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

Spatial Learning and Action Planning in a Prefrontal Cortical Network Model

The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive “insight” capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates

Brainhack: a collaborative workshop for the open neuroscience community

International audienceBrainhack events offer a novel workshop format with participant-generated content that caters to the rapidly growing open neuroscience community. Including components from hackathons and unconferences, as well as parallel educational sessions, Brainhack fosters novel collaborations around the interests of its attendees. Here we provide an overview of its structure, past events, and example projects. Additionally, we outline current innovations such as regional events and post-conference publications. Through introducing Brainhack to the wider neuroscience community, we hope to provide a unique conference format that promotes the features of collaborative, open science

Towards Comprehensive Foundations of Computational Intelligence

Abstract. Although computational intelligence (CI) covers a vast variety of different methods it still lacks an integrative theory. Several proposals for CI foundations are discussed: computing and cognition as compression, meta-learning as search in the space of data models, (dis)similarity based methods providing a framework for such meta-learning, and a more general approach based on chains of transformations. Many useful transformations that extract information from features are discussed. Heterogeneous adaptive systems are presented as particular example of transformation-based systems, and the goal of learning is redefined to facilitate creation of simpler data models. The need to understand data structures leads to techniques for logical and prototype-based rule extraction, and to generation of multiple alternative models, while the need to increase predictive power of adaptive models leads to committees of competent models. Learning from partial observations is a natural extension towards reasoning based on perceptions, and an approach to intuitive solving of such problems is presented. Throughout the paper neurocognitive inspirations are frequently used and are especially important in modeling of the higher cognitive functions. Promising directions such as liquid and laminar computing are identified and many open problems presented.