Empiricists are from Venus, modelers are from Mars: Reconciling experimental and computational approaches in cognitive neuroscience

Mechanisms of memory: An intermediate level of analysis and organization

Research in the last five years has made great strides toward mechanistic explanations of how the brain enables memory. This progress builds upon decades of research from two complementary strands: a Levels of Analysis approach and a Levels of Organization approach. We review how research in cognitive psychology and cognitive neuroscience under these two approaches has recently converged on mechanistic, brain-based theories, couched at the optimal level for explaining cognitive phenomena - the intermediate level. Furthermore, novel empirical and data analysis techniques are now providing ways to test these theories' predictions, a crucial step in unraveling the mechanisms of memory.

Multiscale Modeling of Gene-Behavior Associations in an Artificial Neural Network Model of Cognitive Development

In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction of the whole organism with a variable environment. Given that they have been detected, how do such associations inform cognitive-level theories? To investigate this question, we employed a multiscale computational model of development, using a sample domain drawn from the field of language acquisition. The model comprised an artificial neural network model of past-tense acquisition trained using the backpropagation learning algorithm, extended to incorporate population modeling and genetic algorithms. It included five levels of description-four internal: genetic, network, neurocomputation, behavior; and one external: environment. Since the mechanistic assumptions of the model were known and its operation was relatively transparent, we could evaluate whether cross-level associations gave an accurate picture of causal processes. We established that associations could be detected between artificial genes and behavioral variation, even under polygenic assumptions of a many-to-one relationship between genes and neurocomputational parameters, and when an experience-dependent developmental process interceded between the action of genes and the emergence of behavior. We evaluated these associations with respect to their specificity (to different behaviors, to function vs. structure), to their developmental stability, and to their replicability, as well as considering issues of missing heritability and gene-environment interactions. We argue that gene-behavior associations can inform cognitive theory with respect to effect size, specificity, and timing. The model demonstrates a means by which researchers can undertake multiscale modeling with respect to cognition and develop highly specific and complex hypotheses across multiple levels of description.

A behavioral neuroenergetics theory of ADHD

Energetic insufficiency in neurons due to inadequate lactate supply is implicated in several neuropathologies, including attention-deficit/hyperactivity disorder (ADHD). By formalizing the mechanism and implications of such constraints on function, the behavioral Neuroenergetics Theory (NeT) predicts the results of many neuropsychological tasks involving individuals with ADHD and kindred dysfunctions, and entails many novel predictions. The associated diffusion model predicts that response times will follow a mixture of Wald distributions from the attentive state, and ex-Wald distributions after attentional lapses. It is inferred from the model that ADHD participants can bring only 75-85% of the neurocognitive energy to bear on tasks, and allocate only about 85% of the cognitive resources of comparison groups. Parameters derived from the model in specific tasks predict performance in other tasks, and in clinical conditions often associated with ADHD. The primary action of therapeutic stimulants is to increase norepinephrine in active regions of the brain. This activates glial adrenoceptors, increasing the release of lactate from astrocytes to fuel depleted neurons. The theory is aligned with other approaches and integrated with more general theories of ADHD. Therapeutic implications are explored.