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Highlights: [•] Insufficient knowledge by the experimenter results in neural variability. [•] What counts as variability for the experimenter and for the organism may be different. [•] The sources of variability can be targeted through modeling studies. [•] Variability may have an adaptive functional role. [ABSTRACT FROM AUTHOR]

Summary: A central goal in sensory neuroscience is to fully characterize a neuron''s input-output relation. However, strong nonlinearities in the responses of sensory neurons have made it difficult to develop models that generalize to arbitrary stimuli. Typically, the standard linear-nonlinear models break down when neurons exhibit stimulus-dependent modulations of their gain or selectivity. We studied these issues in optic-flow processing neurons in the fly. We found that the neurons'' receptive fields are fully described by a time-varying vector field that is space-time separable. Increasing the stimulus strength, however, strongly reduces the neurons'' gain and selectivity. To capture these changes in response behavior, we extended the linear-nonlinear model by a biophysically motivated gain and selectivity mechanism. We fit all model parameters directly to the data and show that the model now characterizes the neurons'' input-output relation well over the full range of motion stimuli. [Copyright &y& Elsevier]

Summary: According to Barlow’s seminal “efficient coding hypothesis,” the coding strategy of sensory neurons should be matched to the statistics of stimuli that occur in an animal’s natural habitat. Using an automatic search technique, we here test this hypothesis and identify stimulus ensembles that sensory neurons are optimized for. Focusing on grasshopper auditory receptor neurons, we find that their optimal stimulus ensembles differ from the natural environment, but largely overlap with a behaviorally important sub-ensemble of the natural sounds. This indicates that the receptors are optimized for peak rather than average performance. More generally, our results suggest that the coding strategies of sensory neurons are heavily influenced by differences in behavioral relevance among natural stimuli. [Copyright &y& Elsevier]

Networks adapt to environmental demands by switching betweendistinct dynamical behaviors. The activity of frontal-lobe neuronsduring two-interval discrimination tasks is an example of theseadaptable dynamics. Subjects first perceive a stimulus, then hold it inworking memory, and finally make a decision by comparing it with asecond stimulus. We present a simple mutualinhibition network model thatcaptures all three task phases within a single framework. The modelintegrates both working memory and decision making because its dynamicalproperties are easily controlled without changing its connectivity.Mutual inhibition between nonlinear units is a useful design motif fornetworks that must display multiple behaviors. [ABSTRACT FROM AUTHOR]

• A fundamental question in neuroscience is how neuronal populations in different brain areas communicate. • Multi-area neuronal population recordings are increasingly common. • Multivariate methods are needed to interrogate these recordings and characterize how neuronal population activity is related across areas. • We discuss interpretational challenges and considerations for these methods, and suggest directions for future work. The brain is composed of many functionally distinct areas. This organization supports distributed processing, and requires the coordination of signals across areas. Our understanding of how populations of neurons in different areas interact with each other is still in its infancy. As the availability of recordings from large populations of neurons across multiple brain areas increases, so does the need for statistical methods that are well suited for dissecting and interrogating these recordings. Here we review multivariate statistical methods that have been, or could be, applied to this class of recordings. By leveraging population responses, these methods can provide a rich description of inter-areal interactions. At the same time, these methods can introduce interpretational challenges. We thus conclude by discussing how to interpret the outputs of these methods to further our understanding of inter-areal interactions. [ABSTRACT FROM AUTHOR]

The article discusses a computational model of the human brain capable of simulating complex behaviors developed by C. Eliasmith et al. Eliasmith and colleagues' model, which is called the Semantic Pointer Architecture Unified Network (Spaun), can observe visual images and indicate its responses with a physical model of an arm. Topics include the challenges of building an artificial brain, such as the complexity of the human brain, the range of tasks Spaun can perform, such as image recognition, and an overview of the cognitive machinery of Spaun.

The fundamental building block of every nervous system is the single neuron. Understanding how these exquisitely structured elements operate is an integral part of the quest to solve the mysteries of the brain. Quantitative mathematical models have proved to be an indispensable tool in pursuing this goal. We review recent advances and examine how single-cell models on five levels of complexity, from black-box approaches to detailed compartmental simulations, address key questions about neural dynamics and signal processing. [ABSTRACT FROM AUTHOR]