The statistical relationship between connectivity and neural activity in fractionally connected feed-forward networks.

It is desirable to have a statistical description of neuronal connectivity in developing tractable theories on the development of biological neural networks and in designing artificial neural networks. In this paper, we bring out a relationship between the statistics of the input environment, the degree of network connectivity, and the average postsynaptic activity. These relationships are derived using simple neurons whose inputs are only feed-forward, excitatory and whose activity is a linear function of its inputs.

The influence of limited presynaptic growth and synapse removal on adaptive synaptogenesis.

This report continues our research into the effectiveness of adaptive synaptogenesis in constructing feed-forward networks which perform good transformations on their inputs. Good transformations are characterized by the maintenance of input information and the removal of statistical dependence. Adaptive synaptogenesis stochastically builds and sculpts a synaptic connectivity in initially unconnected networks using two mechanisms.

Adaptive synaptogenesis constructs networks that maintain information and reduce statistical dependence.

This report demonstrates the effectiveness of two processes in constructing simple feedforward networks which perform good transformations on their inputs. Good transformations are characterized by the minimization of two information measures: the information loss incurred with the transformation and the statistical dependency of the output. The two processes build appropriate synaptic connections in initially unconnected networks.

Information maintenance and statistical dependence reduction in simple neural networks.

This study compares the ability of excitatory, feed-forward neural networks to construct good transformations on their inputs. The quality of such a transformation is judged by the minimization of two information measures: the information loss of the transformation and the statistical dependency of the output. The networks that are compared differ from each other in the parametric properties of their neurons and in their connectivity.

Purification and characterization of the voltage-dependent anion channel from the outer mitochondrial membrane of yeast.

The outer mitochondrial membranes of all organisms so far examined contain a protein which forms voltage-dependent anion selective channels (VDAC) when incorporated into planar phospholipid membranes. Previous reports have suggested that the yeast (Saccharomyces cerevisiae) outer mitochondrial membrane component responsible for channel formation is a protein of 29,000 daltons which is also the major component of this membrane. In this report, we describe the purification of this 29,000-dalton protein to virtual homogeneity from yeast outer mitochondrial membranes.