Descriptions of physical properties of visible surfaces, such as their distance and the presence of edges, must be recovered from the primary image data. Computational vision aims to understand how such descriptions can be obtained from inherently ambiguous and noisy data. A recent development in this field sees early vision as a set of ill-posed problems, which can be solved by the use of regularization methods. These lead to algorithms and parallel analog circuits that can solve ‘ill-posed problems’ and which are suggestive of neural equivalents in the brain.

Computational vision and regularization theory

The sections in this article are:


1
Biomechanical and Electromyographical Information
1.1
Single Limb During Locomotion

1.2
Interlimb Coordination

1.3
Treadmill Versus Overground Locomotion

1.4
Trunk Movements During Locomotion

1.5
Pathological Gaits

1.6
Summary




2
Neural Generation of “Basic Locomotor Synergy”
2.1
Central Versus Peripheral

2.2
Parts of CNS of Primary Importance for Neural Control of Basic Locomotor Synergy

2.3
Spinal Centers for Locomotion—Behavioral Results

2.4
Reflex Control of Basic Locomotor Synergy

2.5
Activity in Certain Spinal, Cerebellar, and Brain Stem Neurons and in Certain Reflex Pathways During Locomotion

2.6
Cerebellum and Locomotion

2.7
Initiation of Locomotion—Brain Stem Circuitry

2.8
Central Organization of Spinal Pattern Generation

2.9
Possible Rhythm-Generating Mechanisms and Models—Facts and Fiction

2.10
Developmental Aspects

2.11
Summary




3
Adapting Basic Locomotor Synergy to Animal's Needs
3.1
Changing Speed

3.2
Goal-Directed Locomotion—Turning and Walking Along Curvatures

3.3
Modifications of “Locomotor Posture”

3.4
Positioning of Limb in Each Step

3.5
Reflex Adaptation of Step

3.6
Summary




4
Concluding Remarks

Control of Locomotion in Bipeds, Tetrapods, and Fish

I. We have studied the central and peripheral functions of the metacerebral cell (MCC) ofAplysia. This research concentrated on the role of the MCC in controlling the ARC muscle, which is representative of a group of muscles that effect the biting and swallowing response of Aplysia. 2. Firing of the MCC produced a slowly rising and slowly decaying excitatory synaptic potential in motoneurons of the ARC muscle and other muscles of the buccal mass. The excitatory synaptic potential was not sufficient to fire motoneurons unless they received another source of depolarization. 3. In isolated buccal ganglia that exhibit periodic burst output, firing of the MCC at low, physiological rates, increased the frequency of occurrence of the burst output. Firing of the MCC was never effective in eliciting sustained burst output from nonbursting ganglia. 4. Although the MCC sends axonal branches to nerves innervating the accessory radula closer (ARC) and other muscles, firing of the MCC produced neither an increase nor decrease of the resting tension of the ARC muscle. Furthermore, firing of the ARC was never associated with any direct depolarizing or hyperpolarizing responses in muscle fibers. 5. Brief firing of the MCC enhanced the muscle contraction produced by firing of ARC motoneurons. The enhancement of contraction was maximum approximately 30 s after termination of a brief burst of MCC spikes (e.g., 30 spikes at 5/s), and the process then gradually decayed over a period of a minute or more. Physiological rates of MCC firing (see below) also enhanced contraction of ARC muscles. 6. The above data suggested that the MCCs might be involved in feeding behavior. To explore this hypothesis, chronic extracellular recordings from a MCC were obtained while ani-

Modulatory control of buccal musculature by a serotonergic neuron (metacerebral cell) in Aplysia

Electrical properties of tissues, macromolecular solutions, and cell membranes are summarized at frequencies from the extra low frequency (ELF) to microwave range. Previously presented dielectric data are supplemented by new results and a more detailed discussion of the physical mechanisms for the observed temperature coefficients of the dielectric properties. The dielectric data are discussed in terms of the interaction mechanisms which give rise to observed relaxational effects. Possible mechanisms for nonthermal weak interactions between radio-frequency (RF) energy and tissues are discussed and evaluated.

RF-field interactions with biological systems: Electrical properties and biophysical mechanisms

In contrast to well-studied through-protection neurons that propagate information from one region to another in the central nervous system, short-axon or axonless neurons form local circuits, transmitting signals through synapses and electrical junctions between their dendrites. Interaction in this dendritic network proceeds without spike action potentials. Interaction is mediated by graded electrotonic changes of potential and is transmitted through high sensitivity (submillivolt threshold) synapses rather than by lower sensitivity (20 to 100-mv threshold) synapses typical of projection neurons. A crucial feature of local circuits is their high degree of interaction both through specialized junctional structures and through the extracellular fields generated by local and more distant brain regions. The anatomical evidence for the nature and distribution of neuronal local circuits in the nervous system is surveyed. Bioelectric mechanisms are discussed in relation to the special properties of local circuits, including dendrodendritic synapses, synaptic sensitivity, electrotonic coupling, and field effects. Intraneuronal and interneuronal transport of various types of substances suggests that the biochemical and the bioelectrical parameters are functionally interwoven. Through such interactions neuronal local circuits, with their distinctive properties, may play an essential role in higher brain function.

RO Schmitt

Papers

Electrotonic processing of information by brain cells