Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows (“explaining away”) and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons

Modeling convergent ON and OFF pathways in the early visual system

For understanding the computation and function of single neurons in sensory systems, one needs to investigate how sensory stimuli are related to a neuron’s response and which biological mechanisms underlie this relationship. Mathematical models of the stimulus–response relationship have proved very useful in approaching these issues in a systematic, quantitative way. A starting point for many such analyses has been provided by phenomenological “linear–nonlinear” (LN) models, which comprise a linear filter followed by a static nonlinear transformation. The linear filter is often associated with the neuron’s receptive field. However, the structure of the receptive field is generally a result of inputs from many presynaptic neurons, which may form parallel signal processing pathways. In the retina, for example, certain ganglion cells receive excitatory inputs from ON-type as well as OFF-type bipolar cells. Recent experiments have shown that the convergence of these pathways leads to intriguing response characteristics that cannot be captured by a single linear filter. One approach to adjust the LN model to the biological circuit structure is to use multiple parallel filters that capture ON and OFF bipolar inputs. Here, we review these new developments in modeling neuronal responses in the early visual system and provide details about one particular technique for obtaining the required sets of parallel filters from experimental data

Effective transvascular delivery of nanoparticles across the blood-brain tumor barrier into malignant glioma cells

<p>Abstract</p> <p>Background</p> <p>Effective transvascular delivery of nanoparticle-based chemotherapeutics across the blood-brain tumor barrier of malignant gliomas remains a challenge. This is due to our limited understanding of nanoparticle properties in relation to the physiologic size of pores within the blood-brain tumor barrier. Polyamidoamine dendrimers are particularly small multigenerational nanoparticles with uniform sizes within each generation. Dendrimer sizes increase by only 1 to 2 nm with each successive generation. Using functionalized polyamidoamine dendrimer generations 1 through 8, we investigated how nanoparticle size influences particle accumulation within malignant glioma cells.</p> <p>Methods</p> <p>Magnetic resonance and fluorescence imaging probes were conjugated to the dendrimer terminal amines. Functionalized dendrimers were administered intravenously to rodents with orthotopically grown malignant gliomas. Transvascular transport and accumulation of the nanoparticles in brain tumor tissue was measured <it>in vivo </it>with dynamic contrast-enhanced magnetic resonance imaging. Localization of the nanoparticles within glioma cells was confirmed <it>ex vivo </it>with fluorescence imaging.</p> <p>Results</p> <p>We found that the intravenously administered functionalized dendrimers less than approximately 11.7 to 11.9 nm in diameter were able to traverse pores of the blood-brain tumor barrier of RG-2 malignant gliomas, while larger ones could not. Of the permeable functionalized dendrimer generations, those that possessed long blood half-lives could accumulate within glioma cells.</p> <p>Conclusion</p> <p>The therapeutically relevant upper limit of blood-brain tumor barrier pore size is approximately 11.7 to 11.9 nm. Therefore, effective transvascular drug delivery into malignant glioma cells can be accomplished by using nanoparticles that are smaller than 11.7 to 11.9 nm in diameter and possess long blood half-lives.</p

Grid Cells, Place Cells, and Geodesic Generalization for Spatial Reinforcement Learning

Reinforcement learning (RL) provides an influential characterization of the brain's mechanisms for learning to make advantageous choices. An important problem, though, is how complex tasks can be represented in a way that enables efficient learning. We consider this problem through the lens of spatial navigation, examining how two of the brain's location representations—hippocampal place cells and entorhinal grid cells—are adapted to serve as basis functions for approximating value over space for RL. Although much previous work has focused on these systems' roles in combining upstream sensory cues to track location, revisiting these representations with a focus on how they support this downstream decision function offers complementary insights into their characteristics. Rather than localization, the key problem in learning is generalization between past and present situations, which may not match perfectly. Accordingly, although neural populations collectively offer a precise representation of position, our simulations of navigational tasks verify the suggestion that RL gains efficiency from the more diffuse tuning of individual neurons, which allows learning about rewards to generalize over longer distances given fewer training experiences. However, work on generalization in RL suggests the underlying representation should respect the environment's layout. In particular, although it is often assumed that neurons track location in Euclidean coordinates (that a place cell's activity declines “as the crow flies” away from its peak), the relevant metric for value is geodesic: the distance along a path, around any obstacles. We formalize this intuition and present simulations showing how Euclidean, but not geodesic, representations can interfere with RL by generalizing inappropriately across barriers. Our proposal that place and grid responses should be modulated by geodesic distances suggests novel predictions about how obstacles should affect spatial firing fields, which provides a new viewpoint on data concerning both spatial codes

A Reaction-Diffusion Model to Capture Disparity Selectivity in Primary Visual Cortex

Decades of experimental studies are available on disparity selective cells in visual cortex of macaque and cat. Recently, local disparity map for iso-orientation sites for near-vertical edge preference is reported in area 18 of cat visual cortex. No experiment is yet reported on complete disparity map in V1. Disparity map for layer IV in V1 can provide insight into how disparity selective complex cell receptive field is organized from simple cell subunits. Though substantial amounts of experimental data on disparity selective cells is available, no model on receptive field development of such cells or disparity map development exists in literature. We model disparity selectivity in layer IV of cat V1 using a reaction-diffusion two-eye paradigm. In this model, the wiring between LGN and cortical layer IV is determined by resource an LGN cell has for supporting connections to cortical cells and competition for target space in layer IV. While competing for target space, the same type of LGN cells, irrespective of whether it belongs to left-eye-specific or right-eye-specific LGN layer, cooperate with each other while trying to push off the other type. Our model captures realistic 2D disparity selective simple cell receptive fields, their response properties and disparity map along with orientation and ocular dominance maps. There is lack of correlation between ocular dominance and disparity selectivity at the cell population level. At the map level, disparity selectivity topography is not random but weakly clustered for similar preferred disparities. This is similar to the experimental result reported for macaque. The details of weakly clustered disparity selectivity map in V1 indicate two types of complex cell receptive field organization

Dual coding with STDP in a spiking recurrent neural network model of the hippocampus.

The firing rate of single neurons in the mammalian hippocampus has been demonstrated to encode for a range of spatial and non-spatial stimuli. It has also been demonstrated that phase of firing, with respect to the theta oscillation that dominates the hippocampal EEG during stereotype learning behaviour, correlates with an animal's spatial location. These findings have led to the hypothesis that the hippocampus operates using a dual (rate and temporal) coding system. To investigate the phenomenon of dual coding in the hippocampus, we examine a spiking recurrent network model with theta coded neural dynamics and an STDP rule that mediates rate-coded Hebbian learning when pre- and post-synaptic firing is stochastic. We demonstrate that this plasticity rule can generate both symmetric and asymmetric connections between neurons that fire at concurrent or successive theta phase, respectively, and subsequently produce both pattern completion and sequence prediction from partial cues. This unifies previously disparate auto- and hetero-associative network models of hippocampal function and provides them with a firmer basis in modern neurobiology. Furthermore, the encoding and reactivation of activity in mutually exciting Hebbian cell assemblies demonstrated here is believed to represent a fundamental mechanism of cognitive processing in the brain

Perceived managerial and leadership effectiveness in a Korean context: An indigenous qualitative study

Multinational corporations (MNCs) across the world have sent an increasing number of managers abroad to leverage unprecedented opportunities in the era of globalization. However, their failure rate has been above 33% for decades, resulting in substantial costs (Puck, Kittler, & Wright, 2008). One of the primary reasons for this failure is a lack of understanding of the national and organizational cultures within the host countries (Festing & Maletzky, 2011). For example, while a number of MNCs have entered the Korean market, several such as Yahoo, Motorola, and Walmart have failed and withdrawn due to the companies’ lack of adjustment to the Korean cultural context (Choe, 2006; Woo, 2013). In spite of the significance of culturally embedded practices, most researchers who have explored management and leadership in Asian countries, whether they were Western or indigenous researchers, have implemented studies using extant Western management and leadership theories derived within the Western cultural context (Leung, 2007; Tsui, 2006). Numerous scholars have claimed that this could be problematic because the findings of such studies may not be applicable to non-Western countries (Li, 2012; Liden & Antonakis, 2009), and may fail to provide insights and understanding of novel contexts or to reveal indigenous aspects of management and leadership (Tsui, 2007). Consequently, there have been increasing calls for indigenous management and leadership research within Asian countries (see Li et al., 2014; Lyles, 2009; Tsui, 2004; Wolfgramm, Spiller, & Voyageur, 2014). Over the past 30 years, managerial effectiveness and leadership effectiveness have been substantially neglected areas of management research (Noordegraaf & Stewart, 2000; Yukl, Gordon, & Taber, 2002). In addition, there has been little agreement on what specific behaviors distinguish effective managers from ineffective ones. Furthermore, more research is needed to examine the managerial and leadership behaviors that are critical for shaping the performance of individuals, groups and organizations (see Borman & Brush, 1993; Cammock, Nilakant & Dakin, 1995; Mumford, 2011; Noordegraaf & Stewart, 2000; Yukl et al., 2002). While most of the research related to managerial and leadership effectiveness has been conducted in the U.S., the few notable non-U.S. studies include that of Cammock et al. (1995) in New Zealand who developed a behavioral lay model of managerial effectiveness using the repertory grid technique. Another notable exception is the cumulative series of perceived managerial and leadership effectiveness studies conducted by Hamlin with various indigenous co-researchers in Western and non-Western countries (see Hamlin & Patel, 2012; Ruiz, Wang, & Hamlin, 2013) using Flanagan’s (1954) critical incident technique (CIT)

Effects of abiotic stress on gene transcription in European beech: ozone affects ethylene biosynthesis in saplings of Fagus sylvatica L.

The influence of ozone (150-190 nl L-1; 8h/d) on transcription levels of genes involved in the biosynthesis of the stress hormone ethylene, and its precursor 1-aminocyclopropane-1-carboxylate (ACC), was analysed in leaves of European beech saplings. Ozone-induced leaf lesions appeared 7 weeks after onset of ozone exposure. Cell lesion formation was preceded by persistent increases in ethylene emission, in the level of its malonylated precursor ACC, and in the transcript levels of specific ACC synthase 1 (ACS1), ACS2, ACC oxidase 1 (ACO1), and ACO2. Our results demonstrate that mechanisms similar to those operating in herbaceous plants may determine beech saplings responses to ozone exposure