The new Conductivity Bridge derives its bridge source. voltage from a self-contained vacuum tube oscilltor adjusted to approximately 1,000 cycles. Voltage for the amplifier and null indicator tubes is provided by a.builtin D.C. power supply which operates directly from the A.C. power source. 9-324 Conductivity Bridge, without Conductivity Cell, for use with 110 volts 5060 cycle A.C. 9-351.Cosi~uCvity Cell, for use with Conductivity Bridge, constant 0.8, $20.00

The Vertebrate Eye and Its Adaptive Radiation.

The computational brain: Patricia S. Churchland and Terrence J. Sejnowski (MIT Press, Cambridge, MA, 1992); xi, 544 pages, $39.95

http://www.neuroscience.ubc.ca/CourseMat/Kriegstein-Develop_H_Cortx.pdf

Development and Evolution of the Human Neocortex

Amniote phylogeny and the importance of fossils

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.

Simulation of networks of spiking neurons: A review of tools and strategies

This study gives a quantitative description of hypoglossal neurons in the common boa, Constrictor constrictor. In this animal the hypoglossal nuclei are spatially distinct from the ventral horns and do not contain subnuclei. They contain a mean ± S.D. of 942 ± 162 neurons, many of which have double nucleoli. The neurons are shaped like prolate ellipsoids which are oriented with their long axes perpendicular to the ventricle. An average hypoglossal neuron has a major axis of 21.8 μ and a minor axis of 19.6 μ. Although neurons show a considerable range of sizes, neurons of different sizes are not segregated into different regions of the nucleus. Developmental differences in the size, distribution in the medulla, and the percentage of doubly nucleolated neurons occur, but the shape and orientation of hypoglossal neurons do not vary with age.

Quantitative studies on motoneurons: hypoglossal neurons in the boa constrictor, Constrictor constrictor.

The main purpose of this paper is to show how a population of excitatory and inhibitory interconnection of cells, dynamically interacting to produce a wave of activity can encode the input signal that generates the wave. The population considered are from the visual cortex of freshwater turtles and the input signals are visual signals through the retina of the animal. The wave of activity are represented as a 'low dimensional temporal strands' in an appropriate space described. In this paper, the visual input considered are 'light flashes' localized at three different locations of the visual space. Detection of the input location using 'statistical hypothesis testing' has been described as the main result of this paper.

Decoding the position of a visual stimulus from the cortical waves of turtles

Visual stimuli evoke wave activity in the visual cortex of freshwater turtles. Earlier work from our laboratory showed that information about the positions of stationary visual stimuli is encoded in the spatiotemporal dynamics of the waves and that the waves can be decoded using Bayesian detection theory. This paper extends these results in three ways. First, it shows that flashes of light separated in space and time and stimuli moving with three speeds can be discriminated statistically using the waves generated in a large-scale model of the cortex. Second, it compares the coding capabilities of spike rate and spike time codes. Spike rate codes were obtained by low-pass filtering the activities of individual neurons in the model with filters of different band widths. For the moving targets used in the study, detectability using spike rate codes is immune to the choice of a specific bandwidth, indicating that a coarse filter is able to adequately discriminate targets. Spike timing codes are binary sequences indicating the precise timing of spike activity of individual neurons across the cortex. Spike time codes generally perform better than do spike rate codes. Third, the encoding process is examined in terms of the underlying cellular mechanisms that result in the initiation, propagation and cessation of the wave. The period of peak detectability corresponds to the period in which waves are propagating across the cortex

Encoding of Motion Targets by Waves in Turtle Visual Cortex

The optic tectum is a major subdivision of the visual system in reptiles. Previous studies have characterized the laminar pattern, the neuronal populations, and the afferent and efferent connections of the optic tectum in a variety of reptiles. However, little is known about the interactions that occur between neurons within the tectum. This study describes two kinds of interactions that occur between one major class of neurons, the radial cells, in the optic tectum of Pseudemys using Nissl, Golgi and electron microscopic preparations.



Radial cells have somata which bear long, radially oriented apical dendrites from their upper poles and short, basal dendrites from their lower poles. They are divided into two populations on the basis of the distribution of their somata in the tectum. Deep radial cells have somata densely packed in the stratum griseum periventriculare. Their plasma membranes form casual appositions. Middle radial cells have somata scattered throughout the stratum griseum centrale and stratum fibrosum et griseum superficiale and do not contact each other. The apical dendrites of both populations of radial cells participate in vertically oriented, dendritic bundles. The plasma membranes of the dendrites in these bundles form casual appositions in the deeper tectal layers and chemical, dendrodenritic synapses within the stratum fibrosum et griseum superficiale. The synapses have clear, round synaptic vesicles and slightly asymmetric membrane densities. Thus, radial cells interact via both casual appositions and chemical synapses.



These interactions suggest that radial cells may form a basic framework in the tectum. Because both populations of radial cells extend into the stratum fibrosum et griseum superficiale and stratum opticum, they may receive input from some of the same tectal afferent systems. Because the deep radial cells alone have somata and dendrites in the deep tectal layers, they may receive additional inputs that the middle radial cells do not. Neurons in the two populations interact via chemical dendrodendritic synapses, thereby forming vertically oriented modules in the tectum.

Interactions between tectal radial cells in the red-eared turtle, Pseudemys scripta elegans: an analysis of tectal modules.

The spatial distribution and sizes of degenerating hypoglossal neurons were studied following the removal of the genioglossus or hyoglossus muscles or of unilateral glossectomy in common boas with survival times of 7, 14, 21 or 40 days. The spatial organization of this nucleus differs from that of most cranial nerve nuclei in lacking a subnuclear organization: genioglossus and hyoglossus motoneurons are spread evenly throughout the nucleus and intrinsic tongue neurons show only a slight tendency to accumulate in the caudal half of the nucleus. The genioglossus and tongue neurons probably serve both ipsilateral and contralateral muscles with the ipsilateral innervation being largest; the hyoglossus motoneurons probably innervate only ipsilateral muscles. Complementary analyses of the transverse sectional areas of both degenerating and non-degenerating neurons indicates that, although their size ranges overlap, genioglossus motoneurons are small and tongue and hyoglossus motoneurons are progressively larger.

Quantitative studies on motoneurons. II. Spatial and dimensional organization of hypoglossal motoneurons in the boa constrictor, Constrictor constrictor.

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