On learning time delays between the spikes from different input neurons in a biophysical model of a pyramidal neuron.

Biological systems are able to recognise temporal sequences of stimuli or compute in the temporal domain. In this paper we are exploring whether a biophysical model of a pyramidal neuron can detect and learn systematic time delays between the spikes from different input neurons. In particular, we investigate whether it is possible to reinforce pairs of synapses separated by a dendritic propagation time delay corresponding to the arrival time difference of two spikes from two different input neurons. We examine two subthreshold learning approaches where the first relies on the backpropagation of EPSPs (excitatory postsynaptic potentials) and the second on the backpropagation of a somatic action potential, whose production is supported by a learning-enabling background current. The first approach does not provide a learning signal that sufficiently differentiates between synapses at different locations, while in the second approach, somatic spikes do not provide a reliable signal distinguishing arrival time differences of the order of the dendritic propagation time. It appears that the firing of pyramidal neurons shows little sensitivity to heterosynaptic spike arrival time differences of several milliseconds. This neuron is therefore unlikely to be able to learn to detect such differences

The speed of learning instructed stimulus-response association rules in human: experimental data and model.

Humans can learn associations between visual stimuli and motor responses from just a single instruction. This is known to be a fast process, but how fast is it? To answer this question, we asked participants to learn a briefly presented (200ms) stimulus-response rule, which they then had to rapidly apply after a variable delay of between 50 and 1300ms. Participants showed a longer response time with increased variability for short delays. The error rate was low and did not vary with the delay, showing that participants were able to encode the rule correctly in less than 250ms. This time is close to the fastest synaptic learning speed deemed possible by diffusive influx of AMPA receptors. Learning continued at a slower pace in the delay period and was fully completed in average 900ms after rule presentation onset, when response latencies dropped to levels consistent with basic reaction times. A neural model was proposed that explains the reduction of response times and of their variability with the delay by (i) a random synaptic learning process that generates weights of average values increasing with the learning time, followed by (ii) random crossing of the firing threshold by a leaky integrate-and-fire neuron model, and (iii) assuming that the behavioural response is initiated when all neurons in a pool of m neurons have fired their first spike after input onset. Values of m=2 or 3 were consistent with the experimental data. The proposed model is the simplest solution consistent with neurophysiological knowledge. Additional experiments are suggested to test the hypothesis underlying the model and also to explore forgetting effects for which there were indications for the longer delay conditions. This article is part of a Special Issue entitled Neural Coding 2012

On quaternion based parametrization of orientation in computer vision and robotics

The problem of orientation parameterization for applications in computer vision and robotics is examined in detail herein. The necessary intuition and formulas are provided for direct practical use in any existing algorithm that seeks to minimize a cost function in an iterative fashion. Two distinct schemes of parameterization are analyzed: The first scheme concerns the traditional axis-angle approach, while the second employs stereographic projection from unit quaternion sphere to the 3D real projective space. Performance measurements are taken and a comparison is made between the two approaches. Results suggests that there exist several benefits in the use of stereographic projection that include rational expressions in the rotation matrix derivatives, improved accuracy, robustness to random starting points and accelerated convergence

Probing the early phase of rapid instructed rule encoding

Item does not contain fulltextHumans can rapidly convert instructions about a rule into functional neural structures used to apply the rule. The early stages of this encoding process are poorly understood. We designed a stimulus–response (SR) task in which participants were first shown a SR rule on a screen for 200 ms, and then had to apply it to a test stimulus T, which either matched the S in the rule (SR trial) or not (catch trial). To investigate the early stages of rule encoding, the delay between the end of rule display and the onset of the test stimulus was manipulated and chosen between values of 50 ms to 1300 ms. Participants conducted three sessions of 288 trials each, separated by a median of 9 h. Random sequences of 20 rules were used. We then analysed the reaction times and the types of errors made by participants in the different conditions. The analysis of practice effects in session 1 suggests that the neural networks that process SR and catch trials are at least partially distinct, and improve separately during the practice of respectively SR and catch trials. The rule-encoding process, however, is common to both tasks and improves with the number of trials, irrespective of the trial type. Rule encoding shows interesting dynamic properties that last for 500 ms after the end of the stimulus presentation. The encoding process increases the response time in a non-stochastic way, simply adding a reaction time cost to all responses. The rule-retrieval system is functional before the encoding has stabilized, as early as 50 ms after the end of SR rule presentation, with low response errors. It is sensitive to masking however, producing errors with brief (100 ms) test stimulus presentations. Once encoding has stabilized, the sensitivity to masking disappears. It is suggested that participants do encode rules as a parametrized function, using the same neural encoding structure for each trial, rather than reconfiguring their brain anew for each new SR rule. This structure would have been implemented from instructions received prior to the experiment, by using a library of neural functions available in the brain. The observed errors are consistent with this view.13 p

Superficial warming and cooling of the leg affects walking speed and neuromuscular impairments in people with spastic paraparesis

publisher: Elsevier articletitle: Superficial warming and cooling of the leg affects walking speed and neuromuscular impairments in people with spastic paraparesis journaltitle: Annals of Physical and Rehabilitation Medicine articlelink: http://dx.doi.org/10.1016/j.rehab.2016.04.006 content_type: article copyright: © 2016 Elsevier Masson SAS. All rights reserved