Role of transport performance on neuron cell morphology

The compartmental model is a basic tool for studying signal propagation in neurons, and, if the model parameters are adequately defined, it can also be of help in the study of electrical or fluid transport. Here we show that the input resistance, in different networks which simulate the passive properties of neurons, is the result of an interplay between the relevant conductances, morphology and size. These results suggest that neurons must grow in such a way that facilitates the current flow. We propose that power consumption is an important factor by which neurons attain their final morphological appearance.Comment: 9 pages with 3 figures, submitted to Neuroscience Letter

The Source of Spontaneous Activity in the Main Olfactory Bulb of the Rat

In vivo, most neurons in the main olfactory bulb exhibit robust spontaneous activity. This paper tests the hypothesis that spontaneous activity in olfactory receptor neurons drives much of the spontaneous activity in mitral and tufted cells via excitatory synapses.Single units were recorded in vivo from the main olfactory bulb of a rat before, during, and after application of lidocaine to the olfactory nerve. The effect of lidocaine on the conduction of action potentials from the olfactory epithelium to the olfactory bulb was assessed by electrically stimulating the olfactory nerve rostral to the application site and monitoring the field potential evoked in the bulb.Lidocaine caused a significant decrease in the amplitude of the olfactory nerve evoked field potential that was recorded in the olfactory bulb. By contrast, the lidocaine block did not significantly alter the spontaneous activity of single units in the bulb, nor did it alter the field potential evoked by electrical stimulation of the lateral olfactory tract. Lidocaine block also did not change the temporal patters of action potential or their synchronization with respiration.Spontaneous activity in neurons of the main olfactory bulb is not driven mainly by activity in olfactory receptor neurons despite the extensive convergence onto mitral and tufted cells. These results suggest that spontaneous activity of mitral and tufted is either an inherent property of these cells or is driven by centrifugal inputs to the bulb

Automatic Differentiation: Point And Interval

e applied. In summary, AD is more accurate than numerical differentiation and requires fewer resources and is more generally applicable than symbolic differentiation. The simplest type of algorithmic definition of a function is a code list , which is similar to the segment of computer code for the evaluation of an expression (i.e., a formula). For illustration, consider the function defined by the formula f(x; y) = (xy + sin x + 4)(3y 2 + 6): An equivalent algorithmic definition of this function by a code list is t 1 = x; t 6 = t 5 + 4; t 2 = y; t 7 = t 2 2 ; t 3 = t 1 t 2 ; t 8 = 3t 7 ; t 4 =

Reverse accumulation and implicit functions

Original article can be found at: http://www.informaworld.com/smpp/title~content=t713645924~db=all Copyright Taylor and Francis / Informa.We begin by introducing a simple technique for using reverse accumulation the first derivatives of target functions which include in their construction the solution of systems of linear or nonlinear equations. In the linear case solving Ay= b for y corresponds to the adjoint operations [...] where vis the solution to the adjoint equations vA=y. A more sophisticated construction applies in the nonlinear case. We apply these technique to obtain automatic numerical error estimates for calculated function values. These error estimates include the effects of inaccurate equation solution as well as rounding error. Our basic techniques can be generalized to functions which contain several (linear or nonlinear) implicit functions in their construction, either serially or nested. In the case of scalar-valued target functions that include equation solution as part of their construction. Our algorithms involve at most the same order of computational effort as the computation of the target function value, regardless of the number of independent variables or the size of the systems of equations.Peer reviewe