A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data

Deducing the structure of neural circuits is one of the central problems of modern neuroscience. Recently-introduced calcium fluorescent imaging methods permit experimentalists to observe network activity in large populations of neurons, but these techniques provide only indirect observations of neural spike trains, with limited time resolution and signal quality. In this work we present a Bayesian approach for inferring neural circuitry given this type of imaging data. We model the network activity in terms of a collection of coupled hidden Markov chains, with each chain corresponding to a single neuron in the network and the coupling between the chains reflecting the network's connectivity matrix. We derive a Monte Carlo Expectation--Maximization algorithm for fitting the model parameters; to obtain the sufficient statistics in a computationally-efficient manner, we introduce a specialized blockwise-Gibbs algorithm for sampling from the joint activity of all observed neurons given the observed fluorescence data. We perform large-scale simulations of randomly connected neuronal networks with biophysically realistic parameters and find that the proposed methods can accurately infer the connectivity in these networks given reasonable experimental and computational constraints. In addition, the estimation accuracy may be improved significantly by incorporating prior knowledge about the sparseness of connectivity in the network, via standard L$_1$ penalization methods.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS303 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review

Fundamental activity constraints lead to specific interpretations of the connectome

The continuous integration of experimental data into coherent models of the brain is an increasing challenge of modern neuroscience. Such models provide a bridge between structure and activity, and identify the mechanisms giving rise to experimental observations. Nevertheless, structurally realistic network models of spiking neurons are necessarily underconstrained even if experimental data on brain connectivity are incorporated to the best of our knowledge. Guided by physiological observations, any model must therefore explore the parameter ranges within the uncertainty of the data. Based on simulation results alone, however, the mechanisms underlying stable and physiologically realistic activity often remain obscure. We here employ a mean-field reduction of the dynamics, which allows us to include activity constraints into the process of model construction. We shape the phase space of a multi-scale network model of the vision-related areas of macaque cortex by systematically refining its connectivity. Fundamental constraints on the activity, i.e., prohibiting quiescence and requiring global stability, prove sufficient to obtain realistic layer- and area-specific activity. Only small adaptations of the structure are required, showing that the network operates close to an instability. The procedure identifies components of the network critical to its collective dynamics and creates hypotheses for structural data and future experiments. The method can be applied to networks involving any neuron model with a known gain function.Comment: J. Schuecker and M. Schmidt contributed equally to this wor

細胞種選択的広域カルシウムイメージング法による多感覚刺激応答の研究

早大学位記番号:新7900早稲田大

Towards a comprehensive understanding of brain machinery by correlative microscopy.

Unraveling the complexity of brain structure and function is the biggest challenge of contemporary science. Due to their flexibility, optical techniques are the key to exploring this intricate network. However, a single imaging technique can reveal only a small part of this machinery due to its inherent multilevel organization. To obtain a more comprehensive view of brain functionality, complementary approaches have been combined. For instance, brain activity was monitored simultaneously on different spatiotemporal scales with functional magnetic resonance imaging and calcium imaging. On the other hand, dynamic information on the structural plasticity of neuronal networks has been contextualized in a wider framework combining two-photon and light-sheet microscopy. Finally, synaptic features have been revealed on previously in vivo imaged samples by correlative light-electron microscopy. Although these approaches have revealed important features of brain machinery, they provided small bridges between specific spatiotemporal scales, lacking an omni-comprehensive view. In this perspective, we briefly review the state of the art of correlative techniques and propose a wider methodological framework fusing multiple levels of brain investigation

A reaction-diffusion model of cholinergic retinal waves

Prior to receiving visual stimuli, spontaneous, correlated activity called retinal waves drives activity-dependent developmental programs. Early-stage waves mediated by acetylcholine (ACh) manifest as slow, spreading bursts of action potentials. They are believed to be initiated by the spontaneous firing of Starburst Amacrine Cells (SACs), whose dense, recurrent connectivity then propagates this activity laterally. Their extended inter-wave intervals and shifting wave boundaries are the result of the slow after-hyperpolarization of the SACs creating an evolving mosaic of recruitable and refractory cells, which can and cannot participate in waves, respectively. Recent evidence suggests that cholinergic waves may be modulated by the extracellular concentration of ACh. Here, we construct a simplified, biophysically consistent, reaction-diffusion model of cholinergic retinal waves capable of recapitulating wave dynamics observed in mice retina recordings. The dense, recurrent connectivity of SACs is modeled through local, excitatory coupling occurring via the volume release and diffusion of ACh. In contrast with previous, simulation-based models, we are able to use non-linear wave theory to connect wave features to underlying physiological parameters, making the model useful in determining appropriate pharmacological manipulations to experimentally produce waves of a prescribed spatiotemporal character. The model is used to determine how ACh mediated connectivity may modulate wave activity, and how the noise rate and sAHP refractory period contributes to critical wave size variability.Comment: 38 pages, 10 figure

ENCODING OF SALTATORY TACTILE VELOCITY IN THE ADULT OROFACIAL SOMATOSENSORY SYSTEM

Processing dynamic tactile inputs is a key function of somatosensory systems. Spatial velocity encoding mechanisms by the nervous system are important for skilled movement production and may play a role in recovery of motor function following neurological insult. Little is known about tactile velocity encoding in trigeminal networks associated with mechanosensory inputs to the face, or the consequences of movement. High resolution functional magnetic resonance imaging (fMRI) was used to investigate the neural substrates of velocity encoding in the human orofacial somatosensory system during unilateral saltatory pneumotactile inputs to perioral hairy skin in 20 healthy adults. A custom multichannel, scalable pneumotactile array consisting of 7 TAC-Cells was used to present 5 stimulus conditions: 5 cm/s, 25 cm/s, 65 cm/s, ALL-ON synchronous activation, and ALL-OFF. The spatial organization of cerebral and cerebellar blood oxygen level-dependent (BOLD) response as a function of stimulus velocity was analyzed using general linear modeling (GLM) of pooled group fMRI signal data. The sequential saltatory inputs to the lower face produced localized, predominantly contralateral BOLD responses in primary somatosensory (SI), secondary somatosensory (SII), primary motor (MI), supplemental motor area (SMA), posterior parietal cortices (PPC), and insula, whose spatial organization and intensity were highly dependent on velocity. Additionally, ipsilateral sensorimotor, insular and cerebellar BOLD responses were prominent during the lowest velocity (5 cm/s). Advisor: Steven M. Barlo