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

BACKGROUND: People with hereditary and spontaneous spastic paraparesis (HSSP) report that their legs are stiffer and walking is slower when their legs are cold. OBJECTIVES: This study explored the effects of prolonged superficial cooling and warming of the lower leg on walking speed and local measures of neuromuscular impairments. METHODS: This was a randomised pre- and post-intervention study of 22 HSSP participants and 19 matched healthy controls. On 2 separate occasions, one lower leg was cooled or warmed. Measurements included walking speed and measures of lower limb impairment: ankle movement, passive muscle stiffness, spasticity (stretch reflex size), amplitude and rate of force generation in dorsi- and plantarflexors and central and peripheral nerve conduction time/velocity. RESULTS: For both participants and controls, cooling decreased walking speed, especially for HSSP participants. For both groups, cooling decreased the dorsiflexor rate and amplitude of force generation and peripheral nerve conduction velocity and increased spasticity. Warming increased dorsiflexor rate of force generation and nerve conduction velocity and decreased spasticity. CONCLUSIONS: Superficial cooling significantly reduced walking speed for people with HSSP. Temperature changes were associated with changes in neuromuscular impairments for both people with spastic paraparesis and controls. This study does not support the use of localised cooling in rehabilitation for people with spastic paraparesis as reported in other neurological conditions. Rehabilitation interventions that help prevent heat loss (insulation) or improve limb temperature via passive or active means, particularly when the legs and/or environment are cool, may benefit people with spastic paraparesis.

Biologically plausible neural computation.

The function of a neuron can be described simultaneously at several levels of abstraction. For instance, a spike train represents the result of a computation done by a single neuron with its inputs, but it also represents the result of a complex function realized by the network in which the neuron is embedded. When models of large parts of the brain are considered, it may be desirable to use computational modules operating at a very abstract level. However, it is shown here that abstract neural functions depend on detailed features of the single neuron model used in the network reproducing the abstract function. Examples are given of the multiplicative function, motion detection, short-term memory and timing. All these operations rely on one or another feature of the extended Leaky Integrate-and-Fire neuron used in this paper, e.g. probabilistic synapses, post-synaptic currents modelled with alpha functions or partial reset of the somatic membrane. Consequently it is suggested that neural modelling at an abstract level does not obviate the need for a clear statement on the nature of the underlying model of biological neuron. In that sense, not many abstract functions are convincingly grounded, not even the standard formal neurons used in most artificial neural networks.

Towards a neural model of timing.

This paper describes a neural model of interval timing, which reproduces the duration discrimination experiments of Wearden, J.H., 1992, J. Exp. Psychol. 18, 134-144. The model comprises three layers of neural units. The units in the first layer represent clusters of neurons with probabilistic internal feedback that maintains self-sustained (short-term memory) activity for a random time. The unit in the second layer is a spiking neuron that fires as long as a sufficient number of input clusters are active. The unit in the third layer detects the offset of firing in the previous layer by producing a short burst of spikes. Analysis and simulation of the model shows spikes produced at random times with a distribution determined by the number of units in the first layer, their survival time constant, and the threshold of the unit in layer 2. Interval times can be learned with any of these parameters but lead to different Weber law relations. A variable threshold in layer 2 predicts S-shaped Weber curves, a variable number of units in layer 1 leads to a saturation of the Weber curve (decreasing Weber fraction) and a variable time constant in layer 1 causes a linear Weber curve.

Near Poisson-type firing produced by concurrent excitation and inhibition.

The effect of inhibition on the firing variability is examined in this paper using the biologically-inspired temporal noisy-leaky integrator (TNLI) neuron model. The TNLI incorporates hyperpolarising inhibition with negative current pulses of controlled shapes and it also separates dendritic from somatic integration. The firing variability is observed by looking at the coefficient of variation (C(V)) (standard deviation/mean interspike interval) as a function of the mean interspike interval of firing (delta tM) and by comparing the results with the theoretical curve for random spike trains, as well as looking at the interspike interval (ISI) histogram distributions. The results show that with 80% inhibition, firing at high rates (up to 200 Hz) is nearly consistent with a Poisson-type variability, which complies with the analysis of cortical neuron firing recordings by Softky and Koch [1993, J. Neurosci. 13(1) 334-530]. We also demonstrate that the mechanism by which inhibition increases the C(V) values is by introducing more short intervals in the firing pattern as indicated by a small initial hump at the beginning of the ISI histogram distribution. The use of stochastic inputs and the separation of the dendritic and somatic integration which we model in TNLI, also affect the high firing, near Poisson-type (explained in the paper) variability produced. We have also found that partial dendritic reset increases slightly the firing variability especially at short ISIs.

Modelling relative recency discrimination tasks using a stochastic working memory model.

Relative recency discrimination (RD) task is typically used to assess the temporal organization function of the prefrontal cortex (PFC). Subjects look at a series of cards (with words or drawings on them) and on seeing a test card determine which of the two items was seen more recently. Results show that patients with damage to the prefrontal cortex are severely impaired on this task. We propose a memory trace-priming mechanism, based on automatic time-marking process hypothesis (Schacter, D.L., 1987. Memory, amnesia, and frontal lobe dysfunction: a critique and interpretation. Psychobiology 15, 21-36), to offer a computational account of the results. In this model, successive words seen by subjects leave decaying memory traces in PFC, which subsequently prime the representations in higher sensory areas such as inferior temporal Cortex (IT) during discrimination judgements. The paper focuses on the evaluation of a probabilistic pre-frontal trace mechanism using a pool of clusters of neurons with self-sustained firing that ends at a random time. The results show that the probabilistic behavior of subjects can be accounted for by the stochasticity of the trace model. A good fit to experimental data is obtained with a PFC memory persistence probability with a decay time constant of tau = approximately 30 s. The model allows for a distributed representation in IT and PFC, but the best fit suggests a sparse representation. It is concluded that further data are needed on representations in IT and PFC, on the connectivity between these two areas, and on the statistical and dynamic properties of memory neurons in PFC.

[Demonstration of a control lever for a simulator of visual aptitudes]. / Présentation d'un manche-réponse pour un simulateur d'aptitudes visuelles.

In this paper, the design and testing of the control of a new visual screener are described. The equipment is composed of a push-pull lever, an electronic digitalizer and a display on which Landolt rings are presented. The problem to be solved was to check whether the eight directions in which the lever could be oriented were equally reliable. For this purpose, 24 untrained subjects were required to respond to 108 by-chance oriented optotypes. This experiment demonstrated that the device was reliable in this respect that it produced very few errors; however, fiability was unequally distributed over the eight orientations. Suggestions for improvement were made.

Multiplying with neurons: compensation for irregular input spike trains by using time-dependent synaptic efficiencies.

A leaky integrate-and-fire (LIF) neurons can act as multipliers by detecting coincidences of input spikes. However, in case of input spike trains with irregular interspike delays, false coincidences are also detected and the operation as a multiplier is degraded. This problem can be solved by using time dependent synaptic weights which are set to zero after each input spike and recover with the same time constant as the decay time of the corresponding excitatory postsynaptic potentials (EPSP). Such a mechanism results in EPSP's with amplitudes independent on the input interspike delays. Neuronal computation is then performed without frequency decoding.