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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 541 records · Page 30Linked to original sources

bioRxiv2015

Response to Comment on ”Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons”

Networks of excitatory and inhibitory neurons form the basic computational units in the mammalian cortex. Within the dominant paradigm, neurons in such networks encode and process information by asynchronously emitting action potentials. In a recent publication, I argued that unstructured, sparsely connected networks of integrate-and-fire neurons display a transition between two qualitatively different types of asynchronous activity as the synaptic coupling is increased. A comment by Engelken et al (bioRxiv doi: 10.1101/017798) disputes this finding. Here I provide additional evidence for a transition between two qualitatively different types of asynchronous activity and address the criticism raised in the comment. The claims that the original paper is \"factually incorrect\" and \"conceptually misleading\" are unsubstantiated and inappropriate.

Neuroscience↗

bioRxiv2015

Estimating information flow through a memory system: the utility of meta-analytic methods for genetics

Critics of significance testing claim that this statistical framework promotes discrepancies by using arbitrary thresholds () to impose reject/accept dichotomies on continuous data, which is not reflective of the biological reality of quantitative phenotypes. Here we explore this idea and evaluate an alternative approach, demonstrating the potential for meta-analysis and related estimation methods to resolve discordance generated by the use of traditional significance tests. We selected a set of behavioral studies proposing differing models of the physiological basis of Drosophila olfactory memory and used systematic review and meta-analysis approaches to define the true role of lobular specialization within the brain. The mainstream view is that each of the three lobes of the Drosophila mushroom body play specialized roles in short-term aversive olfactory memory [1-5], but a number of studies have made divergent conclusions based on their discordant experimental findings [6-8]. Multivariate meta-regression models revealed that short-term memory lobular specialization is not in fact supported by the data, and identified the cellular extent of a transgenic driver as the major predictor of its effect on short-term memory. Our findings demonstrate that meta-analysis, meta-regression, hierarchical models and estimation methods in general can be successfully harnessed to identify knowledge gaps, synthesize divergent results, accommodate heterogeneous experimental design and quantify genetic mechanisms.

Neuroscience↗

bioRxiv2015

Striatal dynamics explain duration judgments

The striatum is an input structure of the basal ganglia implicated in several time-dependent functions including reinforcement learning, decision making, and interval timing. To determine whether striatal ensembles drive subjects' judgments of duration, we manipulated and recorded from striatal neurons in rats performing a duration categorization psychophysical task. We found that the dynamics of striatal neurons predicted duration judgments, and that simultaneously recorded ensembles could judge duration as well as the animal. Furthermore, striatal neurons were necessary for duration judgments, as muscimol infusions produced a specific impairment in animals' duration sensitivity. Lastly, we show that time as encoded by striatal populations ran faster or slower when rats judged a duration as longer or shorter, respectively. These results demonstrate that the speed with which striatal population state changes supports the fundamental ability of animals to judge the passage of time.

Neuroscience↗

bioRxiv2015

Stimulus induced visual cortical networks are recapitulated by spontaneous local and inter-areal synchronization

Intrinsic covariation of brain activity has been studied across many levels of brain organization. Between visual areas, neuronal activity covaries primarily among portions with similar retinotopic selectivity. We hypothesized that spontaneous inter-areal co-activation is subserved by neuronal synchronization. We performed simultaneous high-density electrocorticographic recordings across several visual areas in awake monkeys to investigate spatial patterns of local and inter-areal synchronization. We show that stimulation-induced patterns of inter-areal co-activation were reactivated in the absence of stimulation. Reactivation occurred through both, inter-areal co-fluctuation of local activity and inter-areal phase synchronization. Furthermore, the trial-by-trial covariance of the induced responses recapitulated the pattern of inter-areal coupling observed during stimulation, i.e. the signal correlation. Reactivation-related synchronization showed distinct peaks in the theta, alpha and gamma frequency bands. During passive states, this rhythmic reactivation was augmented by specific patterns of arrhythmic correspondence. These results suggest that networks of intrinsic covariation observed at multiple levels and with several recording techniques are related to synchronization and that behavioral state may affect the structure of intrinsic dynamics.

Neuroscience↗

bioRxiv2015

Correspondence between resting state activity and brain gene expression

The relationship between functional brain activity and gene expression has not been fully explored in the human brain. Here, we identify significant correlations between gene expression in the brain and functional activity by comparing fractional Amplitude of Low Frequency Fluctuations (fALFF) from two independent human fMRI resting state datasets to regional cortical gene expression from a newly generated RNA-seq dataset and two additional gene expression datasets to obtain robust and reproducible correlations. We find significantly more genes correlated with fALFF than expected by chance, and identify specific genes correlated with the imaging signals in multiple expression datasets in the default mode network. Together, these data support a population-level relationship between regional steady state brain gene expression and resting state brain activity.\n\nHIGHLIGHTSO_LIGene expression is correlated with resting state activity in human brain.\nC_LIO_LISpecific genes are correlated with activity in the default mode network.\nC_LIO_LIActivity-correlated genes are enriched in neurons.\nC_LIO_LIRegionally-patterned gene expression is enriched for activity-correlated genes.\nC_LI

Neuroscience↗

bioRxiv2015

Decoding the Population Activity of Grid Cells for Spatial Localization and Goal-Directed Navigation

Mammalian grid cells fire whenever an animal crosses the points of an imaginary, hexagonal grid tessellating the environment. Here, we show how animals can localize themselves and navigate by reading-out a simple population vector of grid cell activity across multiple scales, even though this activity is intrinsically stochastic. This theory of dead reckoning explains why grid cells are organized into modules with equal lattice scale and orientation. Computing the homing vector is least error-prone when the ratio of successive grid scales is around 3/2. Silencing intermediate-scale modules should cause systematic errors in navigation, while knocking out the module at the smallest scale will only affect navigational precision. Read-out neurons should behave like goal-vector cells subject to nonlinear gain fields.

Neuroscience↗

bioRxiv2015

Mechanical morphogenesis and the development of neocortical organisation

The development and evolution of complex neocortical organisations is thought to result from the interaction of genetic and activity-dependent processes. Here we propose that a third type of process - mechanical morphogenesis - may also play an important role. We review recent theoretical and experimental results in non-linear physics showing how homogeneous growth can produce a rich variety of forms, in particular neocortical folding. The mechanical instabilities that produce these forms also induce heterogeneous patterns of stress at the scale of the organ. We review the evidence showing how these stresses can influence cell proliferation, migration and apoptosis, cell differentiation and shape, migration and axonal guidance, and could thus be able to influence regional neocortical identity and connectivity.

Neuroscience↗

bioRxiv2015

Simple Plans or Sophisticated Habits? State, Transition and Learning Interactions in the Two-step Task.

The recently developed two-step behavioural task promises to differentiate model-based or goal-directed from model-free or habitual reinforcement learning, while generating neurophysiologically-friendly decision datasets with parametric variation of decision variables. These desirable features have prompted widespread adoption of the task. However, the signatures of model-based control can be elusive - here, we investigate model-free learning methods that, depending on the analysis strategy, can masquerade as being model-based. We first show that unadorned model-free reinforcement learning can induce correlations between action values at the start of the trial and the subsequent trial events in such a way that analysis based on comparing successive trials can lead to erroneous conclusions. We also suggest a correction to the analysis that can alleviate this problem. We then consider model-free reinforcement learning strategies based on different state representations from those envisioned by the experimenter, which generate behaviour that appears model-based under these, and also more sophisticated, analyses. The existence of such strategies is of particular relevance to the design and interpretation of animal studies using the two-step task, as extended training and a sharp contrast between good and bad options are likely to promote their use.\n\nAuthor SummaryPlanning is the use of a predictive model of the consequences of actions to guide decision making. Planning plays a critical role in human behaviour but isolating its contribution is challenging because it is complemented by control systems which learn values of actions directly from the history of reinforcement, resulting in automatized mappings from states to actions often termed habits. Our study examined a recently developed behavioural task which uses choices in a multi-step decision tree to differentiate planning from value-based control. Using simulation, we demonstrated the existence of strategies which produce behaviour that resembles planning but in fact arises as a fixed mapping from particular sorts of states to actions. These results show that when a planning problem is faced repeatedly, sophisticated automatization strategies may be developed which identify that there are in fact a limited number of relevant states of the world each with an appropriate fixed or habitual response. Understanding such strategies is important for the design and interpretation of tasks which aim to isolate the contribution of planning to behaviour. Such strategies are also of independent scientific interest as they may contribute to automatization of behaviour in complex environments.

Neuroscience↗

bioRxiv2015

Life-span development of functional brain networks as assessed with minimum spanning tree analysis of resting state EEG.

The brain matures with large quantitative changes in anatomy and function. Graph analysis of EEG has previously revealed increased connectivity between distant brain areas and a decrease in randomness and increased integration in the brain network with concurrent increased modularity. Comparisons of graph parameters across age groups, however, may be confounded with network degree distributions. Here, we analyzed graph parameters from minimum spanning tree (MST) graphs. MST graphs are constructed by selecting only the strongest available connections avoiding loops resulting in a backbone graph that is thought reflect the major qualitative properties of connectivity while allowing a better comparison across age groups by avoiding the degree distribution confound. EEG was recorded in a large (N=1500) population-based sample aged 5 to 71 years. Connectivity was assessed using Phase Lag Index to reduce effects of volume conduction. As previously reported, connectivity increased from childhood to adolescence, continuing to grow nonsignificantly into adulthood decreasing only after ~30 years of age. Leaf number, degree, degree correlation, maximum centrality from the MST graph indicated a pattern of increased integration and decreased randomness from childhood into early adulthood. The observed development in network topology suggested that maturation at the neuronal level is aimed to increase connectivity as well as increase integration of the brain network. We confirm that brain network connectivity shows quantitative changes across the life span, and additionally demonstrate parallel qualitative changes in the connectivity pattern.

Neuroscience↗

bioRxiv2015

On the dimensionality of olfactory space

We recently presented an estimate of the number of mutually discriminable olfactory stimuli at one trillion (1). Subjects were asked to sniff mixtures of molecules with increasing component overlap selected from a panel of 128 isointense structurally and perceptually diverse monomolecular odorants (2). We considered stimulus pairs discriminable when the majority of subjects could significantly discriminate them at p=0.05, a conventional statistical threshold given our sample size. From these empirical data, we estimated that human discriminative capacity exceeds one trillion olfactory stimuli. Several readers have pointed out that such extrapolations are sensitive to underlying assumptions about the chosen significance threshold (3) and the dimensionality of olfaction (4). It is important to note that any exponential function will be sensitive in this way, and the goal of our model was not to identify the exact number of discriminable olfactory stimuli, or even the exact mathematical bounds, but an estimate of the order of magnitude of human discriminatory power across a population of human subjects. This was not clearly stated in our paper, and we agree that contradictory references to a \"lower limit\" and an \"upper bound\" were confusing. The central argument in (4) is that our estimation method assumes that the dimensionality of olfactory space is large. We agree that the high-dimensional nature of olfaction is indeed an assumption, and we should have stated this explicitly in our paper (1). Even if we follow this logic of the models presented in (4), purely geometrical calculations show that our results hold if the dimensionality of olfactory representations is D[≥]25. The dimensionality of olfaction is a question of interest to everyone, and while we do not know for sure, all available evidence suggests that olfaction is a high-dimensional sense. The olfactory system is wired to keep information from the ~400 odorant receptors strictly separated, so it is plausible that olfaction operates at least in 400-dimensional space. This is an important topic of discussion in olfaction, and we welcome continued debate of the dimensionality of smell and how this impacts human olfactory perception.

Neuroscience↗

bioRxiv2015

Endocannabinoids regulate mAChR-evoked theta rhythm IPSCs in hippocampus

Exogenous cannabinoids can affect behaviorally relevant neuronal oscillations, but there is little evidence that endogenous cannabinoids (endocannabinoids, eCBs) can affect them, although it is unknown whether eCBs were generated during oscillations investigated in previous studies. In rat hippocampal slices, muscarinic receptor (mAChR) agonists stimulate the occurrence of persistent, rhythmic inhibitory post-synaptic currents (IPSC) activity and mobilize eCBs. We tested the hypothesis that mAChR-induced IPSCs would be modulated by concomitantly produced eCBs. With ionotropic glutamate receptors inhibited, mAChR agonist application triggered eCB-sensitive IPSCs that were enhanced in amplitude and frequency when a cannabinoid receptor antagonist was also present. There was also a highly significant increase in IPSC spectral power in the theta-frequency range. The data show that eCBs released by mAChRs modulate rhythmic IPSCs, and suggest that eCBs are candidate regulators of neuronal oscillations associated with eCB production in vivo.\n\nSignificance statementEndocannabinoids (eCB) are ubiquitous and powerful modulators of neuronal activity, acting in the brain mainly via cannabinoid receptors (CB1Rs) on presynaptic nerve terminals, and inhibiting neurotransmitter release. Numerous neurotransmitters, including acetylcholine (ACh), stimulate eCB release. Behaviorally important neuronal oscillations can be suppressed by exogenous cannabinoids, suggesting that eCBs could regulate oscillations normally. Such regulation, if it occurred, would represent an important new dimension of eCB actions, but to date there is little evidence that eCBs do affect neuronal oscillations. Given the enrichment of eCBs and CB1Rs in brain regions whose behavioral roles depend on neuronal oscillations, including the hippocampus, it is important to understand whether, or how, the eCBs affect rhythms.

Neuroscience↗

bioRxiv2015

Fundamental Structure and Modulation of Neuronal Excitability: Synaptic Control of Coding, Resonance, and Network Synchronization

Neuronal encoding and collective network activity depend on the precise mechanism for generating action potentials. A dynamic switch in this mechanism could greatly expand the functional repertoire of neurons and circuits. Here we show that changes in neuronal biophysics control a complex, yet fundamental, sequence of dynamic transitions in neuronal excitability in which neurons switch from integrators to resonators near the spike threshold, from simple voltage dynamics to the bistable co-existence of action potentials and quiescence, and from continuous class-I to discontinuous class-II firing rate encoding. Using multiple bifurcation theory, we prove that this transition sequence is universal in conductance-based neurons. Using dynamic-clamp and pharmacology, we show experimentally that an increase in leak conductance or application of the inhibitory agonist GABA can dynamically induce these transitions in hippocampal and brainstem neurons. Our results imply that synaptic activity can flexibly control resonance, excitability and bistability of neurons. In simulated neuronal networks, we show that such synaptically induced transitions provide a mechanism for the dynamic gating of input signals and the targeted synchronization of sub-networks with a tunable number of neurons.\n\nSignificanceNeuronal function depends on the mechanism by which neurons transform synaptic input into action potentials (APs). It is unclear how neurons might control the AP mechanism to systematically modulate their responses to input signals or their collective behavior. Here we identify a complex, but model-independent, universal sequence of transitions in the dynamics of AP generation. Using patch-clamp recordings, we show that synaptic receptor activation can flexibly change the AP dynamics, confirming our theoretical predictions: non-resonant neurons develop a sub-threshold resonance, become bistable, and develop an abrupt jump in onset AP frequency. Our results explain how synapses or neuro-modulators could control neuronal excitability, influence information processing, and processing during collective network dynamics.

Neuroscience↗

bioRxiv2015

Nanoscale architecture of the axon initial segment reveals an organized and robust scaffold

The Axon Initial Segment [AIS], located within the first 30 m of the axon, has two essential roles in generating action potentials and maintaining axonal identity. AIS assembly depends on a {beta}IV-spectrin / ankyrin G scaffold, but its macromolecular arrangement is not well understood. Here we quantitatively determined the AIS nanoscale architecture using STo-chastic Optical Reconstruction Microscopy [STORM]. First we directly demonstrate that the 190-nm periodicity of the AIS submembrane lattice results from longitudinal, head-to-head {beta}IV-spectrin molecules connecting actin rings. Using multicolor 3D-STORM, we resolve the nanoscale organization of ankyrin G: its aminoterminus associates with the submembrane lattice, whereas the carboxyterminus radially extends (~32 nm on average) toward the cytosol. This AIS nano-architecture is highly resistant to cytoskeletal perturbations, advocating its role in structural stabilization. Our findings provide a comprehensive view of the AIS molecular architecture, and will help understanding the crucial physiological functions of this compartment.

Neuroscience↗

bioRxiv2015

Dendrites enable a robust mechanism for neuronal stimulus selectivity

Hearing, vision, touch -- underlying all of these senses is stimulus selectivity, a robust information processing operation in which cortical neurons respond more to some stimuli than to others. Previous models assume that these neurons receive the highest weighted input from an ensemble encoding the preferred stimulus, but dendrites enable other possibilities. Non-linear dendritic processing can produce stimulus selectivity based on the spatial distribution of synapses, even if the total preferred stimulus weight does not exceed that of non-preferred stimuli. Using a multi-subunit non-linear model, we demonstrate that selectivity can arise from the spatial distribution of synapses. Moreover, we show that this implementation of stimulus selectivity increases the neuron's robustness to synaptic and dendritic failure. Contrary to an equivalent linear model, our model can maintain stimulus selectivity even when 50% of synapses fail or when more than 50% of dendrites fail. We then use a Layer 2/3 biophysical neuron model to show that our implementation is consistent with recent experimental observations, of a mixture of selectivities in dendrites, that can differ from the somatic selectivity, and of hyperpolarization broadening somatic tuning without affecting dendritic tuning. Our model predicts that an initially non-selective neuron can become selective when depolarized. In addition to motivating new experiments, the model's increased robustness to synapses and dendrites loss provides a starting point for fault-resistant neuromorphic chip development.

Neuroscience↗

bioRxiv2015

Topology, cross-frequency, and same-frequency band interactions shape the generation of phase-amplitude coupling in a neural mass model of a cortical column

Phase-amplitude coupling (PAC), a type of cross-frequency coupling (CFC) where the phase of a low-frequency rhythm modulates the amplitude of a higher frequency, is becoming an important indicator of information transmission in the brain. However, the neurobiological mechanisms underlying its generation remain undetermined. A realistic, yet tractable computational model of the phenomenon is thus needed. Here we propose a neural mass model of a cortical column, comprising fourteen neuronal populations distributed across four layers (L2/3, L4, L5 and L6). The conditional transfer entropies (cTE) from the phases to the amplitudes of the generated oscillations are estimated by means of the conditional mutual information. This approach provides information regarding directionality by distinguishing PAC from APC (amplitude-phase coupling), i.e. the information transfer from amplitudes to phases, and can be used to estimate other types of CFC such as amplitude-amplitude coupling (AAC) and phase-phase coupling (PPC). While experiments often only focus on one or two PAC combinations (e.g., theta-gamma or alpha-gamma), we found that a cortical column can simultaneously generate almost all possible PAC combinations, depending on connectivity parameters, time constants, and external inputs. We found that the strength of PAC between two populations was strongly correlated with the strength of the effective connections between them and, on average, did not depend upon the presence or absence of a direct (anatomical) connection. When considering a cortical column circuit as a complex network, we found that neuronal populations making indirect PAC connections had, on average, higher local clustering coefficient, efficiency, and betweenness centrality than populations making direct connections and populations not involved in PAC connections. This suggests that their interactions were more efficient when transmitting information. Since more than 60% of the obtained interactions represented indirect connections, our results highlight the importance of the topology of cortical circuits for the generation on of the PAC phenomenon. Finally, our results demonstrated that indirect PAC interactions can be explained by a cascade of direct CFC and same-frequency band interactions, suggesting that PAC analysis of experimental data should be accompanied by the estimation of other types of frequency interactions for an integrative understanding of the phenomenon.

Neuroscience↗

bioRxiv2015

Formation and maintenance of robust long-term information storage in the presence of synaptic turnover

A long-standing problem is how memories can be stored for very long times despite the volatility of the underlying neural substrate, most notably the high turnover of dendritic spines and synapses. To address this problem, here we are using a generic and simple probabilistic model for the creation and removal of synapses. We show that information can be stored for several months when utilizing the intrinsic dynamics of multi-synapse connections. In such systems, single synapses can still show high turnover, which enables fast learning of new information, but this will not perturb prior stored information (slow forgetting), which is represented by the compound state of the connections. The model matches the time course of recent experimental spine data during learning and memory in mice supporting the assumption of multi-synapse connections as the basis for long-term storage.\n\nAuthor SummaryIt is widely believed that information is stored in the connectivity, i.e. the synapses of neural networks. Yet, the morphological correlates of excitatory synapses, the dendritic spines, have been found to undergo a remarkable turnover on daily basis. This poses the question, how information can be retained on such a variable substrate.\n\nIn this study, using connections with multiple synapses, we show that connections which follow the experimentally measured bimodal distribution in the number of synapses can store information orders of magnitude longer than the lifetime of a single synapse. This is a consequence of the underlying bistable collective dynamic of multiple synapses: Single synapses can appear and disappear without disturbing the memory as a whole.\n\nFurthermore, increasing or decreasing neural activity changes the distribution of the number of synapses of multi-synaptic connections such that only one of the peaks remains. This leads to a desirable property: information about these altered activities can be stored much faster than it is forgotten. Remarkably, the resulting model dynamics match recent experimental data investigating the long-term effect of learning on the dynamics of dendritic spines.

Neuroscience↗

bioRxiv2015

INSULIN GROWTH FACTOR I AND ITS RECEPTOR ARE ANTAGONISTIC MODULATORS OF GLUCOSE HANDLING BY ASTROCYTES

Reducing insulin-like growth factor I receptor (IGF-IR) levels or administration of IGF-I show beneficial effects in the brain. We now provide evidence to help resolve this paradox. The unliganded IGF-IR inhibits glucose uptake by astrocytes while its stimulation with IGF-I, in concert with insulin activation of the insulin receptor, produces the opposite effect. In vivo imaging showed that shRNA interference of brain IGF-IR increased glucose uptake by astrocytes while pharmacological blockade of IGF-IR reduced it. Brain 18FGlucose-PET of IGF-IR shRNA injected mice confirmed an inhibitory role of unliganded IGF-IR on glucose uptake, whereas glucose-dependent recovery of neuronal activity in brain slices was blunted by pharmacological blockade of IGF-IR. Mechanistically, we found that the unliganded IGF-IR retains glucose transporter 1 (GLUT1), the main glucose transporter in astrocytes, inside the cell while IGF-I, in cooperation with insulin, synergistically stimulates MAPK/PKD to promote association of IGF-IR with GLUT 1 via Rac1/GIPC1 and increases GLUT1 availability at the cell membrane. These findings identify IGF-I and its receptor as antagonistic modulators of brain glucose uptake.

Neuroscience↗

bioRxiv2015

Biomimetic and Non-biomimetic Extraction of Motor Control Signals Through Matched Filtering of Neural Population Dynamics

Brain-machine interfaces rely on extracting motor control signals from brain activity in real time to actuate external devices such as robotic limbs. Whereas biomimetic approaches to neural decoding use motor imagery/observation signals, non-biomimetic approaches assign an arbirary transformation that maps neural activity to motor control signals. In this work, we present a unified framework for the design of both biomimetic and non-biomimetic decoders based on kernel-based system identification. This framework seamlessly incorporates the neural population dynamics in the decoder design, is particularly robust even with short training data records, and results in decoders with small filter delays. The theory and results presented here provide a new formulation of optimal linear decoding, a formal method for designing non-biomimetic decoders, and a set of proposed metrics for assessing decoding performance from an online control perspective. The theoretical framework is also applicable to the design of closed-loop neural control schemes.

Neuroscience↗