Of Toasters and Molecular Ticker Tapes

Experiments in systems neuroscience can be seen as consisting of three steps: (1) selecting the signals we are interested in, (2) probing the system with carefully chosen stimuli, and (3) getting data out of the brain. Here I discuss how emerging techniques in molecular biology are starting to improve these three steps. To estimate its future impact on experimental neuroscience, I will stress the analogy of ongoing progress with that of microprocessor production techniques. These techniques have allowed computers to simplify countless problems; because they are easier to use than mechanical timers, they are even built into toasters. Molecular biology may advance even faster than computer speeds and has made immense progress in understanding and designing molecules. These advancements may in turn produce impressive improvements to each of the three steps, ultimately shifting the bottleneck from obtaining data to interpreting it

Reconstructing Speech from Human Auditory Cortex

Direct brain recordings from neurosurgical patients listening to speech reveal that the acoustic speech signals can be reconstructed from neural activity in auditory cortex

Confusion and dependence in uses of history

Many people argue that history makes a special difference to the subjects of biology and psychology, and that history does not make this special difference to other parts of the world. This paper will show that historical properties make no more or less of a difference to biology or psychology than to chemistry, physics, or other sciences. Although historical properties indeed make a certain kind of difference to biology and psychology, this paper will show that historical properties make the same kind of difference to geology, sociology, astronomy, and other sciences. Similarly, many people argue that nonhistorical properties make a special difference to the nonbiological and the nonpsychological world. This paper will show that nonhistorical properties make the same difference to all things in the world when it comes to their causal behavior and that historical properties make the same difference to all things in the world when it comes to their distributions. Although history is special, it is special in the same way to all parts of the worl

Direction of Movement Is Encoded in the Human Primary Motor Cortex

The present study investigated how direction of hand movement, which is a well-described parameter in cerebral organization of motor control, is incorporated in the somatotopic representation of the manual effector system in the human primary motor cortex (M1). Using functional magnetic resonance imaging (fMRI) and a manual step-tracking task we found that activation patterns related to movement in different directions were spatially disjoint within the representation area of the hand on M1. Foci of activation related to specific movement directions were segregated within the M1 hand area; activation related to direction 0° (right) was located most laterally/superficially, whereas directions 180° (left) and 270° (down) elicited activation more medially within the hand area. Activation related to direction 90° was located between the other directions. Moreover, by investigating differences between activations related to movement along the horizontal (0°+180°) and vertical (90°+270°) axis, we found that activation related to the horizontal axis was located more anterolaterally/dorsally in M1 than for the vertical axis, supporting that activations related to individual movement directions are direction- and not muscle related. Our results of spatially segregated direction-related activations in M1 are in accordance with findings of recent fMRI studies on neural encoding of direction in human M1. Our results thus provide further evidence for a direct link between direction as an organizational principle in sensorimotor transformation and movement execution coded by effector representations in M1

Shape similarity, better than semantic membership, accounts for the structure of visual object representations in a population of monkey inferotemporal neurons

The anterior inferotemporal cortex (IT) is the highest stage along the hierarchy of visual areas that, in primates, processes visual objects. Although several lines of evidence suggest that IT primarily represents visual shape information, some recent studies have argued that neuronal ensembles in IT code the semantic membership of visual objects (i.e., represent conceptual classes such as animate and inanimate objects). In this study, we investigated to what extent semantic, rather than purely visual information, is represented in IT by performing a multivariate analysis of IT responses to a set of visual objects. By relying on a variety of machine-learning approaches (including a cutting-edge clustering algorithm that has been recently developed in the domain of statistical physics), we found that, in most instances, IT representation of visual objects is accounted for by their similarity at the level of shape or, more surprisingly, low-level visual properties. Only in a few cases we observed IT representations of semantic classes that were not explainable by the visual similarity of their members. Overall, these findings reassert the primary function of IT as a conveyor of explicit visual shape information, and reveal that low-level visual properties are represented in IT to a greater extent than previously appreciated. In addition, our work demonstrates how combining a variety of state-of-the-art multivariate approaches, and carefully estimating the contribution of shape similarity to the representation of object categories, can substantially advance our understanding of neuronal coding of visual objects in cortex

Exploring Brain Effective Connectivity in Visual Perception Using a Hierarchical Correlation Network

Part 6: Constraint Programming - Brain Inspired ModelingInternational audienceBrain-inspired computing is a research hotspot in artificial intelligence (AI). One of the key problems in this field is how to find the bridge between brain connectivity and data correlation in a connection-to-cognition model. Functional magnetic resonance imaging (fMRI) signals provide rich information about brain activities. Existing modeling approaches with fMRI focus on the strength information, but neglect structural information. In a previous work, we proposed a monolayer correlation network (CorrNet) to model the structural connectivity. In this paper, we extend the monolayer CorrNet to a hierarchical correlation network (HcorrNet) by analysing visual stimuli of natural images and fMRI signals in the entire visual cortex, that is, V1, V2 V3, V4, fusiform face area (FFA), the lateral occipital complex (LOC) and parahippocampal place area (PPA). Through the HcorrNet, the efficient connectivity of the brain can be inferred layer by layer. Then, the stimulus-sensitive activity mode of voxels can be extracted, and the forward encoding process of visual perception can be modeled. Both of them can guide the decoding process of fMRI signals, including classification and image reconstruction. In the experiments, we improved a dynamic evolving spike neuron network (SNN) as the classifier, and used Generative Adversarial Networks (GANs) to reconstruct image