Pediatric brain tumors are highly associated with epileptic seizures. However, their epileptogenic mechanisms remain unclear. Here, we show that the oncogenic BRAF somatic mutation p.Val600Glu (V600E) in developing neurons underlies intrinsic epileptogenicity in ganglioglioma, one of the leading causes of intractable epilepsy. To do so, we developed a mouse model harboring the BRAF somatic mutation during early brain development to reflect the most frequent mutation, as well as the origin and timing thereof. Therein, the BRAF mutation arising in progenitor cells during brain development led to the acquisition of intrinsic epileptogenic properties in neuronal lineage cells, whereas tumorigenic properties were attributed to high proliferation of glial lineage cells. RNA sequencing analysis of patient brain tissues with the mutation revealed that BRAF-induced epileptogenesis is mediated by RE1-silencing transcription factor (REST), which is a regulator of ion channels and neurotransmitter receptors associated with epilepsy. Moreover, we found that seizures in mice were significantly alleviated by an FDA-approved BRAF inhibitor, vemurafenib, as well as various genetic inhibitions of Rest. Accordingly, this study provides direct evidence of a BRAF somatic mutation contributing to the intrinsic epileptogenicity in pediatric brain tumors and suggests that BRAF and REST could be treatment targets for intractable epilepsy.

Number sense, the ability to estimate numerosity, is observed in naïve animals, but how this cognitive function emerges in the brain remains unclear. Here, using an artificial deep neural network that models the ventral visual stream of the brain, we show that number-selective neurons can arise spontaneously, even in the complete absence of learning. We also show that the responses of these neurons can induce the abstract number sense, the ability to discriminate numerosity independent of low-level visual cues. We found number tuning in a randomly initialized network originating from a combination of monotonically decreasing and increasing neuronal activities, which emerges spontaneously from the statistical properties of bottom-up projections. We confirmed that the responses of these number-selective neurons show the single- and multineuron characteristics observed in the brain and enable the network to perform number comparison tasks. These findings provide insight into the origin of innate cognitive functions.

Face-selective neurons are observed in the primate visual pathway and are considered as the basis of face detection in the brain. However, it has been debated as to whether this neuronal selectivity can arise innately or whether it requires training from visual experience. Here, using a hierarchical deep neural network model of the ventral visual stream, we suggest a mechanism in which face-selectivity arises in the complete absence of training. We found that units selective to faces emerge robustly in randomly initialized networks and that these units reproduce many characteristics observed in monkeys. This innate selectivity also enables the untrained network to perform face-detection tasks. Intriguingly, we observed that units selective to various non-face objects can also arise innately in untrained networks. Our results imply that the random feedforward connections in early, untrained deep neural networks may be sufficient for initializing primitive visual selectivity.

Highlights d Determinants of columnar and salt-and-pepper patterns in visual cortex are sought d Retino-cortical mapping ratio solely predicts cortical patterns across species d Nyquist sampling model explains sharp parametric division of patterns d Controlled simulation of retino-cortical sampling ratio reproduces observed patterns

In higher mammals, orientation tuning of neurons is organized into a quasi-periodic pattern in the primary visual cortex. Our previous model studies suggested that the topography of cortical orientation maps may originate from moiré interference of ON and OFF retinal ganglion cell (RGC) mosaics, but did not account for how the consistent spatial period of maps could be achieved. Here we address this issue with two crucial findings on the development of RGC mosaics: first, homotypic local repulsion between RGCs can develop a long-range hexagonal periodicity. Second, heterotypic interaction restrains the alignment of ON and OFF mosaics, and generates a periodic interference pattern map with consistent spatial frequency. To validate our model, we quantitatively analyzed the RGC mosaics in cat data, and confirmed that the observed retinal mosaics showed evidence of heterotypic interactions, contrary to the previous view that ON and OFF mosaics are developed independently. Orientation map is one of the most studied functional maps in the brain, but it has remained unanswered how the consistent spatial periodicity of maps could be developed. In the current study, we address this issue with our developmental model for the retinal origin of orientation map. We showed that local repulsive interactions between retinal ganglion cells (RGCs) can develop a hexagonal periodicity in the RGC mosaics and restrict the alignment between ON and OFF mosaics, so that they generate a periodic pattern with consistent spatial frequency for both the RGC mosaics and the cortical orientation maps. Our results demonstrate that the organization of functional maps in visual cortex, including its structural consistency, may be constrained by a retinal blueprint.

Table of contentsA1 Functional advantages of cell-type heterogeneity in neural circuitsTatyana O. SharpeeA2 Mesoscopic modeling of propagating waves in visual cortexAlain DestexheA3 Dynamics and biomarkers of mental disordersMitsuo KawatoF1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneuronsVladislav Sekulić, Frances K. SkinnerF2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brainsDaniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán SomogyváriF3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks.Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir JosićO1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generatorsIrene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo VaronaO2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrainEunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun ChoiO3 Modeling auditory stream segregation, build-up and bistabilityJames Rankin, Pamela Osborn Popp, John RinzelO4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fieldsAlejandro Tabas, André Rupp, Emili Balaguer-BallesterO5 A simple model of retinal response to multi-electrode stimulationMatias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish MeffinO6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination taskVeronika Koren, Timm Lochmann, Valentin Dragoi, Klaus ObermayerO7 Input-location dependent gain modulation in cerebellar nucleus neuronsMaria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Nielsen, Volker SteuberO8 Analytic solution of cable energy function for cortical axons and dendritesHuiwen Ju, Jiao Yu, Michael L. Hines, Liang Chen, Yuguo YuO9 C. elegans interactome: interactive visualization of Caenorhabditis elegans worm neuronal networkJimin Kim, Will Leahy, Eli ShlizermanO10 Is the model any good? Objective criteria for computational neuroscience model selectionJustas Birgiolas, Richard C. Gerkin, Sharon M. CrookO11 Cooperation and competition of gamma oscillation mechanismsAtthaphon Viriyopase, Raoul-Martin Memmesheimer, Stan GielenO12 A discrete structure of the brain wavesYuri Dabaghian, Justin DeVito, Luca PerottiO13 Direction-specific silencing of the Drosophila gaze stabilization systemAnmo J. Kim, Lisa M. Fenk, Cheng Lyu, Gaby MaimonO14 What does the fruit fly think about values? A model of olfactory associative learningChang Zhao, Yves Widmer, Simon Sprecher,Walter SennO15 Effects of ionic diffusion on power spectra of local field potentials (LFP)Geir Halnes, Tuomo Mäki-Marttunen, Daniel Keller, Klas H. Pettersen,Ole A. Andreassen...

Correlated neural activities such as synchronizations can significantly alter the characteristics of spike transfer between neural layers. However, it is not clear how this synchronization-dependent spike transfer can be affected by the structure of convergent feedforward wiring. To address this question, we implemented computer simulations of model neural networks: a source and a target layer connected with different types of convergent wiring rules. In the Gaussian-Gaussian (GG) model, both the connection probability and the strength are given as Gaussian distribution as a function of spatial distance. In the Uniform-Constant (UC) and Uniform-Exponential (UE) models, the connection probability density is a uniform constant within a certain range, but the connection strength is set as a constant value or an exponentially decaying function, respectively. Then we examined how the spike transfer function is modulated under these conditions, while static or synchronized input patterns were introduced to simulate different levels of feedforward spike synchronization. We observed that the synchronization-dependent modulation of the transfer function appeared noticeably different for each convergence condition. The modulation of the spike transfer function was largest in the UC model, and smallest in the UE model. Our analysis showed that this difference was induced by the different spike weight distributions that was generated from convergent synapses in each model. Our results suggest that, the structure of the feedforward convergence is a crucial factor for correlation-dependent spike control, thus must be considered important to understand the mechanism of information transfer in the brain.Electronic supplementary materialThe online version of this article (10.1007/s10827-017-0657-5) contains supplementary material, which is available to authorized users.

11Face-selective neurons are observed in the primate visual pathway and are considered the basis of facial 12 recognition in the brain. However, it is debated whether this neuronal selectivity can arise spontaneously, 13 or requires training from visual experience. Here, we show that face-selective neurons arise 14 spontaneously in random feedforward networks in the absence of learning. Using biologically inspired 15 deep neural networks, we found that face-selective neurons arise under three different network conditions: 16 one trained using non-face natural images, one randomized after being trained, and one never trained. 17 We confirmed that spontaneously emerged face-selective neurons show the biological view-point-18 invariant characteristics observed in monkeys. Such neurons suddenly vanished when feedforward 19 weight variation declined to a certain level. Our results suggest that innate face-selectivity originates from 20 statistical variation of the feedforward projections in hierarchical neural networks. 22The ability to identify and recognize faces is a crucial function in visual-priority social animals such as 23 humans and other primates, and is thought to originate from neuronal tuning at a single or multi-neuronal 24 level. Neurons that selectively respond to faces (face-selective neurons) are observed to occur in the 25 inferior temporal cortex (IT) 1-6 , superior temporal sulcus (STS) 7-10 , and fusiform face area (FFA) [11][12][13][14][15] in 26 the primate brain ( Fig. 1A). Several contradictory observations on the origin of face-selective neurons in 27 infant animals have been reported, raising two different scenarios for the development of this intriguing 28 functional tuning. 29The first scenario is that visual experience develops face-selective neurons. A study using 30 functional magnetic resonance imaging (fMRI) to examine FFA in monkeys reported that the category of 31 selective neuronal activity observed, depends greatly on what a subject had experienced in its lifetime 16 . 32Another fMRI study of IT in monkeys reported that robust tuning of face-selective neurons is not observed 33 until one year after birth 5 and that face-selectivity relies on experience during the early infant years. 34Furthermore, it was reported that monkeys raised without face exposure did not develop normal face-35 selective domains 17 . These results suggest that face-selective neurons are developed from training with 36 visual experience.37However, the other view suggests that face-selectivity can innately arise without visual 38 experience 18-21 . It was observed that human infants behaviorally prefer to look face-like objects rather 39 than non-face ones [22][23][24] , implying that face-encoding units may already exist in infants. It was also 40 reported that adult humans with no visual experience have category-selective domains including face, in 41 the ventral visual cortex 19 . In addition, a recent fMRI study of infant animals reported that face-selective 42 neurons are observed with movie st...