Neurons in the brain communicate by short electrical pulses, the so-called action potentials or spikes. How can we understand the process of spike generation? How can we understand information transmission by neurons? What happens if thousands of neurons are coupled together in a seemingly random network? How does the network connectivity determine the activity patterns? And, vice versa, how does the spike activity influence the connectivity pattern? These questions are addressed in this 2002 introduction to spiking neurons aimed at those taking courses in computational neuroscience, theoretical biology, biophysics, or neural networks. The approach will suit students of physics, mathematics, or computer science; it will also be useful for biologists who are interested in mathematical modelling. The text is enhanced by many worked examples and illustrations. There are no mathematical prerequisites beyond what the audience would meet as undergraduates: more advanced techniques are introduced in an elementary, concrete fashion when needed.

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Spiking Neuron Models: Single Neurons, Populations, Plasticity

We have measured the spatial density of cones and rods in eight whole-mounted human retinas, obtained from seven individuals between 27 and 44 years of age, and constructed maps of photoreceptor density and between-individual variability. The average human retina contains 4.6 million cones (4.08-5.29 million). Peak foveal cone density averages 199,000 cones/mm2 and is highly variable between individuals (100,000-324,000 cones/mm2). The point of highest density may be found in an area as large as 0.032 deg2. Cone density falls steeply with increasing eccentricity and is an order of magnitude lower 1 mm away from the foveal center. Superimposed on this gradient is a streak of high cone density along the horizontal meridian. At equivalent eccentricities, cone density is 40-45% higher in nasal compared to temporal retina and slightly higher in midperipheral inferior compared to superior retina. Cone density also increases slightly in far nasal retina. The average human retina contains 92 million rods (77.9-107.3 million). In the fovea, the average horizontal diameter of the rod-free zone is 0.350 mm (1.25 degrees). Foveal rod density increases most rapidly superiorly and least rapidly nasally. The highest rod densities are located along an elliptical ring at the eccentricity of the optic disk and extending into nasal retina with the point of highest density typically in superior retina (5/6 eyes). Rod densities decrease by 15-25% where the ring crosses the horizontal meridian. Rod density declines slowly from the rod ring to the far periphery and is highest in nasal and superior retina. Individual variability in photoreceptor density differs with retinal region and is similar for both cones and rods. Variability is highest near the fovea, reaches a minimum in the midperiphery, and then increases with eccentricity to the ora serrata. The total number of foveal cones is similar for eyes with widely varying peak cone density, consistent with the idea that the variability reflects differences in the lateral migration of photoreceptors during development. Two fellow eyes had cone and rod numbers within 8% and similar but not identical photoreceptor topography.

Human photoreceptor topography

I. Introduction II. History of the Incretin Concept: Discovery of Gastric Inhibitory Polypeptide III. Discovery of GLP-1 IV. Structures of GLPs and Family of Glucagon-Related Peptides V. Tissue Distribution of the Expression of GLPs A. Pancreatic α-cells B. Intestinal L cells C. Central nervous system VI. Proglucagon Biosynthesis A. Organization/structure of the proglucagon gene B. Regulation of glucagon gene expression C. Posttranslational processing of proglucagon VII. Regulation of GLP Secretion A. Overview B. Intracellular signals C. Carbohydrates D. Fats E. Proteins F. Endocrine G. Neural H. GLP-2 VIII. Metabolism of GLPs A. GLP-1 B. GLP-2 IX. Physiological Actions of GLPs A. Overview B. Pancreatic islets C. Counterregulatory actions of GLP-1 and leptin on β-cells D. Stomach E. Lung F. Brain G. Liver, skeletal muscle, and fat H. Pituitary, hypothalamus, and thyroid I. Cardiovascular system J. GLP-2 X. GLP Receptors A. Structure B. Signaling C. Distribution D. Regulation E. GLP-2 XI. Pathophysiology o...

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The glucagon-like peptides.

Suppression of ICE and Apoptosis in Mammary Epithelial Cells by Extracellular Matrix Nancy Boudreau,* Carolyn J. Sympson, Zena Werb, Mina J. Bissell N. Boudreau and M. J. Bissell Life Sciences Division, Lawrence Berkeley Laboratory 1 Cyclotron Road, Building 83, Berkeley, CA 94720, USA. C. J. Sympson Life Sciences Division, Lawrence Berkeley Laboratory 1 Cyclotron Road, Building 83, Berkeley, CA 94720, USA Laboratory of Radiobiology and Environmental Health University of California, San Francisco, CA 94143, USA. Z. Werb Laboratory of Radiobiology and Environmental Health University of California, San Francisco, CA 94143, USA. *To whom correspondence should be addressed. LBNL/DOE funding & contract number: DE-AC02-05CH11231 DISCLAIMER This document was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor The Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or The Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or The Regents of the University of California.

Suppression of ICE and Apoptosis in Mammary Epithelial Cells by Extracellular Matrix

Information theory quantifies how much information a neural response carries about the stimulus This can be compared to the information transferred in particular models of the stimulus-response function and to maximum possible information transfer Such comparisons are crucial because they validate assumptions present in any neurophysiological analysis Here we review information-theory basics before demonstrating its use in neural coding We show how to use information theory to validate simple stimulus-response models of neural coding of dynamic stimuli Because these models require specification of spike timing precision, they can reveal which time scales contain information in neural coding This approach shows that dynamic stimuli can be encoded efficiently by single neurons and that each spike contributes to information transmission We argue, however, that the data obtained so far do not suggest a temporal code, in which the placement of spikes relative to each other yields additional information

/pdf/information-theory-and-neural-coding-3onlqdfcw0.pdf

Information theory and neural coding.

Spectral analysis provides powerful techniques for describing the lower order moments of a stochastic process and interactions between two or more stochastic processes. A major problem in the application of spectral analysis to neuronal spike trains is how to obtain equispaced samples of the spike trains which will give unbiased and alias-free spectral estimates. Various sampling methods, which treat the spike train as a continuous signal, a point process and as a series of Dirac delta-functions, are reviewed and their limitations discussed. A new sampling technique, which gives unbiased and alias-free estimates, is described. This technique treats the spike train as a series of delta functions and generates samples by digital filtering. Implementation of this technique on a small computer is simple and virtually on-line.

Alias-free sampling of neuronal spike trains

Information processing by graded-potential transmission through tonically active synapses

New methods for analysing motor function in man and animals.

The Frequency Response, Coherence, and Information Capacity of Two Neuronal Models

A new method is presented for the measurement of the Wiener kernels of a non-linear system. The method uses the complex exponential functions as a set of orthogonal functions with which to expand the kernels. The fast Fourier transform is then employed in the most efficient measurement of the kernels so far discovered.

Andrew S. French

Papers

Alias-free sampling of neuronal spike trains

Information processing by graded-potential transmission through tonically active synapses

New methods for analysing motor function in man and animals.

The Frequency Response, Coherence, and Information Capacity of Two Neuronal Models

Measuring the Wiener kernels of a non-linear system using the fast Fourier transform algorithm†