Do Biological Constraints Impair Dendritic Computation?

Computations on the dendritic trees of neurons have important constraints. Voltage dependent conductances in dendrites are not similar to arbitrary direct-current generation, they are the basis for dendritic nonlinearities and they do not allow converting positive currents into negative currents. While it has been speculated that the dendritic tree of a neuron can be seen as a multi-layer neural network and it has been shown that such an architecture could be computationally strong, we do not know if that computational strength is preserved under these biological constraints. Here we simulate models of dendritic computation with and without these constraints. We find that dendritic model performance on interesting machine learning tasks is not hurt by these constraints but may benefit from them. Our results suggest that single real dendritic trees may be able to learn a surprisingly broad range of tasks.

Might a Single Neuron Solve Interesting Machine Learning Problems Through Successive Computations on Its Dendritic Tree?

Physiological experiments have highlighted how the dendrites of biological neurons can nonlinearly process distributed synaptic inputs. However, it is unclear how aspects of a dendritic tree, such as its branched morphology or its repetition of presynaptic inputs, determine neural computation beyond this apparent nonlinearity. Here we use a simple model where the dendrite is implemented as a sequence of thresholded linear units. We manipulate the architecture of this model to investigate the impacts of binary branching constraints and repetition of synaptic inputs on neural computation. We find that models with such manipulations can perform well on machine learning tasks, such as Fashion MNIST or Extended MNIST. We find that model performance on these tasks is limited by binary tree branching and dendritic asymmetry and is improved by the repetition of synaptic inputs to different dendritic branches. These computational experiments further neuroscience theory on how different dendritic properties might determine neural computation of clearly defined tasks.

DNA methylation biomarkers prospectively predict both antenatal and postpartum depression.

We sought to replicate and expand upon previous work demonstrating antenatal TTC9B and HP1BP3 gene DNA methylation is prospectively predictive of postpartum depression (PPD) with ~80% accuracy. In a preterm birth study from Emory, Illumina MethylEPIC microarray derived 1st but not 3rd trimester biomarker models predicted 3rd trimester Edinburgh Postnatal Depression Scale (EPDS) scores ≥ 13 with an AUC=0.8 (95% CI: 0.63-0.8). Bisulfite pyrosequencing derived biomarker methylation was generated using bisulfite pyrosequencing across all trimesters in a pregnancy cohort at UC Irvine and in 3rd trimester from an independent Johns Hopkins pregnancy cohort. A support vector machine model incorporating 3rd trimester EPDS scores, TTC9B, and HP1BP3 methylation status predicted 4 week to 6 week postpartum EPDS ≥ 13 from 3rd trimester blood in the UC Irvine cohort (AUC=0.78, 95% CI: 0.64-0.78) and from the Johns Hopkins cohort (AUC=0.84, 95% CI: 0.72-0.97), both independent of previous psychiatric diagnosis. Technical replicate predictions in a subset of the Johns Hopkins cohort exhibited strong cross experiment correlation. This study confirms the PPD prediction model has the potential to be developed into a clinical tool enabling the identification of pregnant women at future risk of PPD who may benefit from clinical intervention.

Quantifying the role of neurons for behavior is a mediation question.

Many systems neuroscientists want to understand neurons in terms of mediation; we want to understand how neurons are involved in the causal chain from stimulus to behavior. Unfortunately, most tools are inappropriate for that while our language takes mediation for granted. Here we discuss the contrast between our conceptual drive toward mediation and the difficulty of obtaining meaningful evidence.

On Functions Computed on Trees.

Any function can be constructed using a hierarchy of simpler functions through compositions. Such a hierarchy can be characterized by a binary rooted tree. Each node of this tree is associated with a function that takes as inputs two numbers from its children and produces one output. Since thinking about functions in terms of computation graphs is becoming popular, we may want to know which functions can be implemented on a given tree. Here, we describe a set of necessary constraints in the form of a system of nonlinear partial differential equations that must be satisfied. Moreover, we prove that these conditions are sufficient in contexts of analytic and bit-valued functions. In the latter case, we explicitly enumerate discrete functions and observe that there are relatively few. Our point of view allows us to compare different neural network architectures in regard to their function spaces. Our work connects the structure of computation graphs with the functions they can implement and has potential applications to neuroscience and computer science.

DNA methylation and expression of KCNQ3 in bipolar disorder.

OBJECTIVES: Accumulating evidence implicates the potassium voltage-gated channel, KQT-like subfamily, member 2 and 3 (KCNQ2 and KCNQ3) genes in the etiology of bipolar disorder (BPD). Reduced KCNQ2 or KCNQ3 gene expression might lead to a loss of inhibitory M-current and an increase in neuronal hyperexcitability in disease. The goal of the present study was to evaluate epigenetic and gene expression associations of the KCNQ2 and KCNQ3 genes with BPD. METHODS: DNA methylation and gene expression levels of alternative transcripts of KCNQ2 and KCNQ3 capable of binding the ankyrin G (ANK3) gene were evaluated using bisulfite pyrosequencing and the quantitative real-time polymerase chain reaction in the postmortem prefrontal cortex of subjects with BPD and matched controls from the McLean Hospital. Replication analyses of DNA methylation findings were performed using prefrontal cortical DNA obtained from the Stanley Medical Research Institute. RESULTS: Significantly lower expression was observed in KCNQ3, but not KCNQ2. DNA methylation analysis of CpGs within an alternative exonic region of KCNQ3 exon 11 demonstrated significantly lower methylation in BPD, and correlated significantly with KCNQ3 mRNA levels. Lower KCNQ3 exon 11 DNA methylation was observed in the Stanley Medical Research Institute replication cohort, although only after correcting for mood stabilizer status. Mood stabilizer treatment in rats resulted in a slight DNA methylation increase at the syntenic KCNQ3 exon 11 region, which subsequent analyses suggested could be the result of alterations in neuronal proportion. CONCLUSION: The results of the present study suggest that epigenetic alterations in the KCNQ3 gene may be important in the etiopathogenesis of BPD and highlight the importance of controlling for medication and cellular composition-induced heterogeneity in psychiatric studies of the brain.