Dynamic Adaptive Computation: Tuning network states to task requirements

Neural circuits are able to perform computations under very diverse conditions and requirements. The required computations impose clear constraints on their fine-tuning: a rapid and maximally informative response to stimuli in general requires decorrelated baseline neural activity. Such network dynamics is known as asynchronous-irregular. In contrast, spatio-temporal integration of information requires maintenance and transfer of stimulus information over extended time periods. This can be realized at criticality, a phase transition where correlations, sensitivity and integration time diverge. Being able to flexibly switch, or even combine the above properties in a task-dependent manner would present a clear functional advantage. We propose that cortex operates in a "reverberating regime" because it is particularly favorable for ready adaptation of computational properties to context and task. This reverberating regime enables cortical networks to interpolate between the asynchronous-irregular and the critical state by small changes in effective synaptic strength or excitation-inhibition ratio. These changes directly adapt computational properties, including sensitivity, amplification, integration time and correlation length within the local network. We review recent converging evidence that cortex in vivo operates in the reverberating regime, and that various cortical areas have adapted their integration times to processing requirements. In addition, we propose that neuromodulation enables a fine-tuning of the network, so that local circuits can either decorrelate or integrate, and quench or maintain their input depending on task. We argue that this task-dependent tuning, which we call "dynamic adaptive computation", presents a central organization principle of cortical networks and discuss first experimental evidence.Comment: 6 pages + references, 2 figure

Neural Correlates of Spontaneous BOLD Fluctuations: A Simultaneous LFP-fMRI Investigation In The Non-human Primate

Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to explore functional connectivity (FC) between brain regions across neurological and psychiatric diseases. However, the neural basis of spontaneous low frequency blood-oxygen level dependent (BOLD) fluctuations is poorly understood. Here, we acquired rs-fMRI data in macaque monkeys together with simultaneous recordings of local field potentials (LFPs) in prefrontal cortex area 9/46d. We first evaluated the correlation between LFPs (1-100 Hz) and BOLD signals and found unique frequency power correlates of positive and negative FC. Anti-correlation of high and low power envelopes indicated that ongoing cross-frequency interactions are a neural correlate of FC. On the other hand, seed-based analysis of the BOLD signal from the vicinity of electrode revealed the same spatial topology when using the power envelopes of high frequency bands of LFPs in the regression analysis. Variations of the canonical hemodynamic response function (HRF) in distinct cortical areas were also investigated to find the optimal HRF that can best fit in model analysis and estimate the BOLD response. While we found the optimal HRF that yields the highest correlation, the HRF shape was consistent within subjects and between brain regions. Our results suggest that intrinsic connectivity networks may be specifically driven by unique LFP profiles and these profiles contribute differently to BOLD FC. This study provides insight into the neural correlates of spontaneous BOLD FC at rest

Multi-modal and multi-model interrogation of large-scale functional brain networks

Existing whole-brain models are generally tailored to the modelling of a particular data modality (e.g., fMRI or MEG/EEG). We propose that despite the differing aspects of neural activity each modality captures, they originate from shared network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct features of brain activity - captured across modalities - to the dynamics unfolding on a macroscopic structural connectome. To jointly predict connectivity, spatiotemporal and transient features of distinct signal modalities, we consider two large-scale models - the Stuart Landau and Wilson and Cowan models - which generate short-lived 40 Hz oscillations with varying levels of realism. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data. We show that both models can represent MEG functional connectivity (FC), functional connectivity dynamics (FCD) and generate MOMs to a comparable degree. This is achieved by adjusting the global coupling and mean conduction time delay and, in the WC model, through the inclusion of balance between excitation and inhibition. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD and MOMs, highlighting the importance of balanced dynamics for the emergence of spatiotemporal and transient patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.4 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns that extended beyond the constraints of the anatomical structure. Finally, we show that both models can generate MOMs with empirical-like properties such as size (number of brain regions engaging in a mode) and duration (continuous time interval during which a mode appears). Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours

Neurotensin and cortical arousal : an in vitro study

The information processing associated with wakefulness occurs during what is commonly referred to as a cortical desynchronized state. In contrast, deep sleep is accompanied by slow, synchronized electrical activity, perhaps best exemplified by the slow (<0.1Hz) oscillation (SO). The brain switches the cortex from the synchronized to desynchronized state through neuromodulation. Modulators are released in the cortex primarily from ascending systems, mostly signaling through biogenic amines and neuropeptides. One modulator associated with wakefulness through its effects on ascending arousal systems is the neuropeptide, neurotensin (NT). Immunohistochemical studies have revealed the presence of NT-immunoreactive fibers throughout the cortical mantle. Yet, the possibility of direct effects of NT on the cortex and cortical arousal has so far received little attention. Multi-unit activity recordings were performed on cortical slices spontaneously exhibiting the SO. Slices were prepared from rat to investigate the role of NT in modulating cortical global network activity. Single-cell and paired recordings of and between neuronal subgroups were performed to assess NT effects on the microcircuitry in slices from rats and mice. In this thesis a method was first developed to facilitate the investigation of cortical network activity in spontaneously oscillating rat slices (study I). Using this method, a robust SO could be recorded in combination with visually guided whole-cell recordings of single neurons. In study II, the effect of NT on spontaneous and evoked global network activity was assessed finding a depression of the spontaneous and evoked response in agreement with an arousal mediating mechanism. Through recordings of single neurons, we identified a rarely studied group of neurons residing within the cortical white matter as particularly sensitive targets for direct, excitatory actions of NT. To further investigate the role of particular neuronal subgroups transgenic mice were used in study III and IV. Neurotensin was found to excite all major classes of inhibitory interneurons, providing an explanation for the reduced global network activity. Collectively the data presented in this thesis strongly support an arousal mediating role for NT in the cortex, and identify salient components of an arousal-promoting cortical microcircuitry

Geometric and functional architecture of visceral sensory microcircuitry

Is microcircuit wiring designed deterministically or probabilistically? Does geometric architecture predict functional dynamics of a given neuronal microcircuit? These questions were addressed in the visceral sensory microcircuit of the caudal nucleus of the tractus solitarius (NTS), which is generally thought to be homogeneous rather than laminar in cytoarchitecture. Using in situ hybridization histochemistry and whole-cell patch clamp recordings followed by neuronal reconstruction with biocytin filling, anatomical and functional organization of NTS microcircuitry was quantified to determine associative relationships. Morphologic and chemical features of NTS neurons displayed different patterns of process arborization and sub-nuclear localization according to neuronal types: smaller cells featured presynaptic local axons and GABAergic cells were aggregated specifically within the ventral NTS. The results suggested both a laminar organization and a spatial heterogeneity of NTS microcircuit connectivity. Geometric analysis of pre- and postsynaptic axodendritic arbor overlap of reconstructed neurons (according to parent somal distance) confirmed a heterogeneity of microcircuit connectivity that could underlie differential functional dynamics along the dorsoventral axis. Functional dynamics in terms of spontaneous and evoked postsynaptic current patterns behaved in a strongly location-specific manner according to the geometric dimension, suggesting a spatial laminar segregation of neuronal populations: a dorsal group of high excitation and a ventral group of balanced excitation and inhibition. Recurrent polysynaptic activity was also noted in a subpopulation of the ventral group. Such geometric and functional laminar organization seems to provide the NTS microcircuit with both reverberation capability and a differentiated projection system for appropriate computation of visceral sensory information. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00429-010-0294-5) contains supplementary material, which is available to authorized users

Integration of continuous-time dynamics in a spiking neural network simulator

Contemporary modeling approaches to the dynamics of neural networks consider two main classes of models: biologically grounded spiking neurons and functionally inspired rate-based units. The unified simulation framework presented here supports the combination of the two for multi-scale modeling approaches, the quantitative validation of mean-field approaches by spiking network simulations, and an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most efficient spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. We further demonstrate the broad applicability of the framework by considering various examples from the literature ranging from random networks to neural field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation

Changes in the Striatal Network Connectivity in Parkinsonian and Dyskinetic Rodent Models

In Parkinson’s disease, there is a loss of dopaminergic innervation in the basal ganglia. The lack of dopamine produces substantial changes in neural plasticity and generates pathological activity patterns between basal ganglia nuclei. The treatment to relieve Parkinsonism is the administration of levodopa. However, the treatment produces dyskinesia. The question to answer is how the interactions between neurons change in the brain microcircuits under these pathological conditions. Calcium imaging is a way to record the activity of dozens of neurons simultaneously with single-cell resolution in brain slices from rodents. We studied these interactions in the striatum, since it is the nucleus of the basal ganglia that receives the major dopaminergic innervation. We used network analysis, where each active neuron is taken as a node and its coactivity with other neurons is taken as its functional connections. The network obtained represents the functional connectome of the striatal microcircuit, which can be characterized with a small set of parameters taken from graph theory. We then quantify the pathological changes at the functional histological scale and the differences between normal and pathological conditions