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Animal experiments report contradictory findings on the presence of a behavioural and neuronal anisotropy exhibited in vertical and horizontal capabilities of spatial orientation and navigation. We performed a pointing experiment in humans on the imagined 3-D direction of the location of various invisible goals that were distributed horizontally and vertically in a familiar multilevel hospital building. The 21 participants were employees who had worked for years in this building. The hypothesis was that comparison of the experimentally determined directions and the true directions would reveal systematic inaccuracy or dimensional anisotropy of the localizations. The study provides first evidence that the internal representation of a familiar multilevel building was distorted compared to the dimensions of the true building: vertically 215% taller and horizontally 51% shorter. This was not only demonstrated in the mathematical reconstruction of the mental model based on the analysis of the pointing experiments but also by the participants’ drawings of the front view and the ground plan of the building. Thus, in the mental model both planes were altered in different directions: compressed for the horizontal floor plane and stretched for the vertical column plane. This could be related to human anisotropic behavioural performance of horizontal and vertical navigation in such buildings.
Abstract: Fine-scale temporal organization of cortical activity in the gamma range (∼25–80Hz) may play a significant role in information processing, for example by neural grouping ('binding') and phase coding. Recent experimental studies have shown that the precise frequency of gamma oscillations varies with input drive (e.g. visual contrast) and that it can differ among nearby cortical locations. This has challenged theories assuming widespread gamma synchronization at a fixed common frequency. In the present study, we investigated which principles govern gamma synchronization in the presence of input-dependent frequency modulations and whether they are detrimental for meaningful input-dependent gamma-mediated temporal organization. To this aim, we constructed a biophysically realistic excitatory-inhibitory network able to express different oscillation frequencies at nearby spatial locations. Similarly to cortical networks, the model was topographically organized with spatially local connectivity and spatially-varying input drive. We analyzed gamma synchronization with respect to phase-locking, phase-relations and frequency differences, and quantified the stimulus-related information represented by gamma phase and frequency. By stepwise simplification of our models, we found that the gamma-mediated temporal organization could be reduced to basic synchronization principles of weakly coupled oscillators, where input drive determines the intrinsic (natural) frequency of oscillators. The gamma phase-locking, the precise phase relation and the emergent (measurable) frequencies were determined by two principal factors: the detuning (intrinsic frequency difference, i.e. local input difference) and the coupling strength. In addition to frequency coding, gamma phase contained complementary stimulus information. Crucially, the phase code reflected input differences, but not the absolute input level. This property of relative input-to-phase conversion, contrasting with latency codes or slower oscillation phase codes, may resolve conflicting experimental observations on gamma phase coding. Our modeling results offer clear testable experimental predictions. We conclude that input-dependency of gamma frequencies could be essential rather than detrimental for meaningful gamma-mediated temporal organization of cortical activity.
Author Summary: Almost 350 years ago the physicist and polymath Christiaan Huygens first observed the synchronization between two pendulum clocks attached to a common support. Since then synchronization has been recognized as a universal phenomenon from astronomy to biology. The phase-locking (synchrony) and the phase-relation between the two pendulums are determined by two principal forces: the synchronization force exerted over the connection and the tendency to desynchronize due to frequency (speed) differences. We propose that gamma synchronization (25–80Hz) among oscillating cortical neurons in the brain can be understood according to the same principles—like a field of many connected pendula—with the critical addition that input changes the frequency of gamma oscillations, as shown by recent experimental studies. It has been assumed that input-dependent changes in oscillation frequency are detrimental for a meaningful role of gamma synchronization in neural processing. To the contrary, our theoretical analysis demonstrates that because input can change the frequency of the oscillation, phase-locking and phase-relations among neurons relate systematically to input. By analogy, it is because a local push to a pendulum will change its frequency, that resulting changes in phase-locking and phase-relation among the pendula can be used to derive the external force applied.
In vivo long-term monitoring of circulating tumor cells fluctuation during medical interventions
(2015)
The goal of this research was to study the long-term impact of medical interventions on circulating tumor cell (CTC) dynamics. We have explored whether tumor compression, punch biopsy or tumor resection cause dissemination of CTCs into peripheral blood circulation using in vivo fluorescent flow cytometry and breast cancer-bearing mouse model inoculated with MDA-MB-231-Luc2-GFP cells in the mammary gland. Two weeks after tumor inoculation, three groups of mice were the subject of the following interventions: (1) tumor compression for 15 minutes using 400 g weight to approximate the pressure during mammography; (2) punch biopsy; or (3) surgery. The CTC dynamics were determined before, during and six weeks after these interventions. An additional group of tumor-bearing mice was used as control and did not receive an intervention. The CTC dynamics in all mice were monitored weekly for eight weeks after tumor inoculation. We determined that tumor compression did not significantly affect CTC dynamics, either during the procedure itself (P = 0.28), or during the 6-week follow-up. In the punch biopsy group, we observed a significant increase in CTC immediately after the biopsy (P = 0.02), and the rate stayed elevated up to six weeks after the procedure in comparison to the tumor control group. The CTCs in the group of mice that received a tumor resection disappeared immediately after the surgery (P = 0.03). However, CTC recurrence in small numbers was detected during six weeks after the surgery. In the future, to prevent these side effects of medical interventions, the defined dynamics of intervention-induced CTCs may be used as a basis for initiation of aggressive anti-CTC therapy at time-points of increasing CTC number.
Network graphs have become a popular tool to represent complex systems composed of many interacting subunits; especially in neuroscience, network graphs are increasingly used to represent and analyze functional interactions between multiple neural sources. Interactions are often reconstructed using pairwise bivariate analyses, overlooking the multivariate nature of interactions: it is neglected that investigating the effect of one source on a target necessitates to take all other sources as potential nuisance variables into account; also combinations of sources may act jointly on a given target. Bivariate analyses produce networks that may contain spurious interactions, which reduce the interpretability of the network and its graph metrics. A truly multivariate reconstruction, however, is computationally intractable because of the combinatorial explosion in the number of potential interactions. Thus, we have to resort to approximative methods to handle the intractability of multivariate interaction reconstruction, and thereby enable the use of networks in neuroscience. Here, we suggest such an approximative approach in the form of an algorithm that extends fast bivariate interaction reconstruction by identifying potentially spurious interactions post-hoc: the algorithm uses interaction delays reconstructed for directed bivariate interactions to tag potentially spurious edges on the basis of their timing signatures in the context of the surrounding network. Such tagged interactions may then be pruned, which produces a statistically conservative network approximation that is guaranteed to contain non-spurious interactions only. We describe the algorithm and present a reference implementation in MATLAB to test the algorithm’s performance on simulated networks as well as networks derived from magnetoencephalographic data. We discuss the algorithm in relation to other approximative multivariate methods and highlight suitable application scenarios. Our approach is a tractable and data-efficient way of reconstructing approximative networks of multivariate interactions. It is preferable if available data are limited or if fully multivariate approaches are computationally infeasible.
The bug Gyaclavator kohlsi Wappler, Guilbert, Wedmann et Labandeira, gen. et sp. nov., represents a new extinct genus of lace bugs (Insecta: Heteroptera: Tingidae) occurring in latest early Eocene deposits of the Green River Formation, from the southern Piceance Basin of Northwestern Colorado, in North America. Gyaclavator can be placed within the Tingidae with certainty, perhaps it is sistergroup to Cantacaderinae. If it belongs to Cantacaderinae, it is the first fossil record of this group for North America. Gyaclavator has unique, conspicuous antennae bearing a specialized, highly dilated distiflagellomere, likely important for intra- or intersex reproductive competition and attraction. This character parallels similar antennae in leaf-footed bugs (Coreidae), and probably is associated with a behavioral convergence as well.
At the sensor level many aspects, such as spectral power, functional and effective connectivity as well as relative-power-ratio ratio (RPR) and spatial resolution have been comprehensively investigated through both electroencephalography (EEG) and magnetoencephalography (MEG). Despite this, differences between both modalities have not yet been systematically studied by direct comparison. It remains an open question as to whether the integration of EEG and MEG data would improve the information obtained from the above mentioned parameters. Here, EEG (64-channel system) and MEG (275 sensor system) were recorded simultaneously in conditions with eyes open (EO) and eyes closed (EC) in 29 healthy adults. Spectral power, functional and effective connectivity, RPR, and spatial resolution were analyzed at five different frequency bands (delta, theta, alpha, beta and gamma). Networks of functional and effective connectivity were described using a spatial filter approach called the dynamic imaging of coherent sources (DICS) followed by the renormalized partial directed coherence (RPDC). Absolute mean power at the sensor level was significantly higher in EEG than in MEG data in both EO and EC conditions. At the source level, there was a trend towards a better performance of the combined EEG+MEG analysis compared with separate EEG or MEG analyses for the source mean power, functional correlation, effective connectivity for both EO and EC. The network of coherent sources and the spatial resolution were similar for both the EEG and MEG data if they were analyzed separately. Results indicate that the combined approach has several advantages over the separate analyses of both EEG and MEG. Moreover, by a direct comparison of EEG and MEG, EEG was characterized by significantly higher values in all measured parameters in both sensor and source level. All the above conclusions are specific to the resting state task and the specific analysis used in this study to have general conclusion multi-center studies would be helpful.
Adult neurogenesis is frequently studied in the mouse hippocampus. We examined the morphological development of adult-born, immature granule cells in the suprapyramidal blade of the septal dentate gyrus over the period of 7–77 days after mitosis with BrdU-labeling in 6-weeks-old male Thy1-GFP mice. As Thy1-GFP expression was restricted to maturated granule cells, it was combined with doublecortin-immunolabeling of immature granule cells. We developed a novel classification system that is easily applicable and enables objective and direct categorization of newborn granule cells based on the degree of dendritic development in relation to the layer specificity of the dentate gyrus. The structural development of adult-generated granule cells was correlated with age, albeit with notable differences in the time course of development between individual cells. In addition, the size of the nucleus, immunolabeled with the granule cell specific marker Prospero-related homeobox 1 gene, was a stable indicator of the degree of a cell's structural maturation and could be used as a straightforward parameter of granule cell development. Therefore, further studies could employ our doublecortin-staging system and nuclear size measurement to perform investigations of morphological development in combination with functional studies of adult-born granule cells. Furthermore, the Thy1-GFP transgenic mouse model can be used as an additional investigation tool because the reporter gene labels granule cells that are 4 weeks or older, while very young cells could be visualized through the immature marker doublecortin. This will enable comparison studies regarding the structure and function between young immature and older matured granule cells.
Background: From 2008–2013, the European indication for panitumumab required that patients' tumor KRAS exon 2 mutation status was known prior to starting treatment. To evaluate physician awareness of panitumumab prescribing information and how physicians prescribe panitumumab in patients with metastatic colorectal cancer (mCRC), two European multi-country, cross-sectional, observational studies were initiated in 2012: a physician survey and a medical records review. The first two out of three planned rounds for each study are reported.
Methods: The primary objective in the physician survey was to estimate the prevalence of KRAS testing, and in the medical records review, it was to evaluate the effect of test results on patterns of panitumumab use. The medical records review study also included a pathologists' survey.
Results: In the physician survey, nearly all oncologists (299/301) were aware of the correct panitumumab indication and the need to test patients' tumor KRAS status before treatment with panitumumab. Nearly all oncologists (283/301) had in the past 6 months of clinical practice administered panitumumab correctly to mCRC patients with wild-type KRAS status. In the medical records review, 97.5% of participating oncologists (77/79) conducted a KRAS test for all of their patients prior to prescribing panitumumab. Four patients (1.3%) did not have tumor KRAS mutation status tested prior to starting panitumumab treatment. Approximately one-quarter of patients (85/306) were treated with panitumumab and concurrent oxaliplatin-containing chemotherapy; of these, 83/85 had confirmed wild-type KRAS status prior to starting panitumumab treatment. All 56 referred laboratories that participated used a Conformité Européenne-marked or otherwise validated KRAS detection method, and nearly all (55/56) participated in a quality assurance scheme.
Conclusions: There was a high level of knowledge amongst oncologists around panitumumab prescribing information and the need to test and confirm patients' tumors as being wild-type KRAS prior to treatment with panitumumab, with or without concurrent oxaliplatin-containing therapy.
Synaptic vesicles (SVs) undergo a cycle of biogenesis and membrane fusion to release transmitter, followed by recycling. How exocytosis and endocytosis are coupled is intensively investigated. We describe an all-optical method for identification of neurotransmission genes that can directly distinguish SV recycling factors in C. elegans, by motoneuron photostimulation and muscular RCaMP Ca2+ imaging. We verified our approach on mutants affecting synaptic transmission. Mutation of genes affecting SV recycling (unc-26 synaptojanin, unc-41 stonin, unc-57 endophilin, itsn-1 intersectin, snt-1 synaptotagmin) showed a distinct ‘signature’ of muscle Ca2+ dynamics, induced by cholinergic motoneuron photostimulation, i.e. faster rise, and earlier decrease of the signal, reflecting increased synaptic fatigue during ongoing photostimulation. To facilitate high throughput, we measured (3–5 times) ~1000 nematodes for each gene. We explored if this method enables RNAi screening for SV recycling genes. Previous screens for synaptic function genes, based on behavioral or pharmacological assays, allowed no distinction of the stage of the SV cycle in which a protein might act. We generated a strain enabling RNAi specifically only in cholinergic neurons, thus resulting in healthier animals and avoiding lethal phenotypes resulting from knockdown elsewhere. RNAi of control genes resulted in Ca2+ measurements that were consistent with results obtained in the respective genomic mutants, albeit to a weaker extent in most cases, and could further be confirmed by opto-electrophysiological measurements for mutants of some of the genes, including synaptojanin. We screened 95 genes that were previously implicated in cholinergic transmission, and several controls. We identified genes that clustered together with known SV recycling genes, exhibiting a similar signature of their Ca2+ dynamics. Five of these genes (C27B7.7, erp-1, inx-8, inx-10, spp-10) were further assessed in respective genomic mutants; however, while all showed electrophysiological phenotypes indicative of reduced cholinergic transmission, no obvious SV recycling phenotypes could be uncovered for these genes.
An individual's choices are shaped by its experience, a fundamental property of behavior important to understanding complex processes. Learning and memory are observed across many taxa and can drive behaviors, including foraging behavior. To explore the conditions under which memory provides an advantage, we present a continuous-space, continuous-time model of animal movement that incorporates learning and memory. Using simulation models, we evaluate the benefit memory provides across several types of landscapes with variable-quality resources and compare the memory model within a nested hierarchy of simpler models (behavioral switching and random walk). We find that memory almost always leads to improved foraging success, but that this effect is most marked in landscapes containing sparse, contiguous patches of high-value resources that regenerate relatively fast and are located in an otherwise devoid landscape. In these cases, there is a large payoff for finding a resource patch, due to size, value, or locational difficulty. While memory-informed search is difficult to differentiate from other factors using solely movement data, our results suggest that disproportionate spatial use of higher value areas, higher consumption rates, and consumption variability all point to memory influencing the movement direction of animals in certain ecosystems.
Magnetic resonance imaging (MRI) provides non-invasive, repetitive measures in the same individual, allowing the study of a physio-pathological event over time. In this study, we tested the performance of 7 Tesla multi-parametric MRI to monitor the dynamic changes of mouse skeletal muscle injury and regeneration upon acute ischemia induced by femoral artery dissection. T2-mapping (T2 relaxation time), diffusion-tensor imaging (Fractional Anisotropy) and perfusion by Dynamic Contrast-Enhanced MRI (K-trans) were measured and imaging results were correlated with histological morphometric analysis in both Gastrocnemius and Tibialis anterior muscles. We found that tissue damage positively correlated with T2-relaxation time, while myofiber regeneration and capillary density positively correlated with Fractional Anisotropy. Interestingly, K-trans positively correlated with capillary density. Accordingly, repeated MRI measurements between day 1 and day 28 after surgery in ischemic muscles showed that: 1) T2-relaxation time rapidly increased upon ischemia and then gradually declined, returning almost to basal level in the last phases of the regeneration process; 2) Fractional Anisotropy dropped upon ischemic damage induction and then recovered along with muscle regeneration and neoangiogenesis; 3) K-trans reached a minimum upon ischemia, then progressively recovered. Overall, Gastrocnemius and Tibialis anterior muscles displayed similar patterns of MRI parameters dynamic, with more marked responses and less variability in Tibialis anterior. We conclude that MRI provides quantitative information about both tissue damage after ischemia and the subsequent vascular and muscle regeneration, accounting for the differences between subjects and, within the same individual, between different muscles.
Background: Chronic ethanol (EtOH) abuse worsens pathophysiological derangements after hemorrhagic shock and resuscitation (H/R) that induce hepatic injury and strong inflammatory changes via JNK and NF-κB activation. Inhibiting JNK with a cell-penetrating, protease-resistant peptide D-JNKI-1 after H/R in mice with healthy livers ameliorated these effects. Here, we studied if JNK inhibition by D-JNKI-1 in chronically EtOH-fed mice after hemorrhagic shock prior to the onset of resuscitation also confers protection.
Methods: Male mice were fed a Lieber-DeCarli diet containing EtOH or an isocaloric control (ctrl) diet for 4 weeks. Animals were hemorrhaged for 90 min (32 ± 2 mm Hg) and randomly received either D-JNKI-1 (11 mg/kg, intraperitoneally, i. p.) or sterile saline as vehicle (veh) immediately before the onset of resuscitation. Sham animals underwent surgical procedures without H/R and were either D-JNKI-1 or veh treated. Two hours after resuscitation, blood samples and liver tissue were harvested.
Results: H/R induced hepatic injury with increased systemic interleukin (IL)-6 levels, and enhanced local gene expression of NF-κB-controlled genes such as intercellular adhesion molecule (ICAM)-1 and matrix metallopeptidase (MMP)9. c-Jun and NF-κB phosphorylation were increased after H/R. These effects were further increased in EtOH-fed mice after H/R. D-JNKI-1 application inhibited the proinflammatory changes and reduced significantly hepatic injury after H/R in ctrl-fed mice. Moreover, D-JNKI-1 reduces in ctrl-fed mice the H/R-induced c-Jun and NF-κB phosphorylation. However, in chronically EtOH-fed mice, JNK inhibition did not prevent the H/R-induced hepatic damage and proinflammatory changes nor c-Jun and NF-κB phosphorylation after H/R.
Conclusions: These results indicate, that JNK inhibition is protective only in not pre-harmed liver after H/R. In contrast, the pronounced H/R-induced liver damage in mice being chronically fed with ethanol cannot be prevented by JNK inhibition after H/R and seems to be under the control of NF-κB.
A fundamental question in evolutionary genetics concerns the extent to which adaptive phenotypic convergence is attributable to convergent or parallel changes at the molecular sequence level. Here we report a comparative analysis of hemoglobin (Hb) function in eight phylogenetically replicated pairs of high- and low-altitude waterfowl taxa to test for convergence in the oxygenation properties of Hb, and to assess the extent to which convergence in biochemical phenotype is attributable to repeated amino acid replacements. Functional experiments on native Hb variants and protein engineering experiments based on site-directed mutagenesis revealed the phenotypic effects of specific amino acid replacements that were responsible for convergent increases in Hb-O2 affinity in multiple high-altitude taxa. In six of the eight taxon pairs, high-altitude taxa evolved derived increases in Hb-O2 affinity that were caused by a combination of unique replacements, parallel replacements (involving identical-by-state variants with independent mutational origins in different lineages), and collateral replacements (involving shared, identical-by-descent variants derived via introgressive hybridization). In genome scans of nucleotide differentiation involving high- and low-altitude populations of three separate species, function-altering amino acid polymorphisms in the globin genes emerged as highly significant outliers, providing independent evidence for adaptive divergence in Hb function. The experimental results demonstrate that convergent changes in protein function can occur through multiple historical paths, and can involve multiple possible mutations. Most cases of convergence in Hb function did not involve parallel substitutions and most parallel substitutions did not affect Hb-O2 affinity, indicating that the repeatability of phenotypic evolution does not require parallelism at the molecular level.
We describe a new large-sized species of hypercarnivorous hyainailourine–Kerberos langebadreae gen. & sp. nov.–from the Bartonian (MP16) locality of Montespieu (Tarn, France). These specimens consist of a skull, two hemimandibles and several hind limb elements (fibula, astragalus, calcaneum, metatarsals, and phalanges). Size estimates suggest K. langebadreae may have weighed up to 140 kg, revealing this species as the largest carnivorous mammal in Europe at that time. Besides its very large size, K. langebadreae possesses an interesting combination of primitive and derived features. The distinctive skull morphology of K. langebadreae reflects a powerful bite force. The postcranial elements, which are rarely associated with hyainailourine specimens, indicate an animal capable of a plantigrade stance and adapted for terrestrial locomotion. We performed the first phylogenetic analysis of hyainailourines to determine the systematic position of K. langebadreae and to understand the evolution of the group that includes other massive carnivores. The analysis demonstrates that Hemipsalodon, a North American taxon, is a hyainailourine and is closely related to European Paroxyaena. Based on this analysis we hypothesize the biogeographic history of the Hyainailourinae. The group appeared in Africa with a first migration to Europe during the Bartonian that likely included the ancestors of Kerberos, Paroxyaena and Hemipsalodon, which further dispersed into North America at this time. We propose that the hyainailourines dispersed into Europe also during the Priabonian. These migrants have no ecological equivalent in Europe during these intervals and likely did not conflict with the endemic hyaenodont proviverrines. The discovery of K. langebadreae shows that large body size appears early in the evolution of hyainailourines. Surprisingly, the late Miocene Hyainailouros shares a more recent common ancestor with small-bodied hyainailourines (below 15 kg). Finally, our study supports a close relationship between the Hyainailourinae and Apterodontinae and we propose the new clade: Hyainailouridae.
During the last decade, Bayesian probability theory has emerged as a framework in cognitive science and neuroscience for describing perception, reasoning and learning of mammals. However, our understanding of how probabilistic computations could be organized in the brain, and how the observed connectivity structure of cortical microcircuits supports these calculations, is rudimentary at best. In this study, we investigate statistical inference and self-organized learning in a spatially extended spiking network model, that accommodates both local competitive and large-scale associative aspects of neural information processing, under a unified Bayesian account. Specifically, we show how the spiking dynamics of a recurrent network with lateral excitation and local inhibition in response to distributed spiking input, can be understood as sampling from a variational posterior distribution of a well-defined implicit probabilistic model. This interpretation further permits a rigorous analytical treatment of experience-dependent plasticity on the network level. Using machine learning theory, we derive update rules for neuron and synapse parameters which equate with Hebbian synaptic and homeostatic intrinsic plasticity rules in a neural implementation. In computer simulations, we demonstrate that the interplay of these plasticity rules leads to the emergence of probabilistic local experts that form distributed assemblies of similarly tuned cells communicating through lateral excitatory connections. The resulting sparse distributed spike code of a well-adapted network carries compressed information on salient input features combined with prior experience on correlations among them. Our theory predicts that the emergence of such efficient representations benefits from network architectures in which the range of local inhibition matches the spatial extent of pyramidal cells that share common afferent input.
Understanding factors that structure regional biodiversity is important for linking ecological and biogeographic processes. Our objective was to explore regional patterns in riverine benthic invertebrate assemblages in relation to their broad positioning along the river network and examine differences in composition, biodiversity (alpha and beta diversity), and environmental drivers. We up-scaled methods used to examine patterns in metacommunity structure (Elements of Metacommunity Structure framework) to examine faunal distribution patterns at the regional extent for 168 low-mountain stream invertebrate assemblages in central Germany. We then identified the most influential environmental factors using boosted regression trees. Faunal composition patterns were compartmentalised (Clementsian or quasi-Clementsian), with little difference from headwaters to large rivers, potentially reflecting the regional scale of the study, by crossing major catchment boundaries and incorporating different species pools. While idealised structures did not vary, environmental drivers of composition varied considerably between river sections and with alpha diversity. Prediction was substantially weaker, and the importance of space was greater, in large rivers compared to other sections suggesting a weakening in species sorting downstream. Further, there was a stronger transition in composition than for alpha diversity downstream. The stronger links with regional faunal composition than with richness further emphasises the importance of considering the alternative ways in which anthropogenic stressors are operating to affect biodiversity patterns. Our approach allowed bridging the gap between local (or metacommunity) and regional scales, providing key insights into drivers of regional biodiversity patterns.
Long-term potentiation (LTP) and long-term depression (LTD) are widely accepted to be synaptic mechanisms involved in learning and memory. It remains uncertain, however, which particular activity rules are utilized by hippocampal neurons to induce LTP and LTD in behaving animals. Recent experiments in the dentate gyrus of freely moving rats revealed an unexpected pattern of LTP and LTD from high-frequency perforant path stimulation. While 400 Hz theta-burst stimulation (400-TBS) and 400 Hz delta-burst stimulation (400-DBS) elicited substantial LTP of the tetanized medial path input and, concurrently, LTD of the non-tetanized lateral path input, 100 Hz theta-burst stimulation (100-TBS, a normally efficient LTP protocol for in vitro preparations) produced only weak LTP and concurrent LTD. Here we show in a biophysically realistic compartmental granule cell model that this pattern of results can be accounted for by a voltage-based spike-timing-dependent plasticity (STDP) rule combined with a relatively fast Bienenstock-Cooper-Munro (BCM)-like homeostatic metaplasticity rule, all on a background of ongoing spontaneous activity in the input fibers. Our results suggest that, at least for dentate granule cells, the interplay of STDP-BCM plasticity rules and ongoing pre- and postsynaptic background activity determines not only the degree of input-specific LTP elicited by various plasticity-inducing protocols, but also the degree of associated LTD in neighboring non-tetanized inputs, as generated by the ongoing constitutive activity at these synapses.
Even in the absence of sensory stimulation the brain is spontaneously active. This background “noise” seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN), which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network's spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network's behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural responses can be accounted for by a simple deterministic recurrent neural network which learns a predictive model of its sensory environment via a combination of generic neural plasticity mechanisms.