodegeneration on homeostatic set-points in neuronal activity. In a mouse model of tauopathy, we examine 27,000 hours of hippocampal recordings during free behavior throughout disease progression. Contrary to our initial hypothesis that tauopathy would impact set-points in spike rate and variance, we found that cell-level set-points are resilient to even the latest stages of disease. Instead, we find that tauopathy disrupts neuronal activity at the network-level, which we quantify using both pairwise measures of neuron interactions as well as measurement of the network's nearness to criticality, an ideal computational regime that is known to be a homeostatic set-point. We find that shifts in network criticality 1) track with symptoms, 2) predict underlying anatomical and molecular pathology, 3) occur in a sleep/wake dependent manner, and 4) can be used to reliably classify an animal's genotype. Our data suggest that the critical set-point is intact, but that homeostatic machinery is progressively incapable of stabilizing hippocampal networks, particularly during waking. This work illustrates how neurodegenerative processes can impact the computational capacity of neurobiological systems, and suggest an important connection between molecular pathology, circuit function, and animal behavior.

Stable representation of a naturalistic movie emerges from episodic activity with gain variability

Visual cortical responses are known to be highly variable across trials within an experimental session. However, the long-term stability of visual cortical responses is poorly understood. Here using chronic imaging of V1 in mice we show that neural responses to repeated natural movie clips are unstable across weeks. Individual neuronal responses consist of sparse episodic activity which are stable in time but unstable in gain across weeks. Further, we find that the individual episode, instead of neuron, serves as the basic unit of the week-to-week fluctuation. To investigate how population activity encodes the stimulus, we extract a stable one-dimensional representation of the time in the natural movie, using an unsupervised method. Most week-to-week fluctuation is perpendicular to the stimulus encoding direction, thus leaving the stimulus representation largely unaffected. We propose that precise episodic activity with coordinated gain changes are keys to maintain a stable stimulus representation in V1.

Diverse coactive neurons encode stimulus-driven and stimulus-independent variables

Both experimenter-controlled stimuli and stimulus-independent variables impact cortical neural activity. A major hurdle to understanding neural representation is distinguishing between qualitatively different causes of the fluctuating population activity. We applied an unsupervised low-rank tensor decomposition analysis to the recorded population activity in the visual cortex of awake mice in response to repeated presentations of naturalistic visual stimuli. We found that neurons covaried largely independently of individual neuron stimulus response reliability and thus encoded both stimulus-driven and stimulus-independent variables. Importantly, a neuron's response reliability and the neuronal coactivation patterns substantially reorganized for different external visual inputs. Analysis of recurrent balanced neural network models revealed that both the stimulus specificity and the mixed encoding of qualitatively different variables can arise from clustered external inputs. These results establish that coactive neurons with diverse response reliability mediate a mixed representation of stimulus-driven and stimulus-independent variables in the visual cortex.NEW & NOTEWORTHY V1 neurons covary largely independently of individual neuron's response reliability. A single neuron's response reliability imposes only a weak constraint on its encoding capabilities. Visual stimulus instructs a neuron's reliability and coactivation pattern. Network models revealed using clustered external inputs.

Precision multidimensional neural population code recovered from single intracellular recordings

Neurons in sensory cortices are more naturally and deeply integrated than any current neural population recording tools (e.g. electrode arrays, fluorescence imaging). Two concepts facilitate efforts to observe population neural code with single-cell recordings. First, even the highest quality single-cell recording studies find a fraction of the stimulus information in high-dimensional population recordings. Finding any of this missing information provides proof of principle. Second, neurons and neural populations are understood as coupled nonlinear differential equations. Therefore, fitted ordinary differential equations provide a basis for single-trial single-cell stimulus decoding. We obtained intracellular recordings of fluctuating transmembrane current and potential in mouse visual cortex during stimulation with drifting gratings. We use mean deflection from baseline when comparing to prior single-cell studies because action potentials are too sparse and the deflection response to drifting grating stimuli (e.g. tuning curves) are well studied. Equation-based decoders allowed more precise single-trial stimulus discrimination than tuning-curve-base decoders. Performance varied across recorded signal types in a manner consistent with population recording studies and both classification bases evinced distinct stimulus-evoked phases of population dynamics, providing further corroboration. Naturally and deeply integrated observations of population dynamics would be invaluable. We offer proof of principle and a versatile framework.

Stability of motor cortex network states during learning-associated neural reorganizations

A substantial reorganization of neural activity and neuron-to-movement relationship in motor cortical circuits accompanies the emergence of reproducible movement patterns during motor learning. Little is known about how this tempest of neural activity restructuring impacts the stability of network states in recurrent cortical circuits. To investigate this issue, we reanalyzed data in which we recorded for 14 days via population calcium imaging the activity of the same neural populations of pyramidal neurons in layer 2/3 and layer 5 of forelimb motor and premotor cortex in mice during the daily learning of a lever-press task. We found that motor cortex network states remained stable with respect to the critical network state during the extensive reorganization of both neural population activity and its relation to lever movement throughout learning. Specifically, layer 2/3 cortical circuits unceasingly displayed robust evidence for operating at the critical network state, a regime that maximizes information capacity and transmission and provides a balance between network robustness and flexibility. In contrast, layer 5 circuits operated away from the critical network state for all 14 days of recording and learning. In conclusion, this result indicates that the wide-ranging malleability of synapses, neurons, and neural connectivity during learning operates within the constraint of a stable and layer-specific network state regarding dynamic criticality, and suggests that different cortical layers operate under distinct constraints because of their specialized goals.NEW & NOTEWORTHY The neural activity reorganizes throughout motor learning, but how this reorganization impacts the stability of network states is unclear. We used two-photon calcium imaging to investigate how the network states in layer 2/3 and layer 5 of forelimb motor and premotor cortex are modulated by motor learning. We show that motor cortex network states are layer-specific and constant regarding criticality during neural activity reorganization, and suggests that layer-specific constraints could be motivated by different functions.

Psychosocial health of farmers

Background

Studies from several countries have shown increased suicide mortality rates among farmers as compared to non-farmer citizens. Research has shown that farmers often face psychosocial problems resulting from business operations and financial problems. However, there are hardly European studies on these potential problems. Therefore, the study aimed to assess the psychosocial health and health services use among farmers in The Netherlands.

Methods

A qualitative study including 16 semi-structured interviews with farmers in eastern Netherlands. The interview guide was based on the health care use process model of Levesque. Interviews were verbatim transcribed and thematically analyzed in MAXQDA.

Results

This study identified several risk conditions for psychosocial health problems related to the social and occupational position of farmers, including: financial problems, persistently high work pressure, negative public views towards farming, uncertainties due to continuously changing regulations, traditional attitudes emphasizing stoicism and hard work, and lack of support from nearby farmers. Farmers are often not seeking help for psychosocial problems, due to underestimation of their own health needs, lack of knowledge and confidence in professional support, and difficult in using and finding support tailored to their needs.

Conclusions

This study for eastern Netherlands revealed several themes contributing to a high risk of farmers to develop psychosocial problems, and low use of appropriate mental health and social services.

Key messages

Farmers are at increased risk of psychosocial problems, due to social and work-related conditions. Nonetheless, these farmers were less likely to find and use health and social services. This implies an urgent need for the development of services that provide appropriate psychological support to farmers in the Netherlands.

Characteristics of supported living – a perspective of professionals and potential residents

Background

Policies are increasingly decentralized, and people with a demand for support often live in familiar environments and regular neighborhoods instead of institutions. To meet the demand for support, the local government aims to offer supported living facilities in Staphorst, Netherlands. Accordingly, this study aimed to provide insight into the characteristics of supported living from the perspective of professionals and potential residents.

Methods

Semi-structured interviews were conducted with professionals and potential residents to obtain insight about their perspectives regarding supported living. First, ten interviews were conducted with professionals (i.e. local government, care, welfare, and housing corporation), followed by six interviews with potential residents and/or their parents. The interviews were transcribed, coded and analyzed by two researchers.

Results

A small amount of the residents in Staphorst would benefit from supported living. This concerns mainly (young) adults with a mild intellectual disability and/or a mental illness who need practical, social and emotional support in order to live on their own. Composing the support, it is important to acknowledge the religious identity of the potential residents and their families. Furthermore, the interviews with professionals and potential residents revealed that the following options should be considered: time-out, short- and long-term support, daycare and open house facilities, various types of support (i.e. volunteer and professional) and combining target groups (i.e. adolescents and adults).

Conclusions

This study reveals several options to consider when composing supported living in Staphorst. Accordingly, most participants emphasized the need for short-term support focusing on skills related to independent living. Furthermore, it is important to involve professionals and potential residents and/or their parents in order to offer sustainable supported living for the residents in Staphorst.

Key messages

Most participants emphasized the need for short-term support focusing on skills related to independent living. Both professionals and potential residents should be involved when composing supported living facilities.

Pre-Synaptic Pool Modification (PSPM): A supervised learning procedure for recurrent spiking neural networks

Learning synaptic weights of spiking neural network (SNN) models that can reproduce target spike trains from provided neural firing data is a central problem in computational neuroscience and spike-based computing. The discovery of the optimal weight values can be posed as a supervised learning task wherein the weights of the model network are chosen to maximize the similarity between the target spike trains and the model outputs. It is still largely unknown whether optimizing spike train similarity of highly recurrent SNNs produces weight matrices similar to those of the ground truth model. To this end, we propose flexible heuristic supervised learning rules, termed Pre-Synaptic Pool Modification (PSPM), that rely on stochastic weight updates in order to produce spikes within a short window of the desired times and eliminate spikes outside of this window. PSPM improves spike train similarity for all-to-all SNNs and makes no assumption about the post-synaptic potential of the neurons or the structure of the network since no gradients are required. We test whether optimizing for spike train similarity entails the discovery of accurate weights and explore the relative contributions of local and homeostatic weight updates. Although PSPM improves similarity between spike trains, the learned weights often differ from the weights of the ground truth model, implying that connectome inference from spike data may require additional constraints on connectivity statistics. We also find that spike train similarity is sensitive to local updates, but other measures of network activity such as avalanche distributions, can be learned through synaptic homeostasis.

Cortical Circuit Dynamics Are Homeostatically Tuned to Criticality In Vivo

Homeostatic mechanisms stabilize neuronal activity in vivo, but whether this process gives rise to balanced network dynamics is unknown. Here, we continuously monitored the statistics of network spiking in visual cortical circuits in freely behaving rats for 9 days. Under control conditions in light and dark, networks were robustly organized around criticality, a regime that maximizes information capacity and transmission. When input was perturbed by visual deprivation, network criticality was severely disrupted and subsequently restored to criticality over 48 h. Unexpectedly, the recovery of excitatory dynamics preceded homeostatic plasticity of firing rates by >30 h. We utilized model investigations to manipulate firing rate homeostasis in a cell-type-specific manner at the onset of visual deprivation. Our results suggest that criticality in excitatory networks is established by inhibitory plasticity and architecture. These data establish that criticality is consistent with a homeostatic set point for visual cortical dynamics and suggest a key role for homeostatic regulation of inhibition.

Coupling of synaptic inputs to local cortical activity differs among neurons and adapts after stimulus onset

Cortical activity contributes significantly to the high variability of sensory responses of interconnected pyramidal neurons, which has crucial implications for sensory coding. Yet, largely because of technical limitations of in vivo intracellular recordings, the coupling of a pyramidal neuron's synaptic inputs to the local cortical activity has evaded full understanding. Here we obtained excitatory synaptic conductance ( g) measurements from putative pyramidal neurons and local field potential (LFP) recordings from adjacent cortical circuits during visual processing in the turtle whole brain ex vivo preparation. We found a range of g-LFP coupling across neurons. Importantly, for a given neuron, g-LFP coupling increased at stimulus onset and then relaxed toward intermediate values during continued visual stimulation. A model network with clustered connectivity and synaptic depression reproduced both the diversity and the dynamics of g-LFP coupling. In conclusion, these results establish a rich dependence of single-neuron responses on anatomical, synaptic, and emergent network properties. NEW & NOTEWORTHY Cortical neurons are strongly influenced by the networks in which they are embedded. To understand sensory processing, we must identify the nature of this influence and its underlying mechanisms. Here we investigate synaptic inputs to cortical neurons, and the nearby local field potential, during visual processing. We find a range of neuron-to-network coupling across cortical neurons. This coupling is dynamically modulated during visual processing via biophysical and emergent network properties.

The turtle visual system mediates a complex spatiotemporal transformation of visual stimuli into cortical activity

The three-layered visual cortex of turtle is characterized by extensive intracortical axonal projections and receives non-retinotopic axonal projections from lateral geniculate nucleus. What spatiotemporal transformation of visual stimuli into cortical activity arises from such tangle of malleable cortical inputs and intracortical connections? To address this question, we obtained band-pass filtered extracellular recordings of neural activity in turtle dorsal cortex during visual stimulation of the retina. We discovered important spatial and temporal features of stimulus-modulated cortical local field potential (LFP) recordings. Spatial receptive fields span large areas of the visual field, have an intricate internal structure, and lack directional tuning. The receptive field structure varies across recording sites in a distant-dependent manner. Such composite spatial organization of stimulus-modulated cortical activity is accompanied by an equally multifaceted temporal organization. Cortical visual responses are delayed, persistent, and oscillatory. Further, prior cortical activity contributes globally to adaptation in turtle visual cortex. In conclusion, these results demonstrate convoluted spatiotemporal transformations of visual stimuli into stimulus-modulated cortical activity that, at present, largely evade computational frameworks.

Criticality predicts maximum irregularity in recurrent networks of excitatory nodes

A rigorous understanding of brain dynamics and function requires a conceptual bridge between multiple levels of organization, including neural spiking and network-level population activity. Mounting evidence suggests that neural networks of cerebral cortex operate at a critical regime, which is defined as a transition point between two phases of short lasting and chaotic activity. However, despite the fact that criticality brings about certain functional advantages for information processing, its supporting evidence is still far from conclusive, as it has been mostly based on power law scaling of size and durations of cascades of activity. Moreover, to what degree such hypothesis could explain some fundamental features of neural activity is still largely unknown. One of the most prevalent features of cortical activity in vivo is known to be spike irregularity of spike trains, which is measured in terms of the coefficient of variation (CV) larger than one. Here, using a minimal computational model of excitatory nodes, we show that irregular spiking (CV > 1) naturally emerges in a recurrent network operating at criticality. More importantly, we show that even at the presence of other sources of spike irregularity, being at criticality maximizes the mean coefficient of variation of neurons, thereby maximizing their spike irregularity. Furthermore, we also show that such a maximized irregularity results in maximum correlation between neuronal firing rates and their corresponding spike irregularity (measured in terms of CV). On the one hand, using a model in the universality class of directed percolation, we propose new hallmarks of criticality at single-unit level, which could be applicable to any network of excitable nodes. On the other hand, given the controversy of the neural criticality hypothesis, we discuss the limitation of this approach to neural systems and to what degree they support the criticality hypothesis in real neural networks. Finally, we discuss the limitations of applying our results to real networks and to what degree they support the criticality hypothesis.

Induced cortical oscillations in turtle cortex are coherent at the mesoscale of population activity, but not at the microscale of the membrane potential of neurons

Bursts of oscillatory neural activity have been hypothesized to be a core mechanism by which remote brain regions can communicate. Such a hypothesis raises the question to what extent oscillations are coherent across spatially distant neural populations. To address this question, we obtained local field potential (LFP) and membrane potential recordings from the visual cortex of turtle in response to visual stimulation of the retina. The time-frequency analysis of these recordings revealed pronounced bursts of oscillatory neural activity and a large trial-to-trial variability in the spectral and temporal properties of the observed oscillations. First, local bursts of oscillations varied from trial to trial in both burst duration and peak frequency. Second, oscillations of a given recording site were not autocoherent; i.e., the phase did not progress linearly in time. Third, LFP oscillations at spatially separate locations within the visual cortex were more phase coherent in the presence of visual stimulation than during ongoing activity. In contrast, the membrane potential oscillations from pairs of simultaneously recorded pyramidal neurons showed smaller phase coherence, which did not change when switching from black screen to visual stimulation. In conclusion, neuronal oscillations at distant locations in visual cortex are coherent at the mesoscale of population activity, but coherence is largely absent at the microscale of the membrane potential of neurons.NEW & NOTEWORTHY Coherent oscillatory neural activity has long been hypothesized as a potential mechanism for communication across locations in the brain. In this study we confirm the existence of coherent oscillations at the mesoscale of integrated cortical population activity. However, at the microscopic level of neurons, we find no evidence for coherence among oscillatory membrane potential fluctuations. These results raise questions about the applicability of the communication through coherence hypothesis to the level of the membrane potential.

Network activity influences the subthreshold and spiking visual responses of pyramidal neurons in the three-layer turtle cortex

A primary goal of systems neuroscience is to understand cortical function, typically by studying spontaneous and stimulus-modulated cortical activity. Mounting evidence suggests a strong and complex relationship exists between the ongoing and stimulus-modulated cortical state. To date, most work in this area has been based on spiking in populations of neurons. While advantageous in many respects, this approach is limited in scope: it records the activity of a minority of neurons and gives no direct indication of the underlying subthreshold dynamics. Membrane potential recordings can fill these gaps in our understanding, but stable recordings are difficult to obtain in vivo. Here, we recorded subthreshold cortical visual responses in the ex vivo turtle eye-attached whole brain preparation, which is ideally suited for such a study. We found that, in the absence of visual stimulation, the network was "synchronous"; neurons displayed network-mediated transitions between hyperpolarized (Down) and depolarized (Up) membrane potential states. The prevalence of these slow-wave transitions varied across turtles and recording sessions. Visual stimulation evoked similar Up states, which were on average larger and less reliable when the ongoing state was more synchronous. Responses were muted when immediately preceded by large, spontaneous Up states. Evoked spiking was sparse, highly variable across trials, and mediated by concerted synaptic inputs that were, in general, only very weakly correlated with inputs to nearby neurons. Together, these results highlight the multiplexed influence of the cortical network on the spontaneous and sensory-evoked activity of individual cortical neurons.NEW & NOTEWORTHY Most studies of cortical activity focus on spikes. Subthreshold membrane potential recordings can provide complementary insight, but stable recordings are difficult to obtain in vivo. Here, we recorded the membrane potentials of cortical neurons during ongoing and visually evoked activity. We observed a strong relationship between network and single-neuron evoked activity spanning multiple temporal scales. The membrane potential perspective of cortical dynamics thus highlights the influence of intrinsic network properties on visual processing.

Adaptation modulates correlated subthreshold response variability in visual cortex

Cortical sensory responses are highly variable across stimulus presentations. This variability can be correlated across neurons (due to some combination of dense intracortical connectivity, cortical activity level, and cortical state), with fundamental implications for population coding. Yet the interpretation of correlated response variability (or "noise correlation") has remained fraught with difficulty, in part because of the restriction to extracellular neuronal spike recordings. Here, we measured response variability and its correlation at the most microscopic level of electrical neural activity, the membrane potential, by obtaining dual whole cell recordings from pairs of cortical pyramidal neurons during visual processing in the turtle whole brain ex vivo preparation. We found that during visual stimulation, correlated variability adapts toward an intermediate level and that this correlation dynamic is likely mediated by intracortical mechanisms. A model network with external inputs, synaptic depression, and structure reproduced the observed dynamics of correlated variability. These results suggest that intracortical adaptation self-organizes cortical circuits toward a balanced regime at which correlated variability is maintained at an intermediate level.NEW & NOTEWORTHY Correlated response variability has profound implications for stimulus encoding, yet our understanding of this phenomenon is based largely on spike data. Here, we investigate the dynamics and mechanisms of membrane potential-correlated variability (CC) in visual cortex with a combined experimental and computational approach. We observe a visually evoked increase in CC, followed by a fast return to baseline. Our results further suggest a link between this observation and the adaptation-mediated dynamics of emergent network phenomena.

Adaptation towards scale-free dynamics improves cortical stimulus discrimination at the cost of reduced detection

Fundamental to the function of nervous systems is the ability to reorganize to cope with changing sensory input. Although well-studied in single neurons, how such adaptive versatility manifests in the collective population dynamics and function of cerebral cortex remains unknown. Here we measured population neural activity with microelectrode arrays in turtle visual cortex while visually stimulating the retina. First, we found that, following the onset of stimulation, adaptation tunes the collective population dynamics towards a special regime with scale-free spatiotemporal activity, after an initial large-scale transient response. Concurrently, we observed an adaptive tradeoff between two important aspects of population coding-sensory detection and discrimination. As adaptation tuned the cortex toward scale-free dynamics, stimulus discrimination was enhanced, while stimulus detection was reduced. Finally, we used a network-level computational model to show that short-term synaptic depression was sufficient to mechanistically explain our experimental results. In the model, scale-free dynamics emerge only when the model operates near a special regime called criticality. Together our model and experimental results suggest unanticipated functional benefits and costs of adaptation near criticality in visual cortex.

Neocortical activity is stimulus- and scale-invariant

Mounting evidence supports the hypothesis that the cortex operates near a critical state, defined as the transition point between order (large-scale activity) and disorder (small-scale activity). This criticality is manifested by power law distribution of the size and duration of spontaneous cascades of activity, which are referred as neuronal avalanches. The existence of such neuronal avalanches has been confirmed by several studies both in vitro and in vivo, among different species and across multiple spatial scales. However, despite the prevalence of scale free activity, still very little is known concerning whether and how the scale-free nature of cortical activity is altered during external stimulation. To address this question, we performed in vivo two-photon population calcium imaging of layer 2/3 neurons in primary visual cortex of behaving mice during visual stimulation and conducted statistical analyses on the inferred spike trains. Our investigation for each mouse and condition revealed power law distributed neuronal avalanches, and irregular spiking individual neurons. Importantly, both the avalanche and the spike train properties remained largely unchanged for different stimuli, while the cross-correlation structure varied with stimuli. Our results establish that microcircuits in the visual cortex operate near the critical regime, while rearranging functional connectivity in response to varying sensory inputs.

Inferring presynaptic population spiking from single-trial membrane potential recordings

BACKGROUND

The time-varying membrane potential of a cortical neuron contains important information about the network activity. Extracting this information requires separating excitatory and inhibitory synaptic inputs from single-trial membrane potential recordings without averaging across trials.

NEW METHOD

We propose a method to extract the time course of excitatory and inhibitory synaptic inputs to a neuron from a single-trial membrane potential recording. The method takes advantage of the differences in the time constants and the reversal potentials of the excitatory and inhibitory synaptic currents, which allows the untangling of the two conductance types.

RESULTS

We evaluate the applicability of the method on a leaky integrate-and-fire model neuron and find high quality of estimation of excitatory synaptic conductance changes and presynaptic population spikes. Application of the method to a real cortical neuron with known synaptic inputs in a brain slice returns high-quality estimation of the time course of the excitatory synaptic conductance. Application of the method to membrane potential recordings from a cortical pyramidal neuron of an intact brain reveals complex network activity.

COMPARISON WITH EXISTING METHODS

Existing methods are based on repeated trials and thus are limited to estimating the statistical features of synaptic conductance changes, or, when based on single trials, are limited to special cases, have low temporal resolution, or are impractically complicated.

CONCLUSIONS

We propose and test an efficient method for estimating the full time course of excitatory and inhibitory synaptic conductances from single-trial membrane potential recordings. The method is sufficiently simple to ensure widespread use in neuroscience.

Coherent and intermittent ensemble oscillations emerge from networks of irregular spiking neurons

Local field potential (LFP) recordings from spatially distant cortical circuits reveal episodes of coherent gamma oscillations that are intermittent, and of variable peak frequency and duration. Concurrently, single neuron spiking remains largely irregular and of low rate. The underlying potential mechanisms of this emergent network activity have long been debated. Here we reproduce such intermittent ensemble oscillations in a model network, consisting of excitatory and inhibitory model neurons with the characteristics of regular-spiking (RS) pyramidal neurons, and fast-spiking (FS) and low-threshold spiking (LTS) interneurons. We find that fluctuations in the external inputs trigger reciprocally connected and irregularly spiking RS and FS neurons in episodes of ensemble oscillations, which are terminated by the recruitment of the LTS population with concurrent accumulation of inhibitory conductance in both RS and FS neurons. The model qualitatively reproduces experimentally observed phase drift, oscillation episode duration distributions, variation in the peak frequency, and the concurrent irregular single-neuron spiking at low rate. Furthermore, consistent with previous experimental studies using optogenetic manipulation, periodic activation of FS, but not RS, model neurons causes enhancement of gamma oscillations. In addition, increasing the coupling between two model networks from low to high reveals a transition from independent intermittent oscillations to coherent intermittent oscillations. In conclusion, the model network suggests biologically plausible mechanisms for the generation of episodes of coherent intermittent ensemble oscillations with irregular spiking neurons in cortical circuits.

Turtle Dorsal Cortex Pyramidal Neurons Comprise Two Distinct Cell Types with Indistinguishable Visual Responses

A detailed inventory of the constituent pieces in cerebral cortex is considered essential to understand the principles underlying cortical signal processing. Specifically, the search for pyramidal neuron subtypes is partly motivated by the hypothesis that a subtype-specific division of labor could create a rich substrate for computation. On the other hand, the extreme integration of individual neurons into the collective cortical circuit promotes the hypothesis that cellular individuality represents a smaller computational role within the context of the larger network. These competing hypotheses raise the important question to what extent the computational function of a neuron is determined by its individual type or by its circuit connections. We created electrophysiological profiles from pyramidal neurons within the sole cellular layer of turtle visual cortex by measuring responses to current injection using whole-cell recordings. A blind clustering algorithm applied to these data revealed the presence of two principle types of pyramidal neurons. Brief diffuse light flashes triggered membrane potential fluctuations in those same cortical neurons. The apparently network driven variability of the visual responses concealed the existence of subtypes. In conclusion, our results support the notion that the importance of diverse intrinsic physiological properties is minimized when neurons are embedded in a synaptic recurrent network.

Excitable Membranes and Action Potentials in Paramecia: An Analysis of the Electrophysiology of Ciliates

The ciliate Paramecium caudatum possesses an excitable cell membrane whose action potentials (APs) modulate the trajectory of the cell swimming through its freshwater environment. While many stimuli affect the membrane potential and trajectory, students can use current injection and extracellular ionic concentration changes to explore how APs cause reversal of the cell's motion. Students examine these stimuli through intracellular recordings, also gaining insight into the practices of electrophysiology. Paramecium's large size of around 150 µm, simple care, and relative ease to penetrate make them ideal model organisms for undergraduate students' laboratory study. The direct link between behavior and excitable membranes has thought provoking evolutionary implications for the study of paramecia. Recording from the cell, students note a small resting potential around -30 mV, differing from animal resting potentials. By manipulating ion concentrations, APs of the relatively long length of 20-30 ms up to several minutes with depolarizations maxing over 0 mV are observed. Through comparative analysis of membrane potentials and the APs induced by either calcium or barium, students can deduce the causative ions for the APs as well as the mechanisms of paramecium APs. Current injection allows students to calculate quantitative electric characteristics of the membrane. Analysis will follow the literature's conclusion in a V-Gated Ca(++) influx and depolarization resulting in feedback from intracellular Ca(++) that inactivates V-Gated Ca(++) channels and activates Ca-Dependent K(+) channels through a secondary messenger cascade that results in the K(+) efflux and repolarization.

Response properties of visual neurons in the turtle nucleus isthmi

The optic tectum holds a central position in the tectofugal pathway of non-mammalian species and is reciprocally connected with the nucleus isthmi. Here, we recorded from individual nucleus isthmi pars parvocellularis (Ipc) neurons in the turtle eye-attached whole-brain preparation in response to a range of computer-generated visual stimuli. Ipc neurons responded to a variety of moving or flashing stimuli as long as those stimuli were small. When mapped with a moving spot, the excitatory receptive field was of circular Gaussian shape with an average half-width of less than 3°. We found no evidence for directional sensitivity. For moving spots of varying sizes, the measured Ipc response-size profile was reproduced by the linear Difference-of-Gaussian model, which is consistent with the superposition of a narrow excitatory center and an inhibitory surround. Intracellular Ipc recordings revealed a strong inhibitory connection from the nucleus isthmi pars magnocellularis (Imc), which has the anatomical feature to provide a broad inhibitory projection. The recorded Ipc response properties, together with the modulatory role of the Ipc in tectal visual processing, suggest that the columns of Ipc axon terminals in turtle optic tectum bias tectal visual responses to small dark changing features in visual scenes.

Recurrent antitopographic inhibition mediates competitive stimulus selection in an attention network

Topographically organized neurons represent multiple stimuli within complex visual scenes and compete for subsequent processing in higher visual centers. The underlying neural mechanisms of this process have long been elusive. We investigate an experimentally constrained model of a midbrain structure: the optic tectum and the reciprocally connected nucleus isthmi. We show that a recurrent antitopographic inhibition mediates the competitive stimulus selection between distant sensory inputs in this visual pathway. This recurrent antitopographic inhibition is fundamentally different from surround inhibition in that it projects on all locations of its input layer, except to the locus from which it receives input. At a larger scale, the model shows how a focal top-down input from a forebrain region, the arcopallial gaze field, biases the competitive stimulus selection via the combined activation of a local excitation and the recurrent antitopographic inhibition. Our findings reveal circuit mechanisms of competitive stimulus selection and should motivate a search for anatomical implementations of these mechanisms in a range of vertebrate attentional systems.

Electrophysiological properties of isthmic neurons in frogs revealed by in vitro and in vivo studies

The frog nucleus isthmi (parabigeminal nucleus in mammals) is a visually responsive, cholinergic and anatomically well-defined group of neurons in the midbrain. It shares reciprocal topographic projections with the ipsilateral optic tectum (superior colliculus in mammals) and strongly influences visual processing. Anatomical and biochemical information indicates the existence of distinct neural populations within the frog nucleus isthmi, which raises the question: are there electrophysiological distinctions between neurons that are putatively classified by their anatomical and biochemical properties? To address this question, we measured frog nucleus isthmi neuron cellular properties in vitro and visual response properties in vivo. No evidence for distinct electrophysiological classes of neurons was found. We thus conclude that, despite the anatomical and biochemical differences, the cells of the frog nucleus isthmi respond homogeneously to both current injections and simple visual stimuli.

Motion processing with wide-field neurons in the retino-tecto-rotundal pathway

The retino-tecto-rotundal pathway is the main visual pathway in non-mammalian vertebrates and has been found to be highly involved in visual processing. Despite the extensive receptive fields of tectal and rotundal wide-field neurons, pattern discrimination tasks suggest a system with high spatial resolution. In this paper, we address the problem of how global processing performed by motion-sensitive wide-field neurons can be brought into agreement with the concept of a local analysis of visual stimuli. As a solution to this problem, we propose a firing-rate model of the retino-tecto-rotundal pathway which describes how spatiotemporal information can be organized and retained by tectal and rotundal wide-field neurons while processing Fourier-based motion in absence of periodic receptive-field structures. The model incorporates anatomical and electrophysiological experimental data on tectal and rotundal neurons, and the basic response characteristics of tectal and rotundal neurons to moving stimuli are captured by the model cells. We show that local velocity estimates may be derived from rotundal-cell responses via superposition in a subsequent processing step. Experimentally testable predictions which are both specific and characteristic to the model are provided. Thus, a conclusive explanation can be given of how the retino-tecto-rotundal pathway enables the animal to detect and localize moving objects or to estimate its self-motion parameters.

Intricate phase diagram of a prevalent visual circuit reveals universal dynamics, phase transitions, and resonances

Neural feedback-triads consisting of two feedback loops with a nonreciprocal lateral connection from one loop to the other are ubiquitous in the brain. We show analytically that the dynamics of this network topology are determined by algebraic combinations of its five synaptic weights. Exploration of network activity over the parameter space demonstrates the importance of the nonreciprocal lateral connection and reveals intricate behavior involving continuous transitions between qualitatively different activity states. In addition, we show that the response to periodic inputs is narrowly tuned around a center frequency determined by the effective synaptic parameters.

Generating oscillatory bursts from a network of regular spiking neurons without inhibition

Avian nucleus isthmi pars parvocellularis (Ipc) neurons are reciprocally connected with the layer 10 (L10) neurons in the optic tectum and respond with oscillatory bursts to visual stimulation. Our in vitro experiments show that both neuron types respond with regular spiking to somatic current injection and that the feedforward and feedback synaptic connections are excitatory, but of different strength and time course. To elucidate mechanisms of oscillatory bursting in this network of regularly spiking neurons, we investigated an experimentally constrained model of coupled leaky integrate-and-fire neurons with spike-rate adaptation. The model reproduces the observed Ipc oscillatory bursting in response to simulated visual stimulation. A scan through the model parameter volume reveals that Ipc oscillatory burst generation can be caused by strong and brief feedforward synaptic conductance changes. The mechanism is sensitive to the parameter values of spike-rate adaptation. In conclusion, we show that a network of regular-spiking neurons with feedforward excitation and spike-rate adaptation can generate oscillatory bursting in response to a constant input.

Contextual interactions in a generalized energy model of complex cells

We propose a generalized energy model of complex cells to describe modulatory contextual influences on the responses of neurons in the primary visual cortex (V1). Many orientation-selective cells in V1 respond to contrast of orientation and motion of stimuli exciting the classical receptive field (CRF) and the non-CRF, or surround. In the proposed model, a central spatiotemporal filter, defining the CRF, is nonlinearly combined with a spatiotemporal filter extending into the non-CRF. These filters are assumed to describe simple-cell responses, while the nonlinear combination of their responses describes the responses of complex cells. This mathematical operation accounts for the inherent nonlinearity of complex cells, such as phase independence and frequency doubling, and for nonlinear interactions between stimuli in the CRF and surround of the cell, including sensitivity to feature contrast. If only the CRF of the generalized complex cell is stimulated by a drifting grating, the model reduces to the standard energy model. The theoretical predictions of the model are supported by computer simulations and compared with experimental data from V1.

Identification of brain- and bone-specific breast cancer metastasis genes

In breast cancer, metastases are relatively widely distributed, with the most common sites being bone, regional lymph nodes, lung, liver, and brain. The detailed mechanism of organ-specific metastasis is poorly understood. In this study, we initiated a search for genes that are implicated in brain or bone metastasis of primary human breast cancer. We generated gene expression profiles of 18 brain and eight bone metastases derived from primary breast tumors. We identified 73 genes differentially expressed between brain and bone metastases. Visualization of the differential gene expression profiles by correspondence and cluster analyses shows that the metastases clearly separate into two distinct groups as an exact reflection of their site of metastasis. Moreover, the analysis of this gene set in primary breast tumors relapsing to either bone or brain allowed accurate categorization of the tumors according to their metastatic site. The identified genes may prove to be excellent markers to predict the site of metastasis in breast cancer patients and could lead to tailor-made therapy to an individual patient.

Distributed delays stabilize neural feedback systems

We consider the effect of distributed delays in neural feedback systems. The avian optic tectum is reciprocally connected with the isthmic nuclei. Extracellular stimulation combined with intracellular recordings reveal a range of signal delays from 3 to 9 ms between isthmotectal elements. This observation together with prior mathematical analysis concerning the influence of a delay distribution on system dynamics raises the question whether a broad delay distribution can impact the dynamics of neural feedback loops. For a system of reciprocally connected model neurons, we found that distributed delays enhance system stability in the following sense. With increased distribution of delays, the system converges faster to a fixed point and converges slower toward a limit cycle. Further, the introduction of distributed delays leads to an increased range of the average delay value for which the system's equilibrium point is stable. The system dynamics are determined almost exclusively by the mean and the variance of the delay distribution and show only little dependence on the particular shape of the distribution.

Winner-take-all selection in a neural system with delayed feedback

We consider the effects of temporal delay in a neural feedback system with excitation and inhibition. The topology of our model system reflects the anatomy of the avian isthmic circuitry, a feedback structure found in all classes of vertebrates. We show that the system is capable of performing a 'winner-take-all' selection rule for certain combinations of excitatory and inhibitory feedback. In particular, we show that when the time delays are sufficiently large a system with local inhibition and global excitation can function as a 'winner-take-all' network and exhibit oscillatory dynamics. We demonstrate how the origin of the oscillations can be attributed to the finite delays through a linear stability analysis.

Comparison of gene expression data from human and mouse breast cancers: identification of a conserved breast tumor gene set

The aim of our work was to establish a database for breast cancer gene expression data in order to compare human and mouse breast cancer. We identified human and mouse homologues genes and compared the expression profile of 24 human breast tumors with 6 WAP-SVT/t breast tumors (WAP-SVT/t animals, line 8). Our studies confirmed the heterogeneity in gene expression of human as well as mouse breast cancer cells. However, 63 genes were found to be differentially expressed (upregulated: 40; downregulated: 23 genes) in at least 75% of the breast tumors of both species. To differentiate between early and late events in tumor formation, we compared the 63 differentially expressed genes with a mouse data set obtained from hyperplastic mammary glands. This revealed that the majority of the early deregulated genes are cell proliferation specific. These early changes seem to be necessary although not sufficient for breast cancer formation. Late alterations concern mainly genes belonging to the category of cell communication and metabolism. Interestingly, most of the 63 conserved genes are commonly associated with tumorigenesis.

Contextual Interaction of GABAergic Circuitry With Dynamic Synapses

Visual context shapes human perception, yet our understanding of this phenomenon in terms of synaptic circuitry is still rudimentary. Our in vitro experiments with avian tectum reveal two distinct GABAergic pathways that mediate the spatiotemporal tectal interaction of retinal inputs. One pathway mediates postsynaptic lateral inhibition. The other pathway interacts with the synaptic depression of retinotectal synapses. Simulations of an experimentally constrained model including the two pathways reproduce the observed avian tectum wide-field neuron's sensitivity to small and moving stimuli, while being insensitive to whole-field motion.

Iterative cooperation between parallel pathways for object and background motion

In a typical visual scene, one or more objects move relative to a larger background, which can itself be in motion as a result of the observer's eyes moving with respect to the outside world. Here we show that accurate estimation of the background motion from an image velocity field can be accomplished through an iterative cooperation between two modules: one that specializes in calculating a weighted average velocity and another one calculating a velocity contrast map. We build on our analysis to provide a model for the tectum-pretectum loop in the nonmammalian midbrain. Our model accounts for some of the known properties of the tectal neurons (sensitivity to relative motion) and pretectal neurons (sensitivity to whole-field motion). It also agrees with our knowledge of the pretectotectal projection (divergent and inhibitory), and with the results of lesion studies in which the pretectal input to the tectum was removed, leading to hyperactivity of the tectal neurons and the animal. Our model also makes a testable prediction regarding the tectopretectal projection, i.e., that the presence of a larger object and a bigger discrepancy between the directions of motion for the object and the background lead to a larger error by the pretectum in estimating the background motion when the tectal input is abolished.

Variational calculation of the limit cycle and its frequency in a two-neuron model with delay

We consider a model system of two coupled Hopfield neurons, which is described by delay differential equations taking into account the finite signal propagation and processing times. When the delay exceeds a critical value, a limit cycle emerges via a supercritical Hopf bifurcation. First, we calculate its frequency and trajectory perturbatively by applying the Poincaré-Lindstedt method. Then, the perturbation series are resummed by means of the Shohat expansion in good agreement with numerical values. However, with increasing delay, the accuracy of the results from the Shohat expansion worsens. We thus apply variational perturbation theory (VPT) to the perturbation expansions to obtain more accurate results, which moreover hold even in the limit of large delays.

[New antiarrhythmic drugs for therapy of atrial fibrillation: I. Ion channel blockers]

During the last ten years we have made substantial progress in our understanding of the underlying mechanisms of atrial fibrillation. The high rate associated alterations in electrical and structural properties of the atria, referred to as atrial remodeling, promote the progression of atrial fibrillation. The development of new therapeutic approaches addresses three different directions: (i) prevention of atrial remodeling, especially of structural remodeling; (ii) increase of long-term efficacy of currently used drugs and improvement of their side-effect profile; and (iii) design of atria- and pathology-specific antiarrhythmic drugs without concomitant proarrhythmic effects in the ventricles. The current review outlines the pathophysiology of atrial fibrillation and focuses on electrical remodeling. The properties of new antiarrhythmic drugs for atrial fibrillation are discussed in detail.

Sparse spatial sampling for the computation of motion in multiple stages

The avian retino-tecto-rotundal pathway plays a central role in motion analysis and features complex connectivity. Yet, the relation between the pathway's structural arrangement and motion computation has remained elusive. For an important type of tectal wide-field neuron, the stratum griseum centrale type I (SGC-I) neuron, we quantified its structure and found a spatially sparse but extensive sampling of the retinal projection. A computational investigation revealed that these structural properties enhance the neuron's sensitivity to change, a behaviorally important stimulus attribute, while preserving information about the stimulus location in the SGC-I population activity. Furthermore, the SGC-I neurons project with an interdigitating topography to the nucleus rotundus, where the direction of motion is computed. We showed that, for accurate direction-of-motion estimation, the interdigitating projection of tectal wide-field neurons requires a two-stage rotundal algorithm, where the second rotundal stage estimates the direction of motion from the change in the relative stimulus position represented in the first stage.

Synchronization from disordered driving forces in arrays of coupled oscillators

The effects of disorder in external forces on the dynamical behavior of coupled nonlinear oscillator networks are studied. When driven synchronously, i.e., all driving forces have the same phase, the networks display chaotic dynamics. We show that random phases in the driving forces result in regular, periodic network behavior. Intermediate phase disorder can produce network synchrony. Specifically, there is an optimal amount of phase disorder, which can induce the highest level of synchrony. These results demonstrate that the spatiotemporal structure of external influences can control chaos and lead to synchronization in nonlinear systems.

CorrXpression--identification of significant groups of genes and experiments by means of correspondence analysis and ratio analysis

CorrXpression is a stand-alone desktop application for the identification of significant genes within collections of microarrays. The software combines three methods in two steps of analysis: correspondence analysis (CA), ratio analysis and correlation analysis. The graphical interface of CorrXpression visualizes the result of the CA with a biplot and the expression of selected genes in dependency of the experiments as bar diagrams. The CA-plot is an excellent tool for visualization and evaluation of data and results of ratio analysis and correlation analysis. The input data are selected from a database or from appropriate ASCII files.

Motion contrast classification is a linearly nonseparable problem

Sensitivity to image motion contrast, that is, the relative motion between different parts of the visual field, is a common and computationally important property of many neurons in the visual pathways of vertebrates. Here we illustrate that, as a classification problem, motion contrast detection is linearly nonseparable. In order to do so, we prove a theorem stating a sufficient condition for linear nonseparability. We argue that nonlinear combinations of local measurements of velocity at different locations and times are needed in order to solve the motion contrast problem.

Computing relative motion with complex cells

Contextual influences shape our perception of local visual stimuli. Relative-motion stimuli represent an important contextual influence, yet the mechanism subserving relative-motion computation remains largely unknown. In the present work, we investigated the responses of an established model for simple and complex cells to relative-motion stimuli. A straightforward mathematical analysis showed that relative-motion computation is inherent in the nonlinear transformation of the complex-cell model. Tuning to relative velocity is achieved by applying a temporal filter to the complex-cell response. The mathematical inference is supported by simulations that quantitatively reproduce measured complex-cell responses in both cat and monkey to a variety of relative-motion stimuli. Importantly, the posited mechanism for cortical computation of relative motion does not require an intermediate neural representation of local velocities and does not require lateral or feedback interactions within a network.

Motion repulsion arises from stimulus statistics when analyzed with a clustering algorithm

Motion repulsion is the perceived enlargement of the angle between the directions of motion of two transparently moving patterns. An explanation of this illusion has long been sought for in the neural circuitry of the brain. We show that motion repulsion already arises from the statistical properties of the motion transparency problem when analyzed with a clustering algorithm.

CD8+ T cells specific for a potential HLA-A*0201 epitope from Chlamydophila pneumoniae are present in the PBMCs from infected patients

Infection with the common pathogen Chlamydophila pneumoniae (Cpn, previously Chlamydia pneumoniae) has a high prevalence in patients suffering from arteriosclerosis and may trigger or contribute to heart disease. In mice, CD8-positive T cells are critical for the eradication of the infection and the development of immune memory against Cpn. Although several H2-class I epitopes have been described, no HLA-class I-associated peptides from Cpn are known. In order to define HLA-A*0201 epitopes from Cpn, we focused on the bacterial heat shock proteins (HSP) 60 and 70 which are known to be recognized by the immune system. Using epitope prediction, peptide binding studies and peptide-specific CTLs from HLA-A2 transgenic mice, we could define a potential HSP-70-derived epitope. The study of PBMCs from Cpn-infected individuals using fluorescent MHC tetramers revealed that some patients have CD8(+) T cells capable of recognizing the Cpn HSP-70 HLA-A*0201 epitope. Our studies pave the way to the immunomonitoring of the anti-Cpn CTL immune response present in patients suffering from different diseases potentially linked to Cpn or anti-Cpn immunity.

Assessment of protein spot components applying correspondence analysis for peptide mass fingerprint data

Proteins separated by two-dimensional gel electrophoresis (2-DE) may be distributed over several spots. Otherwise, one spot may contain more than one component. The same protein occurring in several spots supposedly represents differently modified protein species that might be of biological relevance. Identification of spots with peptide mass fingerprinting and database searching leads only to the detection of the major spot components. If a spot also contains additional minor protein components, quantitation of spots with protein staining techniques or antibody detection becomes misleading. In order to find spots containing minor components we applied correspondence analysis, a multivariate data exploration method, to peptide mass fingerprint data. Correspondence analysis using peak lists revealed groups of spots containing the same protein with their characteristic mass-to-charge ratio (m/z) values. In order to detect different protein spot components an interactive threshold setting and removal of m/z values with subsequent recalculation of the correspondence analysis using our software tool CorrAn are performed. The usefulness of this methodical approach was shown by a data set of peptide mass fingerprints of 284 spots of Helicobacter pylori 26695 separated by 2-DE.

Gene expression profiling: cell cycle deregulation and aneuploidy do not cause breast cancer formation in WAP-SVT/t transgenic animals

Microarray studies revealed that as a first hit the SV40 T/t antigen causes deregulation of 462 genes in mammary gland cells (ME cells) of WAP-SVT/t transgenic animals. The majority of deregulated genes are cell proliferation specific and Rb-E2F dependent, causing ME cell proliferation and gland hyperplasia but not breast cancer formation. In the breast tumor cells a further 207 genes are differentially expressed, most of them belonging to the cell communication category. In tissue culture breast tumor cells frequently switch off WAP-SVT/t transgene expression and regain the morphology and growth characteristics of normal ME cells, although the tumor-revertant cells are aneuploid and only 114 genes regain the expression level of normal ME cells. The profile of retransformants shows that only 38 deregulated genes are tumor-specific, and that none of them is considered to be a typical breast cancer gene.

Motion-contrast computation without directionally selective motion sensors

The detection of relative motion, i.e., motion contrast, has been reported for motion-sensitive neurons in several vertebrate systems, yet the mechanism underlying motion-contrast sensitivity remains unknown. An algorithm for computing motion contrast directly from the moving intensity distribution is proposed. In this algorithm, the time-dependent intensity distribution of the visual space is convolved with a periodic function. For coherent motion, the resulting convolution integral reduces to a traveling wave of fixed amplitude, while incoherent motion causes the amplitude to oscillate. The frequency of the amplitude oscillation provides a measure of motion contrast. The algorithm is successful in reproducing tuning curves derived from measurements of motion-contrast sensitivity in avian tectum and primate middle temporal area.