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Current Opinion in Neurobiology

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https://www.readbyqxmd.com/read/28918313/learning-with-three-factors-modulating-hebbian-plasticity-with-errors
#1
REVIEW
Łukasz Kuśmierz, Takuya Isomura, Taro Toyoizumi
Synaptic plasticity is a central theme in neuroscience. A framework of three-factor learning rules provides a powerful abstraction, helping to navigate through the abundance of models of synaptic plasticity. It is well-known that the dopamine modulation of learning is related to reward, but theoretical models predict other functional roles of the modulatory third factor; it may encode errors for supervised learning, summary statistics of the population activity for unsupervised learning or attentional feedback...
September 14, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28918312/-reinforcement-learning-to-forage-optimally
#2
REVIEW
Nils Kolling, Thomas Akam
Foraging effectively is critical to the survival of all animals and this imperative is thought to have profoundly shaped brain evolution. Decisions made by foraging animals often approximate optimal strategies, but the learning and decision mechanisms generating these choices remain poorly understood. Recent work with laboratory foraging tasks in humans suggest their behaviour is poorly explained by model-free reinforcement learning, with simple heuristic strategies better describing behaviour in some tasks, and in others evidence of prospective prediction of the future state of the environment...
September 14, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28915387/can-circular-inference-relate-the-neuropathological-and-behavioral-aspects-of-schizophrenia
#3
REVIEW
Pantelis Leptourgos, Sophie Denève, Renaud Jardri
Schizophrenia is a complex and heterogeneous mental disorder, and researchers have only recently begun to understand its neuropathology. However, since the time of Kraepelin and Bleuler, much information has been accumulated regarding the behavioral abnormalities usually encountered in patients suffering from schizophrenia. Despite recent progress, how the latter are caused by the former is still debated. Here, we argue that circular inference, a computational framework proposed as a potential explanation for various schizophrenia symptoms, could help end this debate...
September 11, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28892737/misdeed-of-the-need-towards-computational-accounts-of-transition-to-addiction
#4
REVIEW
Mehdi Keramati, Serge H Ahmed, Boris S Gutkin
Drug addiction is a complex behavioral and neurobiological disorder which, in an emergent brain-circuit view, reflects a loss of prefrontal top-down control over subcortical circuits governing drug-seeking and drug-taking. We first review previous computational accounts of addiction, focusing on cocaine addiction and on prevalent dopamine-based positive-reinforcement and negative-reinforcement computational models. Then, we discuss a recent computational proposal that the progression to addiction is unlikely to result from a complete withdrawal of the goal-oriented decision system in favor the habitual one...
September 8, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28888857/modelling-plasticity-in-dendrites-from-single-cells-to-networks
#5
REVIEW
Jacopo Bono, Katharina A Wilmes, Claudia Clopath
One of the key questions in neuroscience is how our brain self-organises to efficiently process information. To answer this question, we need to understand the underlying mechanisms of plasticity and their role in shaping synaptic connectivity. Theoretical neuroscience typically investigates plasticity on the level of neural networks. Neural network models often consist of point neurons, completely neglecting neuronal morphology for reasons of simplicity. However, during the past decades it became increasingly clear that inputs are locally processed in the dendrites before they reach the cell body...
September 7, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28888856/computational-models-of-basal-ganglia-dysfunction-the-dynamics-is-in-the-details
#6
REVIEW
Jonathan E Rubin
The development, simulation, and analysis of mathematical models offer helpful tools for integrating experimental findings and exploring or suggesting possible explanatory mechanisms. As models relating to basal ganglia dysfunction have proliferated, however, there has not always been consistency among their findings. This work points out several ways in which biological details, relating to ionic currents and synaptic pathways, can influence the dynamics of models of the basal ganglia under parkinsonian conditions and hence may be important for inclusion in models...
September 7, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28888183/periodic-population-codes-from-a-single-circular-variable-to-higher-dimensions-multiple-nested-scales-and-conceptual-spaces
#7
REVIEW
Andreas Vm Herz, Alexander Mathis, Martin Stemmler
Across the nervous system, neurons often encode circular stimuli using tuning curves that are not sine or cosine functions, but that belong to the richer class of von Mises functions, which are periodic variants of Gaussians. For a population of neurons encoding a single circular variable with such canonical tuning curves, computing a simple population vector is the optimal read-out of the most likely stimulus. We argue that the advantages of population vector read-outs are so compelling that even the neural representation of the outside world's flat Euclidean geometry is curled up into a torus (a circle times a circle), creating the hexagonal activity patterns of mammalian grid cells...
September 6, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28869818/maximum-entropy-models-as-a-tool-for-building-precise-neural-controls
#8
REVIEW
Cristina Savin, Gašper Tkačik
Neural responses are highly structured, with population activity restricted to a small subset of the astronomical range of possible activity patterns. Characterizing these statistical regularities is important for understanding circuit computation, but challenging in practice. Here we review recent approaches based on the maximum entropy principle used for quantifying collective behavior in neural activity. We highlight recent models that capture population-level statistics of neural data, yielding insights into the organization of the neural code and its biological substrate...
September 1, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28850885/quantifying-behavior-to-solve-sensorimotor-transformations-advances-from-worms-and-flies
#9
REVIEW
Adam J Calhoun, Mala Murthy
The development of new computational tools has recently opened up the study of natural behaviors at a precision that was previously unachievable. These tools permit a highly quantitative analysis of behavioral dynamics at timescales that are well matched to the timescales of neural activity. Here we examine how combining these methods with established techniques for estimating an animal's sensory experience presents exciting new opportunities for dissecting the sensorimotor transformations performed by the nervous system...
August 30, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28863386/from-the-statistics-of-connectivity-to-the-statistics-of-spike-times-in-neuronal-networks
#10
REVIEW
Gabriel Koch Ocker, Yu Hu, Michael A Buice, Brent Doiron, Krešimir Josić, Robert Rosenbaum, Eric Shea-Brown
An essential step toward understanding neural circuits is linking their structure and their dynamics. In general, this relationship can be almost arbitrarily complex. Recent theoretical work has, however, begun to identify some broad principles underlying collective spiking activity in neural circuits. The first is that local features of network connectivity can be surprisingly effective in predicting global statistics of activity across a network. The second is that, for the important case of large networks with excitatory-inhibitory balance, correlated spiking persists or vanishes depending on the spatial scales of recurrent and feedforward connectivity...
August 29, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28850820/mechanisms-of-m%C3%A3-ller-glial-cell-morphogenesis
#11
REVIEW
Ryan B MacDonald, Mark Charlton-Perkins, William A Harris
Müller Glia (MG), the radial glia cells of the retina, have spectacular morphologies subserving their enormous functional complexity. As early as 1892, the great neuroanatomist Santiago Ramon y Cajal studied the morphological development of MG, defining several steps in their morphogenesis [1,2]. However, the molecular cues controlling these developmental steps remain poorly understood. As MG have roles to play in every cellular and plexiform layer, this review discusses our current understanding on how MG morphology may be linked to their function, including the developmental mechanisms involved in MG patterning and morphogenesis...
August 26, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28850819/chromatin-remodeling-enzymes-in-control-of-schwann-cell-development-maintenance-and-plasticity
#12
REVIEW
Claire Jacob
Gene regulation is essential for cellular differentiation and plasticity. Schwann cells (SCs), the myelinating glia of the peripheral nervous system (PNS), develop from neural crest cells to mature myelinating SCs and can at early developmental stage differentiate into various cell types. After a PNS lesion, SCs can also convert into repair cells that guide and stimulate axonal regrowth, and remyelinate regenerated axons. What controls their development and versatile nature? Several recent studies highlight the key roles of chromatin modifiers in these processes, allowing SCs to regulate their gene expression profile and thereby acquire or change their identity and quickly react to their environment...
August 26, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28850815/dopaminergic-dysfunction-in-neurodevelopmental-disorders-recent-advances-and-synergistic-technologies-to-aid-basic-research
#13
REVIEW
J Elliott Robinson, Viviana Gradinaru
Neurodevelopmental disorders (NDDs) represent a diverse group of syndromes characterized by abnormal development of the central nervous system and whose symptomatology includes cognitive, emotional, sensory, and motor impairments. The identification of causative genetic defects has allowed for creation of transgenic NDD mouse models that have revealed pathophysiological mechanisms of disease phenotypes in a neural circuit- and cell type-specific manner. Mouse models of several syndromes, including Rett syndrome, Fragile X syndrome, Angelman syndrome, Neurofibromatosis type 1, etc...
August 26, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28843838/parsing-learning-in-networks-using-brain-machine-interfaces
#14
REVIEW
Amy L Orsborn, Bijan Pesaran
Brain-machine interfaces (BMIs) define new ways to interact with our environment and hold great promise for clinical therapies. Motor BMIs, for instance, re-route neural activity to control movements of a new effector and could restore movement to people with paralysis. Increasing experience shows that interfacing with the brain inevitably changes the brain. BMIs engage and depend on a wide array of innate learning mechanisms to produce meaningful behavior. BMIs precisely define the information streams into and out of the brain, but engage wide-spread learning...
August 24, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28843800/striatal-synapses-circuits-and-parkinson-s-disease
#15
REVIEW
Shenyu Zhai, Asami Tanimura, Steven M Graves, Weixing Shen, D James Surmeier
The striatum is a hub in the basal ganglia circuitry controlling goal directed actions and habits. The loss of its dopaminergic (DAergic) innervation in Parkinson's disease (PD) disrupts the ability of the two principal striatal projection systems to respond appropriately to cortical and thalamic signals, resulting in the hypokinetic features of the disease. New tools to study brain circuitry have led to significant advances in our understanding of striatal circuits and how they adapt in PD models. This short review summarizes some of these recent studies and the gaps that remain to be filled...
August 24, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28841438/modeling-the-mammalian-sleep-cycle
#16
REVIEW
Franz Weber
During sleep, the mammalian brain transitions through repeated cycles of non-rapid-eye-movement (NREM) and rapid-eye-movement (REM) sleep. The physiological implementation of this slow ultradian brain rhythm is largely unknown. Two differing dynamical mechanisms have been proposed to underlie the NREM-REM cycle. The first model type relies on reciprocal interactions between inhibitory and excitatory neural populations resulting in stable limit cycle oscillations. Recent experimental findings instead favor a model, in which mutually inhibitory interactions between REM sleep-promoting (REM-on) and REM sleep-suppressing (REM-off) neural populations stabilize the brain state...
August 24, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28841439/characterizing-and-interpreting-the-influence-of-internal-variables-on-sensory-activity
#17
REVIEW
Richard D Lange, Ralf M Haefner
The concept of a tuning curve has been central for our understanding of how the responses of cortical neurons depend on external stimuli. Here, we describe how the influence of unobserved internal variables on sensory responses, in particular correlated neural variability, can be understood in a similar framework. We suggest that this will lead to deeper insights into the relationship between stimulus, sensory responses, and behavior. We review related recent work and discuss its implication for distinguishing feedforward from feedback influences on sensory responses, and for the information contained in those responses...
August 22, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28841437/new-insights-into-olivo-cerebellar-circuits-for-learning-from-a-small-training-sample
#18
REVIEW
Isao T Tokuda, Huu Hoang, Mitsuo Kawato
Artificial intelligence such as deep neural networks exhibited remarkable performance in simulated video games and 'Go'. In contrast, most humanoid robots in the DARPA Robotics Challenge fell down to ground. The dramatic contrast in performance is mainly due to differences in the amount of training data, which is huge and small, respectively. Animals are not allowed with millions of the failed trials, which lead to injury and death. Humans fall only several thousand times before they balance and walk. We hypothesize that a unique closed-loop neural circuit formed by the Purkinje cells, the cerebellar deep nucleus and the inferior olive in and around the cerebellum and the highest density of gap junctions, which regulate synchronous activities of the inferior olive nucleus, are computational machinery for learning from a small sample...
August 19, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28829986/neurobiology-of-autoimmune-encephalitis
#19
REVIEW
Masaki Fukata, Norihiko Yokoi, Yuko Fukata
Autoimmune encephalitis presenting with amnesia, seizures, and psychosis is highly topical in basic and clinical neuroscience. Recent studies have identified numerous associated autoantibodies, targeting cell-surface synaptic proteins including neurotransmitter receptors (e.g. NMDA receptors (NMDARs)) and a secreted protein, LGI1. In vitro and in vivo analyses of the influence of the autoantibodies have begun to clarify their causal roles. Of particular interest is the generation of recombinant monoclonal antibodies from patients' B cells with anti-NMDAR encephalitis...
August 19, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28806694/what-can-neuronal-populations-tell-us-about-cognition
#20
REVIEW
Iñigo Arandia-Romero, Ramon Nogueira, Gabriela Mochol, Rubén Moreno-Bote
Nowadays, it is possible to record the activity of hundreds of cells at the same time in behaving animals. However, these data are often treated and analyzed as if they consisted of many independently recorded neurons. How can neuronal populations be uniquely used to learn about cognition? We describe recent work that shows that populations of simultaneously recorded neurons are fundamental to understand the basis of decision-making, including processes such as ongoing deliberations and decision confidence, which generally fall outside the reach of single-cell analysis...
August 11, 2017: Current Opinion in Neurobiology
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