Age-Related Attenuation of Dominant Hand Superiority

The decline of motor performance of the human hand-arm system with age is well-documented. While dominant hand performance is superior to that of the non-dominant hand in young individuals, little is known of possible age-related changes in hand dominance. We investigated age-related alterations of hand dominance in 20 to 90 year old subjects. All subjects were unambiguously right-handed according to the Edinburgh Handedness Inventory. In Experiment 1, motor performance for aiming, postural tremor, precision of arm-hand movement, speed of arm-hand movement, and wrist-finger speed tasks were tested. In Experiment 2, accelerometer-sensors were used to obtain objective records of hand use in everyday activities

The Spatial and Temporal Construction of Confidence in the Visual Scene

Human subjects can report many items of a cluttered field a few hundred milliseconds after stimulus presentation. This memory decays rapidly and after a second only 3 or 4 items can be stored in working memory. Here we compared the dynamics of objective performance with a measure of subjective report and we observed that 1) Objective performance beyond explicit subjective reports (blindsight) was significantly more pronounced within a short temporal interval and within specific locations of the visual field which were robust across sessions 2) High confidence errors (false beliefs) were largely confined to a small spatial window neighboring the cue. The size of this window did not change in time 3) Subjective confidence showed a moderate but consistent decrease with time, independent of all other experimental factors. Our study allowed us to asses quantitatively the temporal and spatial access to an objective response and to subjective reports

A Common Cortical Circuit Mechanism for Perceptual Categorical Discrimination and Veridical Judgment

Perception involves two types of decisions about the sensory world: identification of stimulus features as analog quantities, or discrimination of the same stimulus features among a set of discrete alternatives. Veridical judgment and categorical discrimination have traditionally been conceptualized as two distinct computational problems. Here, we found that these two types of decision making can be subserved by a shared cortical circuit mechanism. We used a continuous recurrent network model to simulate two monkey experiments in which subjects were required to make either a two-alternative forced choice or a veridical judgment about the direction of random-dot motion. The model network is endowed with a continuum of bell-shaped population activity patterns, each representing a possible motion direction. Slow recurrent excitation underlies accumulation of sensory evidence, and its interplay with strong recurrent inhibition leads to decision behaviors. The model reproduced the monkey's performance as well as single-neuron activity in the categorical discrimination task. Furthermore, we examined how direction identification is determined by a combination of sensory stimulation and microstimulation. Using a population-vector measure, we found that direction judgments instantiate winner-take-all (with the population vector coinciding with either the coherent motion direction or the electrically elicited motion direction) when two stimuli are far apart, or vector averaging (with the population vector falling between the two directions) when two stimuli are close to each other. Interestingly, for a broad range of intermediate angular distances between the two stimuli, the network displays a mixed strategy in the sense that direction estimates are stochastically produced by winner-take-all on some trials and by vector averaging on the other trials, a model prediction that is experimentally testable. This work thus lends support to a common neurodynamic framework for both veridical judgment and categorical discrimination in perceptual decision making

A complementary role of intracortical inhibition in age-related tactile degradation and its remodelling in humans

Many attempts are currently underway to restore age-related degraded perception, however, the link between restored perception and remodeled brain function remains elusive. To understand remodeling of age-related cortical reorganization we combined functional magnetic resonance imaging (fMRI) with assessments of tactile acuity, perceptual learning, and computational modeling. We show that aging leads to tactile degradation parallel to enhanced activity in somatosensory cortex. Using a neural field model we reconciled the empirical age-effects by weakening of cortical lateral inhibition. Using perceptual learning, we were able to partially restore tactile acuity, which however was not accompanied by the expected attenuation of cortical activity, but by a further enhancement. The neural field model reproduced these learning effects solely through a weakening of the amplitude of inhibition. These findings suggest that the restoration of age-related degraded tactile acuity on the cortical level is not achieved by re-strengthening lateral inhibition but by further weakening intracortical inhibition

Learning to Look: A Dynamic Neural Fields Architecture for Gaze Shift Generation

Abstract. Looking is one of the most basic and fundamental goal-directed behaviors. The neural circuitry that generates gaze shifts to-wards target objects is adaptive and compensates for changes in the sen-sorimotor plant. Here, we present a neural-dynamic architecture, which enables an embodied agent to direct its gaze towards salient objects in its environment. The sensorimotor mapping, which is needed to accu-rately plan the gaze shifts, is initially learned and is constantly updated by a gain adaptation mechanism. We implemented the architecture in a simulated robotic agent and demonstrated autonomous map learning and adaptation in an embodied setting

Artificial cognitive systems: From VLSI networks of spiking neurons to neuromorphic cognition

Neuromorphic engineering (NE) is an emerging research field that has been attempting to identify neural types of computational principles, by implementing biophysically realistic models of neural systems in Very Large Scale Integration (VLSI) technology. Remarkable progress has been made recently, and complex artificial neural sensory-motor systems can be built using this technology. Today, however, NE stands before a large conceptual challenge that must be met before there will be significant progress toward an age of genuinely intelligent neuromorphic machines. The challenge is to bridge the gap from reactive systems to ones that are cognitive in quality. In this paper, we describe recent advancements in NE, and present examples of neuromorphic circuits that can be used as tools to address this challenge. Specifically, we show how VLSI networks of spiking neurons with spike-based plasticity mechanisms and soft winner-take-all architectures represent important building blocks useful for implementing artificial neural systems able to exhibit basic cognitive abilities