Many organism behaviors are innate or instinctual and have been “hard-coded” through evolution. Current approaches to understanding these behaviors model evolution as an optimization problem in which the traits of organisms are assumed to optimize an objective function representing evolutionary fitness. Here, we use a mechanistic birth-death dynamics approach to study the evolution of innate behavioral strategies in a simulated population of organisms. In particular, we performed agent-based stochastic simulations and mean-field analyses of organisms exploring random environments and competing with each other to find locations with plentiful resources. We find that when organism density is low, the mean-field model allows us to derive an effective objective function, predicting how the most competitive phenotypes depend on the exploration-exploitation trade-off between the scarcity of high-resource sites and the increase in birth rate those sites offer organisms. However, increasing organism density alters the most competitive behavioral strategies and precludes the derivation of a well-defined objective function. Moreover, there exists a range of densities for which the coexistence of many phenotypes persists for evolutionarily long times.

Statistically inferring neuronal connections from observed spike train data is a standard procedure for understanding the structure of the underlying neural circuits. However, the inferred connections seldom reflect true synaptic connections, being skewed by factors such as model mismatch, unobserved neurons, and limited data. On the other hand, spike train covariances, sometimes referred to as "functional connections," make no assumption of the underlying neuron models and provide a straightforward way to quantify the statistical relationships between pairs of neurons. The main drawback of functional connections compared to statistically inferred connections is that the former are not causal, whereas statistically inferred connections are often constrained to be. However, we show in this work that the inferred connections in spontaneously active networks modeled by generalized linear point process models strongly reflect covariances between neurons, not causal information. We investigate this relationship between the neuronal connections inferred with model-matched maximum likelihood inference and the corresponding spike train covariance in a nonlinear spiking neural network model. Strong correlations between inferred neuronal connections and spike train covariances are observed when many neurons are unobserved or when neurons are weakly coupled. This phenomenon occurs across different network structures, including random networks and balanced excitatory-inhibitory networks. A theoretical analysis of maximum likelihood solutions in analytically tractable cases elucidates how the inferred filters relate to ground-truth covariances of the neurons, and opens the door for future investigations.

Many organism behaviors are innate or instinctual and have been "hard-coded" through evolution. Current approaches to understanding these behaviors model evolution as an optimization problem in which the traits of organisms are assumed to optimize an objective function representing evolutionary fitness. Here, we use a mechanistic birth-death dynamics approach to study the evolution of innate behavioral strategies in a population of organisms in silico. In particular, we performed agent-based stochastic simulations and mean-field analyses of organisms exploring random environments and competing with each other to find locations with plentiful resources. We find that when organism density is low, the mean-field model allows us to derive an effective objective function, predicting how the most competitive phenotypes depend on the exploration-exploitation trade-off between the scarcity of high-resource sites and the increase in birth rate those sites offer organisms. However, increasing organism density alters the most competitive behavioral strategies and precludes the existence of a well-defined objective function. Moreover, there exists a range of densities for which the coexistence of many phenotypes persists for evolutionarily long times.