大鼠听觉反应阈的测定

应用微电极技术测定了45只大鼠325根单一听神经纤维的特征频率及其阈值和调谐曲线。测得特征频率的最低值为0.58kHz,最高值为62.6kHz。敏感度最高的频带在20～50kHz,敏感度最高的阈值为6dB(SPL),其相应的频率为27.49kHz。由最低阈值连线延续到边侧的调谐曲线,便形成了大鼠整个的听反应阈曲线。该听反应阈曲线与行为测听所观察到的听力曲线近似。     

Comparative visual ecology of cephalopods from different habitats

Previous investigations of vision and visual pigment evolution in aquatic predators have focused on fish and crustaceans, generally ignoring the cephalopods. Since the first cephalopod opsin was sequenced in late 1980s, we now have data on over 50 cephalopod opsins, prompting this functional and phylogenetic examination. Much of this data does not specifically examine the visual pigment spectral absorbance position (λ_max) relative to environment or lifestyle, and cephalopod opsin functional adaptation and visual ecology remain largely unknown. Here we introduce a new protocol for photoreceptor microspectrophotometry (MSP) that overcomes the difficulty of bleaching the bistable visual pigment and that reveals eight coastal coleoid cephalopods to be monochromatic with λ_max varying from 484 to 505 nm. A combination of current MSP results, the λ_max values previously characterized using cephalopod retinal extracts (467–500 nm) and the corresponding opsin phylogenetic tree were used for systematic comparisons with an end goal of examining the adaptations of coleoid visual pigments to different light environments. Spectral tuning shifts are described in response to different modes of life and light conditions. A new spectral tuning model suggests that nine amino acid substitution sites may determine the direction and the magnitude of spectral shifts.     

Energy efficient walking with central pattern generators: from passive dynamic walking to biologically inspired control

Like human walking, passive dynamic walking—i.e. walking down a slope with no actuation except gravity—is energy efficient by exploiting the natural dynamics. In the animal world, neural oscillators termed central pattern generators (CPGs) provide the basic rhythm for muscular activity in locomotion. We present a CPG model, which automatically tunes into the resonance frequency of the passive dynamics of a bipedal walker, i.e. the CPG model exhibits resonance tuning behavior. Each leg is coupled to its own CPG, controlling the hip moment of force. Resonance tuning above the endogenous frequency of the CPG—i.e. the CPG’s eigenfrequency—is achieved by feedback of both limb angles to their corresponding CPG, while integration of the limb angles provides resonance tuning at and below the endogenous frequency of the CPG. Feedback of the angular velocity of both limbs to their corresponding CPG compensates for the time delay in the loop coupling each limb to its CPG. The resonance tuning behavior of the CPG model allows the gait velocity to be controlled by a single parameter, while retaining the energy efficiency of passive dynamic walking.     

A review on directional information in neural signals for brain-machine interfaces

Brain-machine interfaces (BMIs) can be characterized by the technique used to measure brain activity and by the way different brain signals are translated into commands that control an effector. We give an overview of different approaches and focus on a particular BMI approach: the movement of an artificial effector (e.g. arm prosthesis to the right) by those motor cortical signals that control the equivalent movement of a corresponding body part (e.g. arm movement to the right). This approach has been successfully applied in monkeys and humans by accurately extracting parameters of movements from the spiking activity of multiple single-units. Here, we review recent findings showing that analog neuronal population signals, ranging from intracortical local field potentials over epicortical ECoG to non-invasive EEG and MEG, can also be used to decode movement direction and continuous movement trajectories. Therefore, these signals might provide additional or alternative control for this BMI approach, with possible advantages due to reduced invasiveness.     

Combined strategy for preparation of a bioimprinted Geotrichum sp. lipase biocatalyst effective in non-aqueous media

Geotrichum sp. lipase with enhanced activity and operational stability was prepared for use in non-aqueous media. A combined strategy comprising bioimprinting with dual imprint molecules and a co-solvent coupled to pH tuning, KCl salt activation, lecithin coating and immobilization on macroporous resin effectively enhanced the activity and operational stability of Geotrichum sp. lipase. The modified lipase exhibited 18.4-fold enhanced esterification activity towards methyl oleate synthesis, and retained 90% activity following repeated use in 10 cycles. The combined strategy exhibited a significant synergistic effect and was suitable for lipase modification, dramatically enhancing the enzyme activity and operational stability. This approach is applicable to the preparation of other enzyme biocatalysts, since the methods are effective for upgrading crude enzyme to a refined product with high activity and stability for use in non-aqueous media.     

Heterogeneity of muscle activation in relation to force direction: A multi-channel surface electromyography study on the triceps surae muscle

Several skeletal muscles can be divided into sub-modules, called neuromuscular compartments (NMCs), which are thought to be controlled independently and to have distinct biomechanical functions. We looked for distinct muscle activation patterns in the triceps surae muscle (TS) using surface electromyography (EMG) during voluntary contraction. Nine subjects performed isometric and isotonic plantar flexions combined with forces along pre-defined directions. Besides the forces under the ball of the foot, multi-channel surface EMG was measured with electrodes homogeneously distributed over the entire TS. Using principal component analysis, common (global) components were omitted from the EMG signals, thereby estimating muscle activity sufficiently accurate to track fine fluctuations of force during an isotonic contraction (r = 0.80 ± 0.09). A subsequent cluster analysis showed a topographical organization of co-activated parts of the muscle that was different between subjects. Low and negative correlations between the EMG activity within clusters were found, indicating a substantial heterogeneity of TS activation. The correlations between cluster time series and forces at the foot in specific directions differed substantially between clusters, showing that the differentially activated parts of the TS had specific biomechanical functions.     

Neural Coding of Finger and Wrist Movements

Previous work (Schieber and Hibbard, 1993) has shown that single motor cortical neurons do not discharge specifically for a particular flexion-extension finger movement but instead are active with movements of different fingers. In addition, neuronal populations active with movements of different fingers overlap extensively in their spatial locations in the motor cortex. These data suggested that control of any finger movement utilizes a distributed population of neurons. In this study we applied the neuronal population vector analysis (Georgopoulos et al., 1983) to these same data to determine (1) whether single cells are tuned in an abstract, three-dimensional (3D) instructed finger and wrist movement space with hand-like geometry and (2) whether the neuronal population encodes specific finger movements. We found that the activity of 132/176 (75%) motor cortical neurons related to finger movements was indeed tuned in this space. Moreover, the population vector computed in this space predicted well the instructed finger movement. Thus, although single neurons may be related to several disparate finger movements, and neurons related to different finger movements are intermingled throughout the hand area of the motor cortex, the neuronal population activity does specify particular finger movements.     

A Population Density Approach That Facilitates Large-Scale Modeling of Neural Networks: Analysis and an Application to Orientation Tuning

We explore a computationally efficient method of simulating realistic networks of neurons introduced by Knight, Manin, and Sirovich (1996) in which integrate-and-fire neurons are grouped into large populations of similar neurons. For each population, we form a probability density that represents the distribution of neurons over all possible states. The populations are coupled via stochastic synapses in which the conductance of a neuron is modulated according to the firing rates of its presynaptic populations. The evolution equation for each of these probability densities is a partial differential-integral equation, which we solve numerically. Results obtained for several example networks are tested against conventional computations for groups of individual neurons.We apply this approach to modeling orientation tuning in the visual cortex. Our population density model is based on the recurrent feedback model of a hypercolumn in cat visual cortex of Somers et al. (1995). We simulate the response to oriented flashed bars. As in the Somers model, a weak orientation bias provided by feed-forward lateral geniculate input is transformed by intracortical circuitry into sharper orientation tuning that is independent of stimulus contrast.The population density approach appears to be a viable method for simulating large neural networks. Its computational efficiency overcomes some of the restrictions imposed by computation time in individual neuron simulations, allowing one to build more complex networks and to explore parameter space more easily. The method produces smooth rate functions with one pass of the stimulus and does not require signal averaging. At the same time, this model captures the dynamics of single-neuron activity that are missed in simple firing-rate models.