scholarly journals Development of a Scheme and Tools to Construct a Standard Moth Brain for Neural Network Simulations

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Hidetoshi Ikeno ◽  
Tomoki Kazawa ◽  
Shigehiro Namiki ◽  
Daisuke Miyamoto ◽  
Yohei Sato ◽  
...  
Keyword(s):  
Behavioral Control ◽  
Neural Mechanisms ◽  
System Level ◽  
Neural Systems ◽  
Natural Environments ◽  
Controlling Behavior ◽  
Confocal Images ◽  
Network Simulations ◽  
Global Understanding ◽  
Enormous Number

Understanding the neural mechanisms for sensing environmental information and controlling behavior in natural environments is a principal aim in neuroscience. One approach towards this goal is rebuilding neural systems by simulation. Despite their relatively simple brains compared with those of mammals, insects are capable of processing various sensory signals and generating adaptive behavior. Nevertheless, our global understanding at network system level is limited by experimental constraints. Simulations are very effective for investigating neural mechanisms when integrating both experimental data and hypotheses. However, it is still very difficult to construct a computational model at the whole brain level owing to the enormous number and complexity of the neurons. We focus on a unique behavior of the silkmoth to investigate neural mechanisms of sensory processing and behavioral control. Standard brains are used to consolidate experimental results and generate new insights through integration. In this study, we constructed a silkmoth standard brain and brain image, in which we registered segmented neuropil regions and neurons. Our original software tools for segmentation of neurons from confocal images, KNEWRiTE, and the registration module for segmented data, NeuroRegister, are shown to be very effective in neuronal registration for computational neuroscience studies.

scholarly journals A Kiosk Station for the Assessment of Multiple Cognitive Domains and Enrichment of Monkeys

2021 ◽  
Author(s):  
Thilo Womelsdorf ◽  
Christopher Thomas ◽  
Adam Neumann ◽  
Marcus Watson ◽  
Kianoush Banaie Boroujeni ◽  
...  
Keyword(s):  
Behavioral Control ◽  
Animal Cognition ◽  
Cognitive Task ◽  
Cognitive Tasks ◽  
Fully Integrated ◽  
Cognitive Domains ◽  
Visual Tasks ◽  
Versatile Tool ◽  
Controlling Behavior ◽  
Individual Consistency

AbstractBackgroundNonhuman primates (NHPs) are self-motivated to perform cognitive tasks on touchscreens in their animal housing setting. To leverage this ability, fully integrated hardware and software solutions are needed, that work within housing and husbandry routines while also spanning cognitive task constructs of the Research Domain Criteria (RDoC).New MethodWe describe a Kiosk Station (KS-1) that provides robust hardware and software solutions for running cognitive tasks in cage-housed NHPs. KS-1 consists of a frame for mounting flexibly on housing cages, a touchscreen animal interface with mounts for receptables, reward pumps and cameras, and a compact computer cabinet with an interface for controlling behavior. Behavioral control is achieved with a unity3D program that is virtual-reality capable, allowing semi-naturalistic visual tasks to assess multiple cognitive domains.ResultsKS-1 is fully integrated into the regular housing routines of monkeys. A single person can operate multiple KS-1s. Monkeys engage with KS-1 at high motivation and cognitive performance levels at high intra-individual consistency.Comparison with Existing MethodsKS-1 is optimized for flexible mounting onto standard apartment cage systems. KS-1 has a robust animal interface with options for gaze/reach monitoring. It has an integrated user interface for controlling multiple cognitive task using a common naturalistic object space designed to enhance task engagement. All custom KS-1 components are open-sourced.ConclusionsKS-1 is a versatile tool for cognitive profiling and enrichment of cage-housed monkeys. It reliably measures multiple cognitive domains which promises to advance our understanding of animal cognition, inter-individual differences and underlying neurobiology in refined, ethologically meaningful behavioral foraging contexts.

scholarly journals A reflective memory based framework for crowd network simulations

2018 ◽  
Vol 2 (1) ◽  
pp. 74-84 ◽  
Author(s):  
Sun Hongbo ◽  
Mi Zhang
Keyword(s):  
Large Scale ◽  
Service Industry ◽  
System Level ◽  
Scale Problem ◽  
Main Mode ◽  
Content Type ◽  
System Level Simulation ◽  
Logical Aspect ◽  
Network Simulations ◽  
High Level

Purpose As main mode of modern service industry and future economy society, the research on crowd network can greatly facilitate governances of economy society and make it more efficient, humane, sustainable and at the same time avoid disorders. However, because most results cannot be observed in real world, the research of crowd network cannot follow a traditional way. Simulation is the main means to put forward related research studies. Compared with other large-scale interactive simulations, simulation for crowd network has challenges of dynamic, diversification and massive participants. Fortunately, known as the most famous and widely accepted standard, high level architecture (HLA) has been widely used in large-scale simulations. But when it comes to crowd network, HLA has shortcomings like fixed federation, limited scale and agreement outside the software system. Design/methodology/approach This paper proposes a novel reflective memory-based framework for crowd network simulations. The proposed framework adopts a two-level federation-based architecture, which separates simulation-related environments into physical and logical aspect to enhance the flexibility of simulations. Simulation definition is introduced in this architecture to resolve the problem of outside agreements and share resources pool (constructed by reflective memory) is used to address the systemic emergence and scale problem. Findings With reference to HLA, this paper proposes a novel reflective memory-based framework toward crowd network simulations. The proposed framework adopts a two-level federation-based architecture, system-level simulation (system federation) and application-level simulation (application federations), which separates simulation-related environments into physical and logical aspect to enhance the flexibility of simulations. Simulation definition is introduced in this architecture to resolve the problem of outside agreements and share resources pool (constructed by reflective memory) is used to address the systemic emergence and scale problem. Originality/value Simulation syntax and semantic are all settled under this framework by templates, especially interface templates, as simulations are separated by two-level federations, physical and logical simulation environment are considered separately; the definition of simulation execution is flexible. When developing new simulations, recompile is not necessary, which can acquire much more reusability, because reflective memory is adopted as share memory within given simulation execution in this framework; population can be perceived by all federates, which greatly enhances the scalability of this kind of simulations; communication efficiency and capability has greatly improved by this share memory-based framework.

How Reliable is the Pattern Adaptation Technique? A Modeling Study

2009 ◽  
Vol 102 (4) ◽  
pp. 2245-2252 ◽  
Author(s):  
Jay Hegdé
Keyword(s):  
Spatial Frequency ◽  
Frequency Tuning ◽  
Spatial Vision ◽  
Neural Mechanisms ◽  
System Level ◽  
Sinusoidal Grating ◽  
Tuning Parameters ◽  
Neuronal Ensemble ◽  
Spatial Frequency Tuning ◽  
Spatial Frequencies

Upon prolonged viewing of a sinusoidal grating, the visual system is selectively desensitized to the spatial frequency of the grating, while the sensitivity to other spatial frequencies remains largely unaffected. This technique, known as pattern adaptation, has been so central to the psychophysical study of the mechanisms of spatial vision that it is sometimes referred to as the “psychologist's microelectrode.” While this approach implicitly assumes that the adaptation behavior of the system is diagnostic of the corresponding underlying neural mechanisms, this assumption has never been explicitly tested. We tested this assumption using adaptation bandwidth, or the range of spatial frequencies affected by adaptation, as a representative measure of adaptation. We constructed an intentionally simple neuronal ensemble model of spatial frequency processing and examined the extent to which the adaptation bandwidth at the system level reflected the bandwidth at the neuronal level. We find that the adaptation bandwidth could vary widely even when all spatial frequency tuning parameters were held constant. Conversely, different spatial frequency tuning parameters were able to elicit similar adaptation bandwidths from the neuronal ensemble. Thus, the tuning properties of the underlying units did not reliably reflect the adaptation bandwidth at the system level, and vice versa. Furthermore, depending on the noisiness of adaptation at the neural level, the same neuronal ensemble was able to produce selective or nonselective adaptation at the system level, indicating that a lack of selective adaptation at the system level cannot be taken to mean a lack of tuned mechanisms at the neural level. Together, our results indicate that pattern adaptation cannot be used to reliably estimate the tuning properties of the underlying units, and imply, more generally, that pattern adaptation is not a reliable tool for studying the neural mechanisms of pattern analysis.

scholarly journals Human Empathy Through the Lens of Social Neuroscience

2006 ◽  
Vol 6 ◽  
pp. 1146-1163 ◽  
Author(s):  
Jean Decety ◽  
Claus Lamm
Keyword(s):  
Information Processing ◽  
Cognitive Neuroscience ◽  
Executive Control ◽  
Perspective Taking ◽  
Functional Neuroimaging ◽  
Social Neuroscience ◽  
Neural Mechanisms ◽  
The Self ◽  
Neural Systems

Empathy is the ability to experience and understand what others feel without confusion between oneself and others. Knowing what someone else is feeling plays a fundamental role in interpersonal interactions. In this paper, we articulate evidence from social psychology and cognitive neuroscience, and argue that empathy involves both emotion sharing (bottom-up information processing) and executive control to regulate and modulate this experience (top-down information processing), underpinned by specific and interacting neural systems. Furthermore, awareness of a distinction between the experiences of the self and others constitutes a crucial aspect of empathy. We discuss data from recent behavioral and functional neuroimaging studies with an emphasis on the perception of pain in others, and highlight the role of different neural mechanisms that underpin the experience of empathy, including emotion sharing, perspective taking, and emotion regulation.

scholarly journals Some Metatheoretical Principles for Personality Neuroscience

2018 ◽  
Vol 1 ◽  
Author(s):  
Neil McNaughton ◽  
Luke D. Smillie
Keyword(s):  
Nervous System ◽  
Personality Traits ◽  
Big Five ◽  
Future Development ◽  
Neural Mechanisms ◽  
Neural Systems ◽  
Personality Theory ◽  
Vertebrate Species ◽  
Neurotransmitter Systems ◽  
Levels Of Explanation

Abstract Theories in personality neuroscience must aim to be consistent with several levels of explanation. If we view personality traits as constructs located only at the psychological level, we must still make their explanations compatible with observations and theories at lower levels, particularly with what we know at the neural level. If we view personality traits as constructs located only at the neural level, we will still need to predict their emergent effects at the psychological level. Personality theory at present treats traits as psychological-level constructs, with even the recent neurally oriented Cybernetic Big Five Theory specified in terms of a “conceptual nervous system” and not requiring complete or immediate translation into neural mechanisms. Here, we argue for the existence of phylogenetically old, neural-level traits that are substantially conserved across many vertebrate species. We first ask what known mechanisms control trait-like properties of neural systems: Focusing on hormones, the GABAA receptor, and amine neurotransmitter systems. We derive from what we know about these sources of neuronal modulation some metatheoretical principles to guide the future development of those aspects of personality theory, starting with neural-level trait constructs and drawing implications for higher-level trait psychology observations. Current descriptive approaches such as the Big Five are an essential precursor to personality neuroscience, but may not map one-to-one to the mechanisms and constructs of a neuroscience-based approach to traits.

Artificial Neural Systems for Robots

2011 ◽  
pp. 214-248
Author(s):  
Phil Husbands ◽  
Andy Philippides ◽  
Anil K. Seth
Keyword(s):  
Neural Networks ◽  
Neural Mechanisms ◽  
Historical Background ◽  
Neural Systems ◽  
Adaptive Behaviour ◽  
Biologically Inspired ◽  
Related Area ◽  
Artificial Neural Systems ◽  
Artificial Neural ◽  
Research Frontiers

This chapter reviews the use of neural systems in robotics, with particular emphasis on strongly biologically inspired neural networks and methods. As well as describing work at the research frontiers, the paper provides some historical background in order to clarify the motivations and scope of work in this field. There are two major sections that make up the bulk of the chapter: one surveying the application of artificial neural systems to robot control, and one describing the use of robots as tools in neuroscience. The former concentrates on biologically derived neural architectures and methods used to drive robot behaviours, and the latter introduces a closely related area of research where robotic models are used as tools to study neural mechanisms underlying the generation of adaptive behaviour in animals and humans.

scholarly journals The effect of nutrition on the neural mechanisms potentially involved in melatonin-stimulated LH secretion in female Mediterranean goats

2011 ◽  
Vol 211 (3) ◽  
pp. 263-272 ◽  
Author(s):  
Luis A Zarazaga ◽  
Irma Celi ◽  
José Luis Guzmán ◽  
Benoît Malpaux
Keyword(s):  
Excitatory Amino Acid ◽  
Endogenous Opioids ◽  
Neural Mechanisms ◽  
Neural Systems ◽  
Endogenous Opioid ◽  
Long Day ◽  
Dopaminergic Systems ◽  
Nutritional Level ◽  
Maintenance Requirements ◽  
Lh Secretion

This research examines which neural mechanisms among the endogenous opioid, dopaminergic, serotonergic and excitatory amino acid systems are involved in the stimulation of LH secretion by melatonin implantation and their modulation by nutritional level. Female goats were distributed to two experimental groups that received either 1.1 (group H;n=24) or 0.7 (group L;n=24) times their nutritional maintenance requirements. Half of each group was implanted with melatonin after a long-day period. Plasma LH concentrations were measured twice per week. The effects of i.v. injections of naloxone, pimozide, cyproheptadine andN-methyl-d,l-aspartate (NMDA) on LH secretion were assessed the day before melatonin implantation and again on days 30 and 45. The functioning of all but the dopaminergic systems was clearly modified by the level of nutrition, melatonin implantation and time elapsed since implantation. Thirty days after implantation, naloxone increased LH concentrations irrespective of the level of nutrition (P<0.05), similar to NMDA in the melatonin-implanted H goats (HM;P<0.01). On day 45, naloxone increased LH concentrations in the HM animals (P<0.05), similar to cyproheptadine in both the non-implanted H (HC) and the HM animals (P<0.01). Finally, at 45 days, NMDA increased the LH concentration in all subgroups (P<0.01). These results provide evidence that the effects of different neural systems on LH secretion are modified by nutritional level and melatonin implantation. Endogenous opioids seem to be most strongly involved in the inhibition of LH secretion on days 30 and 45 after melatonin implantation. However, the serotonergic mechanism appears to be most influenced by nutritional level.

Trade-off between Capacity and Precision in Visuospatial Working Memory

2014 ◽  
Vol 26 (2) ◽  
pp. 211-222 ◽  
Author(s):  
Chantal Roggeman ◽  
Torkel Klingberg ◽  
Heleen E. M. Feenstra ◽  
Albert Compte ◽  
Rita Almeida
Keyword(s):  
Working Memory ◽  
Storage Capacity ◽  
Excitatory Input ◽  
Spiking Neurons ◽  
Neural Mechanisms ◽  
Visuospatial Working Memory ◽  
Trade Off ◽  
Storage Network ◽  
Trial Basis ◽  
Network Simulations

Limitations in the performance of working memory (WM) tasks have been characterized in terms of the number of items retained (capacity) and in terms of the precision with which the information is retained. The neural mechanisms behind these limitations are still unclear. Here we used a biological constrained computational model to study the capacity and precision of visuospatial WM. The model consists of two connected networks of spiking neurons. One network is responsible for storage of information. The other provides a nonselective excitatory input to the storage network. Simulations showed that this excitation boost could temporarily increase storage capacity but also predicted that this would be associated with a decrease in precision of the memory. This prediction was subsequently tested in a behavioral (38 participants) and fMRI (22 participants) experiment. The behavioral results confirmed the trade-off effect, and the fMRI results suggest that a frontal region might be engaged in the trial-by-trial control of WM performance. The average effects were small, but individuals differed in the amount of trade-off, and these differences correlated with the frontal activation. These results support a two-module model of WM where performance is determined both by storage capacity and by top–down influence, which can vary on a trial-by-trial basis, affecting both the capacity and precision of WM.

Attention

2014 ◽  
Author(s):  
Anna C. (Kia) Nobre ◽  
Sabine Kastner
Keyword(s):  
William James ◽  
Neural Mechanisms ◽  
Fundamental Aspect ◽  
Neural Systems ◽  
Cognitive Domains ◽  
Hermann Von Helmholtz ◽  
Wilhelm Wundt ◽  
Tremendous Progress ◽  
Functional Consequences ◽  
Cognitive Revolution

‘Attention’ is a core and fundamental aspect of cognition. Accordingly it engages a sizeable and thriving research community. The field has precious theoretical and empirical seeds left by the pioneering investigators of mental functions in the nineteenth and early twentieth centuries such as Franciscus Donders (1818–89), Hermann von Helmholtz (1821–94), Wilhelm Wundt (1832–1920), and William James (1842–1910). It re-emerges in full strength in the 1950s with the cognitive revolution and Broadbent’s publication of Perception and Communication (1958). Since then, we have made tremendous progress in understanding the functional consequences of attention, its behavioural and neural mechanisms, its neural systems and dynamics, and its implications for neurological and psychiatric disorders. We are also making headway in understanding its interactions with other cognitive domains, and its applications to healthy cognition in the ‘real world’ more generally.

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