1: Biol Cybern. 2004 Jul;91(1):10-22. Epub 2004 Aug 09.
Biological arm motion through reinforcement learning.
Izawa J, Kondo T, Ito K.
Sensory and Motor Research Group, Human and Information Science Laboratory, NTT
Communication Science Laboratories 3-1, Morinosato-Wakamiya, 243-01, Atsugi-shi,
Japan, izawa@idea.brl.ntt.co.jp
The present paper discusses an optimal learning control method using
reinforcement learning for biological systems with a redundant actuator. It is
difficult to apply reinforcement learning to biological control systems because
of the redundancy in muscle activation space. We solve this problem with the
following method. First, we divide the control input space into two subspaces
according to a priority order of learning and restrict the search noise for
reinforcement learning to the first priority subspace. Then the constraint is
reduced as the learning progresses, with the search space extending to the
second priority subspace. The higher priority subspace is designed so that the
impedance of the arm can be high. A smooth reaching motion is obtained through
reinforcement learning without any previous knowledge of the arm's dynamics.
PMID: 15309543 [PubMed - in process]
2: PLoS Biol. 2004 Sep;2(9):E264. Epub 2004 Aug 24.
Amplification of Trial-to-Trial Response Variability by Neurons in Visual
Cortex.
Carandini M.
Smith-Kettlewell Eye Research Institute, San Francisco, California, United
States of America. matteo@ski.org
The visual cortex responds to repeated presentations of the same stimulus with
high variability. Because the firing mechanism is remarkably noiseless, the
source of this variability is thought to lie in the membrane potential
fluctuations that result from summated synaptic input. Here this hypothesis is
tested through measurements of membrane potential during visual stimulation.
Surprisingly, trial-to-trial variability of membrane potential is found to be
low. The ratio of variance to mean is much lower for membrane potential than for
firing rate. The high variability of firing rate is explained by the threshold
present in the function that converts inputs into firing rates. Given an input
with small, constant noise, this function produces a firing rate with a large
variance that grows with the mean. This model is validated on responses recorded
both intracellularly and extracellularly. In neurons of visual cortex, thus, a
simple deterministic mechanism amplifies the low variability of summated
synaptic inputs into the large variability of firing rate. The computational
advantages provided by this amplification are not known.
PMID: 15328535 [PubMed - in process]
3: Trends Neurosci. 2004 Oct;27(10):637-43.
The cutaneous contribution to adaptive precision grip.
Witney AG, Wing A, Thonnard JL, Smith AM.
Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2
3EJ, UK.
Only after injury, or perhaps prolonged exposure to cold that is sufficient to
numb the fingers, do we suddenly appreciate the complex neural mechanisms that
underlie our effortless dexterity in manipulating objects. The nervous system is
capable of adapting grip forces to a wide range of object shapes, weights and
frictional properties, to provide optimal and secure handling in a variety of
potentially perturbing environments. The dynamic interplay between sensory
information and motor commands provides the basis for this flexibility, and
recent studies supply somewhat unexpected evidence of the essential role played
by cutaneous feedback in maintaining and acquiring predictive grip force
control. These examples also offer new insights into the adaptive control of
other voluntary movements.
PMID: 15374677 [PubMed - in process]
4: Trends Neurosci. 2004 Aug;27(8):496-503.
Viewing and doing: similar cortical mechanisms for perceptual and motor
learning.
Paz R, Wise SP, Vaadia E.
Laboratory of Systems Neuroscience, National Institute of Mental Health,
Bethesda, MD 20892-4401, USA. ronyp@hbf.huij.ac.il
Historically, different groups of researchers have investigated the mechanisms
of perceptual learning and motor learning. For sensory cortex,
neurophysiological and psychophysical findings have linked changes in perception
with altered neuronal tuning properties. However, less information has been
forthcoming from motor cortex. This review compares recent findings on
perceptual and motor learning, and suggests that similar mechanisms govern both.
These mechanisms involve changes in both the center of neuronal tuning functions
and their width or slope. The former reflects the values of the sensory or motor
parameters that a neuron encodes, and the latter adjusts the encoding
sensitivity. These similarities suggest that specific unifying principles for
neural coding and computation exist across sensory and motor domains.
PMID: 15271498 [PubMed - in process]
5: J Mot Behav. 2004 Sep;36(3):245-52.
Saccadic output is influenced by limb kinetics during eye-hand coordination.
van Donkelaar P, Siu KC, Walterschied J.
Department of Exercise and Movement Science, Institute of Neuroscience,
University of Oregon, Eugene, OR, USA. paulvd@darkwing.uoregon.edu
In several recent studies, saccadic eye movements were found to be influenced by
concurrent reaching movements. The authors investigated whether that influence
originates in limb kinematic or kinetic signals. To dissociate those 2
possibilities, the authors required participants (N = 6) to generate pointing
movements with a mass that either resisted or assisted limb motion. With practice,
participants were able to generate pointing responses with very similar kinematics
but whose kinetics varied in a systematic manner. The results showed that saccadic
output was altered by the amount of force required to move the arm, consistent with
an influence from limb kinetic signals. Because the interaction occurred before the
pointing response began, the authors conclude that a predictive signal related to limb
kinetics modulates saccadic output during tasks requiring eye-hand coordination.
PMID: 15262621 [PubMed - in process]