Interactions between the neuromodulatory systems and the amygdala: exploratory survey using the Allen Mouse Brain Atlas.

Neuromodulatory systems originate in nuclei localized in the subcortical region of the brain and control fundamental behaviors by interacting with many areas of the central nervous system. An exploratory survey of the cholinergic, dopaminergic, noradrenergic, and serotonergic receptor expression energy in the amygdala, and in the neuromodulatory areas themselves was undertaken using the Allen Mouse Brain Atlas. The amygdala was chosen because of its importance in cognitive behavior and its bidirectional interaction with the neuromodulatory systems. The gene expression data of 38 neuromodulatory receptor subtypes were examined across 13 brain regions. The substantia innominata of the basal forebrain and regions of the amygdala had the highest amount of receptor expression energy for all four neuromodulatory systems examined. The ventral tegmental area also displayed high receptor expression of all four neuromodulators. In contrast, the locus coeruleus displayed low receptor expression energy overall. In general, cholinergic receptor expression was an order of magnitude greater than other neuromodulatory receptors. Since the nuclei of these neuromodulatory systems are thought to be the source of specific neurotransmitters, the projections from these nuclei to target regions may be inferred by receptor expression energy. The comprehensive analysis revealed many connectivity relations and receptor localization that had not been previously reported. The methodology presented here may be applied to other neural systems with similar characteristics, and to other animal models as these brain atlases become available

A-Lister: a tool for analysis of differentially expressed omics entities across multiple pairwise comparisons.

BackgroundResearchers commonly analyze lists of differentially expressed entities (DEEs), such as differentially expressed genes (DEGs), differentially expressed proteins (DEPs), and differentially methylated positions/regions (DMPs/DMRs), across multiple pairwise comparisons. Large biological studies can involve multiple conditions, tissues, and timepoints that result in dozens of pairwise comparisons. Manually filtering and comparing lists of DEEs across multiple pairwise comparisons, typically done by writing custom code, is a cumbersome task that can be streamlined and standardized.ResultsA-Lister is a lightweight command line and graphical user interface tool written in Python. It can be executed in a differential expression mode or generic name list mode. In differential expression mode, A-Lister accepts as input delimited text files that are output by differential expression tools such as DESeq2, edgeR, Cuffdiff, and limma. To allow for the most flexibility in input ID types, to avoid database installation requirements, and to allow for secure offline use, A-Lister does not validate or impose restrictions on entity ID names. Users can specify thresholds to filter the input file(s) by column(s) such as p-value, q-value, and fold change. Additionally, users can filter the pairwise comparisons within the input files by fold change direction (sign). Queries composed of intersection, fuzzy intersection, difference, and union set operations can also be performed on any number of pairwise comparisons. Thus, the user can filter and compare any number of pairwise comparisons within a single A-Lister differential expression command. In generic name list mode, A-Lister accepts delimited text files containing lists of names as input. Queries composed of intersection, fuzzy intersection, difference, and union set operations can then be performed across these lists of names.ConclusionsA-Lister is a flexible tool that enables the user to rapidly narrow down large lists of DEEs to a small number of most significant entities. These entities can then be further analyzed using visualization, pathway analysis, and other bioinformatics tools

Efficient spiking neural network model of pattern motion selectivity in visual cortex

Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet high-performance approach to simulating large-scale neural network models in real-time is to leverage the parallel processing capability of graphics processing units (GPUs). Based on this approach, we present a two-stage model of visual area MT that we believe to be the first large-scale spiking network to demonstrate pattern direction selectivity. In this model, component-direction- selective (CDS) cells in MT linearly combine inputs from V1 cells that have spatiotemporal receptive fields according to the motion energy model of Simoncelli and Heeger. Pattern-direction-selective (PDS) cells in MT are constructed by pooling over MT CDS cells with a wide range of preferred directions. Responses of our model neurons are comparable to electrophysiological results for grating and plaid stimuli as well as speed tuning. The behavioral response of the network in a motion discrimination task is in agreement with psychophysical data. Moreover, our implementation outperforms a previous implementation of the motion energy model by orders of magnitude in terms of computational speed and memory usage. The full network, which comprises 153,216 neurons and approximately 40 million synapses, processes 20 frames per second of a 40∈×∈40 input video in real-time using a single off-the-shelf GPU. To promote the use of this algorithm among neuroscientists and computer vision researchers, the source code for the simulator, the network, and analysis scripts are publicly available. © 2014 Springer Science+Business Media New York

Neuromodulated attention and goal-driven perception in uncertain domains.

In uncertain domains, the goals are often unknown and need to be predicted by the organism or system. In this paper, contrastive Excitation Backprop (c-EB) was used in two goal-driven perception tasks - one with pairs of noisy MNIST digits and the other with a robot in an action-based attention scenario. The first task included attending to even, odd, low, and high digits, whereas the second task included action goals, such as "eat", "work-on-computer", "read", and "say-hi" that led to attention to objects associated with those actions. The system needed to increase attention to target items and decrease attention to distractor items and background noise. Because the valid goal was unknown, an online learning model based on the cholinergic and noradrenergic neuromodulatory systems was used to predict a noisy goal (expected uncertainty) and re-adapt when the goal changed (unexpected uncertainty). This neurobiologically plausible model demonstrates how neuromodulatory systems can predict goals in uncertain domains and how attentional mechanisms can enhance the perception for that goal

Visual binding, reentry, and neuronal synchrony in a physically situated brain-based device

By constructing and analyzing a physically situated brain-based device (i.e. a device with sensors and actuators whose behavior is guided by a simulated nervous system), we show that reentrant connectivity and dynamic synchronization can provide an effective mechanism for binding the visual features of objects

An efficient automated parameter tuning framework for spiking neural networks

As the desire for biologically realistic spiking neural networks (SNNs) increases, tuning the enormous number of open parameters in these models becomes a difficult challenge. SNNs have been used to successfully model complex neural circuits that explore various neural phenomena such as neural plasticity, vision systems, auditory systems, neural oscillations, and many other important topics of neural function. Additionally, SNNs are particularly well-adapted to run on neuromorphic hardware that will support biological brain-scale architectures. Although the inclusion of realistic plasticity equations, neural dynamics, and recurrent topologies has increased the descriptive power of SNNs, it has also made the task of tuning these biologically realistic SNNs difficult. To meet this challenge, we present an automated parameter tuning framework capable of tuning SNNs quickly and efficiently using evolutionary algorithms (EA) and inexpensive, readily accessible graphics processing units (GPUs). A sample SNN with 4104 neurons was tuned to give V1 simple cell-like tuning curve responses and produce self-organizing receptive fields (SORFs) when presented with a random sequence of counterphase sinusoidal grating stimuli. A performance analysis comparing the GPU-accelerated implementation to a single-threaded central processing unit (CPU) implementation was carried out and showed a speedup of 65× of the GPU implementation over the CPU implementation, or 0.35 h per generation for GPU vs. 23.5 h per generation for CPU. Additionally, the parameter value solutions found in the tuned SNN were studied and found to be stable and repeatable. The automated parameter tuning framework presented here will be of use to both the computational neuroscience and neuromorphic engineering communities, making the process of constructing and tuning large-scale SNNs much quicker and easier

Simulation of cholinergic and noradrenergic modulation of behavior in uncertain environments

Attention is a complex neurobiological process that involves rapidly and flexibly balancing sensory input and goal-directed predictions in response to environmental changes. The cholinergic and noradrenergic systems, which have been proposed to respond to expected and unexpected environmental uncertainty, respectively, play an important role in attention by differentially modulating activity in a multitude of cortical targets. Here we develop a model of an attention task that involves expected and unexpected uncertainty. The cholinergic and noradrenergic systems track this uncertainty and, in turn, influence cortical processing in five different, experimentally verified ways: (1) nicotinic enhancement of thalamocortical input, (2) muscarinic regulation of corticocortical feedback, (3) noradrenergic mediation of a network reset, (4) locus coeruleus (LC) activation of the basal forebrain (BF), and (5) cholinergic and noradrenergic balance between sensory input and frontal cortex predictions. Our results shed light on how the noradrenergic and cholinergic systems interact with each other and a distributed set of neural areas, and how this could lead to behavioral adaptation in the face of uncertainty