Connectivity of random nets

The weak connectivity γ of a random net is defined and computed by an approximation method as a function ofa, the axone density. It is shown that γ rises rapidly witha, attaining 0.8 of its asymptotic value (unity) fora=2, where the number of neurons in the net is arbitrarily large. The significance of this parameter is interpreted also in terms of the maximum expected spread of an epidemic under certain conditions.     

Cycle distributions in random nets

Characteristics of random nets are derived from assumptions concerning the distribution of connections. The single aggregate of neurons with random connections without branching and two parallel chains with normal distribution of connections are considered. The cycle saturation is derived for each type of net, that is, the fraction of neurons which are members of cycles. It is shown that in the single aggregate with random connections, the cycle saturation varies inversely as the square root of the number of neurons; in the dense two-chain net it varies inversely as the square root of the neuron density and inversely on the square root of the standard deviation of the normal distribution.     

Steady states in random nets: II

Two semi-infinite chains are considered interacting as random nets. Conditions for steady state are derived for the cases of cross-excitatory and cross-inhibitory association connections. In the cross-inhibitory case a unique non-trivial self-reproducing steady state is shown to exist.     

Beyond the binary case in random nets

Experiments on random binary, ternary, etc. (P=2, 3,…, 10) switching nets are reported. Behavioral cycle lengths are examined as functions of output variety,P, input connectance,K, and net size,N. Overall, output variety appears an influential, well-behaved net property. Strong, but well-behaved interactions appear among net variables. In high connectance nets, median cycle length grows approx. asP ^N/2. Other factors constant, one-connected nets show the shortest cycles, and connectance effects appear to converge asymptotically aroundN. Data for cycle length as a function of net size suggest a concavity not compatible with the Kauffman “square root law” (Kauffman, 1969). Evidence of a positive relationship between cycle length and run-in length is found in two-input nets; weaker evidence is obtained that in higher connectance nets this relationship becomes negative in sign. The “modular complexity” ofP>2 nets is examined briefly.     

Steady states in random nets: I

A neural net is taken to consist of a semi-infinite chain of neurons with connections distributed according to a certain probability frequency of the lengths of the axones. If an input of excitation is “fed” into the net from an outside source, the statistical properties of the net determine a certain steady state output. The general functional relation between the input and the output is derived as an integral equation. For a certain type of probability distribution of connections, this equation is reducible to a differential equation. The latter can be solved by elementary methods for the output in terms of the input in general and for the input in terms of the output in special cases.     

Mathematical biophysics of some neural nets

Methods are considered for constructing neural nets of the McCulloch-Pitts type giving arbitrary time delays between stimuli and their responses. By introducing cycles of neurons, the problem is treated from the standpoint of economizing on the number of neurons required. A generalization is effect that will realize any unique temporal response pattern from a unique stimulus.     

Contribution to the theory of random and biased nets

The probabilistic theory of random and biased nets is further developed by the “tracing” method treated previously. A number of biases expected to be operating in nets, particularly in sociograms, is described. Distribution of closed chain lengths is derived for random nets and for nets with a simple “reflexive” bias. The “island model” bias is treated for the case of two islands and a single axon tracing, resulting in a pair of linear difference equations with two indices. The reflexive bias is extended to multiple-axon tracing by an approximate method resulting in a modification of the random net recursion formula. Results previously obtained are compared with empirical findings and attempts are made to account for observed discrepancies.     

The group structure of some neural nets

A method is given for using a set of disjoint cycles as the main body of a neural net that will given rise to more than one temporal response pattern for different afferent stimuli. The method arises out of considering the correspondence between this type of net and certain Abelian groups of finite order. The consideration also gives rise to a possible definition of the “complexity” of this type of neural net.     

A note on the problem of cycles in random nets

The problem of cycles in random neural nets is considered. An approximation is derived giving the expected numberE(k, a, n) of cycles of lengthk of which a neuron in a random net will be a member. The approximation is further simplifed for the casen> k. In this caseE(k, a, n)=a ^k/n , where (a) is the axone density of the net.     

On matrices of neural nets

The structure of a complete or incomplete neural net is represented here by several matrices. The activity equation of the net follows in a general form. A chain or cycle is defined as a neural structure whose connection matrix is unitary. We can compute the number of simple chains by a recurrent formula. Academia Sinica     

An exact method for the computation of the connectivity of random nets

The problem of finding the “weak connectivity” of a random net is reduced to one involving a Markov process. This provides a mathematically exact treatment of the problem which had previously been treated by an approximation, whose justification was not rigorous. The exact method allows in principle not only the calculation of the “weak connectivity”, but also of the “strong connectivity”, and, in general, the probability that from a randomly selected neuron in the net there exist paths to a specified number of neurons. The computations become exceedingly involved for large nets.     

On feasible random ecosystems

Only a fraction of the total number of equilibrium solutions of the differential system which describes the populations of species in an ecosystem are feasible: that is only a fraction of the solutions give non-negative values for all the populations. The feasible equilibrium solutions of a generalized logistic equation are considered for the limiting cases of strongly and weakly interacting species. It is found that only about one third of all the possible species are generally expected to be present (i.e. to have non-zero populations) at any given equilibrium solution.     

On random minimization of functions

A non-deterministic minimization algorithm recently proposed is analyzed. Some characteristics are analytically derived from the analysis of positive definite quadratic forms. An improvement is proposed and compared with the basic algorithm. Different variants of the basic algorithm are finally compared to a standard Conjugate Gradient minimization algorithm in the computation of the Rayleigh coefficient of a positive definite symmetric matrix.