CoMuS: simulating coalescent histories and polymorphic data from multiple species.

The simultaneous analysis of intra- and interspecies variation is challenging mainly because our knowledge about patterns of polymorphisms where both intra- and interspecies samples coexist is limited. In this study, we present CoMuS (Coalescent of Multiple Species), a multispecies coalescent software that can simulate intra- and interspecies polymorphisms. CoMuS supports a variety of speciation models and demographic scenarios related to the history of each species. In CoMuS, speciation can be accompanied by either instant or gradual isolation between sister species. Sampling may also occur in the past, and thus, we can study simultaneously extinct and extant species. Our software supports both the infinite- and the finite-site model, with substitution rate heterogeneity among sites and a user-defined proportion of invariable sites. We demonstrate the usage of CoMuS in various applications: species delimitation, software testing, model selection and parameter inference involving present-day and ancestral samples, comparison between gradual and instantaneous isolation models, estimation of speciation time between human and chimpanzee using both intra- and interspecies variation. We expect that CoMuS will be particularly useful for studies where species have been separated recently from their common ancestor and phenomena such as incomplete lineage sorting or introgression still occur.

Impact of active dendrites and structural plasticity on the memory capacity of neural tissue.

We consider the combined effects of active dendrites and structural plasticity on the storage capacity of neural tissue. We compare capacity for two different modes of dendritic integration: (1) linear, where synaptic inputs are summed across the entire dendritic arbor, and (2) nonlinear, where each dendritic compartment functions as a separately thresholded neuron-like summing unit. We calculate much larger storage capacities for cells with nonlinear subunits and show that this capacity is accessible to a structural learning rule that combines random synapse formation with activity-dependent stabilization/elimination. In a departure from the common view that memories are encoded in the overall connection strengths between neurons, our results suggest that long-term information storage in neural tissue could reside primarily in the selective addressing of synaptic contacts onto dendritic subunits.

Choice and value flexibility jointly contribute to the capacity of a subsampled quadratic classifier

Biophysical modeling studies have suggested that neurons with active dendrites can be viewed as linear units augmented by product terms that arise from interactions between synaptic inputs within the same dendritic subregions. However, the degree to which local nonlinear synaptic interactions could augment the memory capacity of a neuron is not known in a quantitative sense. To approach this question, we have studied the family of subsampled quadratic classifiers: linear classifiers augmented by the best k terms from the set of K = (d2 + d)/2 second-order product terms available in d dimensions. We developed an expression for the total parameter entropy, whose form shows that the capacity of an SQ classifier does not reside solely in its conventional weight values-the explicit memory used to store constant, linear, and higher-order coefficients. Rather, we identify a second type of parameter flexibility that jointly contributes to an SQ classifier's capacity: the choice as to which product terms are included in the model and which are not. We validate the form of the entropy expression using empirical studies of relative capacity within families of geometrically isomorphic SQ classifiers. Our results have direct implications for neurobiological (and other hardware) learning systems, where in the limit of high-dimensional input spaces and low-resolution synaptic weight values, this relatively little explored form of choice flexibility could constitute a major source of trainable model capacity.