The poet W.B Yeats wrote that “All that is personal soon rots, it must be packed in ice or salt” . Here we show that in Caenorhabditis elegans nematodes, simple animals with just 302 neurons, memories are preserved on ice and in lithium salt. C. elegans nematodes can form associative memories, which are typically forgotten quickly. We discovered that when placed on ice, worms delay forgetting of specific olfactory memories by at least 8-fold. Delayed forgetting was canceled completely when the worms were gradually adapted to low temperatures, owing to a genetically-encoded program that turns acclimated worms cold-tolerant. RNA-seq, mutant analyses, and pharmacological assays revealed that regulation of membrane properties switches cold-induced delayed forgetting ON and OFF, and, remarkably, that lithium delays forgetting only in cold-sensitive but not cold-tolerant worms. We found that downregulation of the diacylglycerol pathway in the AWC sensory neurons is essential for lithium-mediated delayed forgetting, and using neuronal activity recordings located the memory trace to the downstream AIY interneurons. We suggest that the awesome genetic tractability of C. elegans might be harnessed to study the effects of lithium and cold temperatures on the brain, why it influences psychiatric disorders, and even more fundamentally how memory is stored and lost. ### Competing Interest Statement The authors have declared no competing interest.

Abstract Introduction Peyronie's disease (PD) is a penile connective tissue disorder that results in an inflammatory process, fibrotic plaque in the tunica albuginea (TA) and penile curvature. The molecular changes of fibrotic plaque formation are complex and include different cellular/protein mechanisms. Vitamin D (vitD) is a steroid hormone that has unconventional pathways of action, and among its effects it has already been described that it plays a role in extracellular matrix remodeling, in the immune response and in fibrosis. The literature regarding the action of vitD in the male genital system is scarce, and the gene expression of its receptors has not yet been demonstrated in penile TA. Objective To identify the gene expression of the vitD receptor in penile TA of rats submitted to the experimental model of PD and human penile TA. Methods The experimental model in rats divided into 2 groups (case and control) described before by Cohen et al. Human penile TA was achieved from Peyronie´s surgery cases and cadaveric controls. The antibody anti vitD (VDR D-6: sc-13133, Santa Cruz Biotechnology®, CA, USA) was used. Immunolabeling was carried out using the avidin-biotin-peroxidase complex method, and 3,3′-diaminobenzidine as liquid chromogen. In each case, the immunolabeling was quantified by computer-assisted digital image analysis following the methodology described by Matos et al. PCR real-time analyses followed the MIQE guideline10. One of the fractions of the tissue samples of the TA was stored in RNAholder. The extraction of total RNA from the samples was carried out using the reagent TRIzol®. The reverse transcription was performed using the reverse transcriptase enzyme ImPromII™ to obtain complementary DNA (cDNA). Messenger RNA expression of vitD was obtained by RT-PCR. Quantitative RT-PCR was carried out using pair of oligonucleotides endogenous RPL13a (human samples) and GAPDH (rat samples), known commercially as forward and reverse primers. It was performed statistical analysis with SPSS® version 17.0 (SPSS® Inc; Illinois, USA). Values were expressed as a mean and standard error with a significance level of 95% (p ≤ 0.05). The tests applied were One-Way ANOVA and unpaired t-test with Welch's correction, confirming the similarity between the variances. Sensitivity and specificity analysis was calculated following the Youden index. Ethical approval local committee number 06/2016. Results The vitD mRNA amplifications demonstrated the presence of its receptors in the penile TA in both the control and the case groups, and the expression of the control group was statistically higher (p < 0,0001) when compared to the case group in rats. In human samples, mRNA amplifications demonstrated the presence of its receptors in the penile TA in both the control and the case groups, and the expression of the control group was statistically lower (p < 0,0001) when compared to the case group. Conclusions The presence of vitD receptors (genic and proteic expression) in penile TA of rats Peyronies´ models and human TA was demonstrated and allows future studies regarding its regulation and modulation according to the evolution and phase of PD and vitD serum levels. Disclosure No

Background Among the major challenges in next-generation sequencing experiments are exploratory data analysis, interpreting trends, identifying potential targets/candidates, and visualizing the results clearly and intuitively. These hurdles are further heightened for researchers who are not experienced in writing computer code since most available analysis tools require programming skills. Even for proficient computational biologists, an efficient and replicable system is warranted to generate standardized results. Results We have developed RNAlysis , a modular Python-based analysis software for RNA sequencing data. RNAlysis allows users to build customized analysis pipelines suiting their specific research questions, going all the way from raw FASTQ files (adapter trimming, alignment, and feature counting), through exploratory data analysis and data visualization, clustering analysis, and gene set enrichment analysis. RNAlysis provides a friendly graphical user interface, allowing researchers to analyze data without writing code. We demonstrate the use of RNAlysis by analyzing RNA sequencing data from different studies using C. elegans nematodes. We note that the software applies equally to data obtained from any organism with an existing reference genome. Conclusions RNAlysis is suitable for investigating various biological questions, allowing researchers to more accurately and reproducibly run comprehensive bioinformatic analyses. It functions as a gateway into RNA sequencing analysis for less computer-savvy researchers, but can also help experienced bioinformaticians make their analyses more robust and efficient, as it offers diverse tools, scalability, automation, and standardization between analyses.

Our cognition can be directed to external stimuli or to internal information. While there are many different forms of internal cognition (mind-wandering, recall, imagery etc.), their essential feature is independence from the immediate sensory input, conceptually referred to as perceptual decoupling. Perceptual decoupling is thought to be reflected in brain activity transitioning from a stimulus-processing to internally-processing mode, but a direct investigation of this remains outstanding. Here we present a conceptual and analysis framework that quantifies the extent to which brain networks reflect stimulus processing. We tested this framework by presenting subjects with an audiovisual stimulus and instructing them to either attend to the stimulus (external task) or engage in mental imagery, recall or arithmetic (internal tasks) while measuring the evoked brain activity using functional MRI. We found that stimulus responses were generally attenuated for the internal tasks, though they increased in a subset of tasks and brain networks. However, using our new framework, we showed that brain networks became less reflective of stimulus processing, even in the subset of tasks and brain networks in which stimulus responses increased. These results quantitatively demonstrate that during internal cognition brain networks become decoupled from the external stimuli, opening the door for a fundamental and quantitative understanding of internal cognition.

The physical basis of consciousness remains one of the most elusive concepts in current science. One influential conjecture is that consciousness is to do with some form of causality, measurable through information. The integrated information theory of consciousness (IIT) proposes that conscious experience, filled with rich and specific content, corresponds directly to a hierarchically organised, irreducible pattern of causal interactions; i.e. an integrated informational structure among elements of a system. Here, we tested this conjecture in a simple biological system (fruit flies), estimating the information structure of the system during wakefulness and general anesthesia. Consistent with this conjecture, we found that integrated interactions among populations of neurons during wakefulness collapsed to isolated clusters of interactions during anesthesia. We used classification analysis to quantify the accuracy of discrimination between wakeful and anesthetised states, and found that informational structures inferred conscious states with greater accuracy than a scalar summary of the structure, a measure which is generally championed as the main measure of IIT. In stark contrast to a view which assumes feedforward architecture for insect brains, especially fly visual systems, we found rich information structures, which cannot arise from purely feedforward systems, occurred across the fly brain. Further, these information structures collapsed uniformly across the brain during anesthesia. Our results speak to the potential utility of the novel concept of an "informational structure" as a measure for level of consciousness, above and beyond simple scalar values.

We apply techniques from the field of computational mechanics to evaluate the statistical complexity of neural recording data from fruit flies. First, we connect statistical complexity to the flies' level of conscious arousal, which is manipulated by general anesthesia (isoflurane). We show that the complexity of even single channel time series data decreases under anesthesia. The observed difference in complexity between the two states of conscious arousal increases as higher orders of temporal correlations are taken into account. We then go on to show that, in addition to reducing complexity, anesthesia also modulates the informational structure between the forward- and reverse-time neural signals. Specifically, using three distinct notions of temporal asymmetry we show that anesthesia reduces temporal asymmetry on information-theoretic and information-geometric grounds. In contrast to prior work, our results show that: (1) Complexity differences can emerge at very short timescales and across broad regions of the fly brain, thus heralding the macroscopic state of anesthesia in a previously unforeseen manner, and (2) that general anesthesia also modulates the temporal asymmetry of neural signals. Together, our results demonstrate that anesthetized brains become both less structured and more reversible.

Background: Quantifying interactions among many neurons is fundamental to understanding system-level phenomena such as attention, learning and even conscious experience. Causal influences in the brain, quantified as integrated information, are thought to support subjective conscious experience. Recent empirical work has shown that the spectral decomposition of causal influences, for example using Granger causality, can reveal frequency-specific influences that are not observed in the time domain. However, a spectral decomposition of integrated information has not been put forward, limiting its adoption for analyzing neural data. New method: We present a general and flexible framework for deriving the spectral decomposition of causal influences in autoregressive processes. Results: We use the framework to derive a spectral decomposition of integrated information. We show that other well-known measures, including Granger causality, can be derived using the same framework. Using simulations, we demonstrate a complex interplay between the spectral decomposition of integrated information and other measures that is not observed in the time domain. Comparison with existing methods: This paper provides a spectral decomposition of integrated information for the first time. Although a spectral decomposition of Granger causality has been derived, that approach is only applicable to uni-directional causal influences, not multi-directional causal influences as required for integrated information. Conclusions: Our novel framework can be used to derive the spectral decomposition of uni- and multi-directional measures of causal influences. We use this framework to derive a spectral decomposition of integrated information, paving the way for better understanding how frequency-specific causal influences in the brain relate to cognition.

Rational choice theory assumes optimality in decision-making. Violations of a basic axiom of economic rationality known as “Independence of Irrelevant Alternatives” (IIA) have been demonstrated in both humans and animals and could stem from common neuronal constraints. Here we develop tests for IIA in the nematode Caenorhabditis elegans , an animal with only 302 neurons, using olfactory chemotaxis assays. We find that in most cases C. elegans make rational decisions. However, by probing multiple neuronal architectures using various choice sets, we show that violations of rationality arise when the circuit of olfactory sensory neurons is asymmetric. We further show that genetic manipulations of the asymmetry between the AWC neurons can make the worm irrational. Last, a context-dependent normalization-based model of value coding and gain control explains how particular neuronal constraints on information coding give rise to irrationality. Thus, we demonstrate that bounded rationality could arise due to basic neuronal constraints.

Abstract Hierarchically organized brains communicate through feedforward (FF) and feedback (FB) pathways. In mammals, FF and FB are mediated by higher and lower frequencies during wakefulness. FB is preferentially impaired by general anesthetics in multiple mammalian species. This suggests FB serves critical functions in waking brains. The brain of Drosophila melanogaster (fruit fly) is also hierarchically organized, but the presence of FB in these brains is not established. Here, we studied FB in the fly brain, by simultaneously recording local field potentials (LFPs) from low-order peripheral structures and higher-order central structures. We analyzed the data using Granger causality (GC), the first application of this analysis technique to recordings from the insect brain. Our analysis revealed that low frequencies (0.1–5 Hz) mediated FB from the center to the periphery, while higher frequencies (10–45 Hz) mediated FF in the opposite direction. Further, isoflurane anesthesia preferentially reduced FB. Our results imply that the spectral characteristics of FF and FB may be a signature of hierarchically organized brains that is conserved from insects to mammals. We speculate that general anesthetics may induce unresponsiveness across species by targeting the mechanisms that support FB.

When analyzing neural data it is important to consider the limitations of the particular experimental setup. An enduring issue in the context of electrophysiology is the presence of common signals. For example a non-silent reference electrode adds a common signal across all recorded data and this adversely affects functional and effective connectivity analysis. To address the common signals problem, a number of methods have been proposed, but relatively few detailed investigations have been carried out. As a result, our understanding of how common signals affect neural connectivity estimation is incomplete. For example, little is known about recording preparations involving high spatial-resolution electrodes, used in linear array recordings. We address this gap through a combination of theoretical review, simulations, and empirical analysis of local field potentials recorded from the brains of fruit flies. We demonstrate how a framework that jointly analyzes power, coherence, and quantities based on Granger causality reveals the presence of common signals. We further show that subtracting spatially adjacent signals (bipolar derivations) largely removes the effects of the common signals. However, in some special cases this operation itself introduces a common signal. We also show that Granger causality is adversely affected by common signals and that a quantity referred to as “instantaneous interaction” is increased in the presence of common signals. The theoretical review, simulation, and empirical analysis we present can readily be adapted by others to investigate the nature of the common signals in their data. Our contributions improve our understanding of how common signals affect power, coherence, and Granger causality and will help reduce the misinterpretation of functional and effective connectivity analysis.

Rational choice theory assumes optimality in decision-making. Violations of a basic axiom of economic rationality known as “Independence of Irrelevant Alternatives” (IIA), have been demonstrated in both humans and animals, and could stem from common neuronal constraints. We developed tests for IIA in the nematode Caenorhabditis elegans , an animal with only 302 neurons, using olfactory chemotaxis assays. We found that in most cases C. elegans make rational decisions. However, by probing multiple neuronal architectures using various choice sets, we show that asymmetric sensation of odor options by the AWCON neuron can lead to violations of rationality. We further show that genetic manipulations of the asymmetry between the AWC neurons can make the worm rational or irrational. Last, a normalization-based model of value coding and gain control explains how particular neuronal constraints on information coding give rise to irrationality. Thus, we demonstrate that bounded rationality could arise due to basic neuronal constraints.

Hierarchically organized brains communicate through feedforward and feedback pathways. In mammals, feedforward and feedback are mediated by higher and lower frequencies during wakefulness. Feedback is preferentially impaired by general anesthetics. This suggests feedback serves critical functions in waking brains. The brain of Drosophila melanogaster (fruit fly) is also hierarchically organized, but the presence of feedback in these brains is not established. Here we studied feedback in the fruit fly brain, by simultaneously recording local field potentials (LFPs) from low-order peripheral structures and higher-order central structures. Directed connectivity analysis revealed that low frequencies (0.1-5Hz) mediated feedback from the center to the periphery, while higher frequencies (10-45Hz) mediated feedforward in the opposite direction. Further, isoflurane anesthesia preferentially reduced feedback. Our results imply that similar spectral characteristics of feedforward and feedback may be a signature of hierarchically organized brains and that general anesthetics may induce unresponsiveness by targeting the mechanisms that support feedback.

When analyzing neural data it is important to consider the limitations of the particular experimental setup. An enduring issue in the context of electrophysiology is the presence of common signals. For example a non-silent reference electrode adds a common signal across all recorded data and this adversely affects functional and effective connectivity analysis. To address the common signals problem, a number of methods have been proposed, but relatively few detailed investigations have been carried out. We address this gap by analyzing local field potentials recorded from the small brains of fruit flies. We conduct our analysis following a solid mathematical framework that allows us to make precise predictions regarding the nature of the common signals. We demonstrate how a framework that jointly analyzes power, coherence and quantities from the Granger causality framework allows us to detect and assess the nature of the common signals. Our analysis revealed substantial common signals in our data, in part due to a non-silent reference electrode. We further show that subtracting spatially adjacent signals (bipolar rereferencing) largely removes the effects of the common signals. However, in some special cases this operation itself introduces a common signal. The mathematical framework and analysis pipeline we present can readily be used by others to detect and assess the nature of the common signals in their data, thereby reducing the chance of misinterpreting the results of functional and effective connectivity analysis.

Hybrid methods are highly effective means of solving combinatorial optimization problems and have become increasingly popular. In particular, integrations of exact and incomplete methods have proved to be effective where the hybrid takes advantage of the relative performance of each individual method. However, these methods often require significant run-times to determine good feasible solutions. One way of reducing run-times is to parallelize these algorithms. For large NP-hard problems, parallelization must be done with care, since changes to the algorithm can affect its performance in unpredictable ways. In this paper we develop two parallel variants of constraint-based ACO and test them on a problem arising in the Australian mining industry. We demonstrate that parallelization significantly reduces run times with each parallel variant providing advantages with respect to feasibility and solution quality.

What characteristics of neural activity distinguish the awake and anesthetized brain? Drugs such as isoflurane abolish behavioral responsiveness in all animals, implying evolutionarily conserved mechanisms. However, it is unclear whether this conservation is reflected at the level of neural activity. Studies in humans have shown that anesthesia is characterized by spatially distinct spectral and coherence signatures that have also been implicated in the global impairment of cortical communication. We questioned whether anesthesia has similar effects on global and local neural processing in one of the smallest brains, that of the fruit fly (Drosophila melanogaster). Using a recently developed multielectrode technique, we recorded local field potentials from different areas of the fly brain simultaneously, while manipulating the concentration of isoflurane. Flickering visual stimuli ('frequency tags') allowed us to track evoked responses in the frequency domain and measure the effects of isoflurane throughout the brain. We found that isoflurane reduced power and coherence at the tagging frequency (13 or 17 Hz) in central brain regions. Unexpectedly, isoflurane increased power and coherence at twice the tag frequency (26 or 34 Hz) in the optic lobes of the fly, but only for specific stimulus configurations. By modeling the periodic responses, we show that the increase in power in peripheral areas can be attributed to local neuroanatomy. We further show that the effects on coherence can be explained by impacted signal- to- noise ratios. Together, our results show that general anesthesia has distinct local and global effects on neuronal processing in the fruit fly brain.

Event Abstract Back to Event The neuronal mechanisms of Steady State Visually Evoked Potential (SSVEP) studied in the fly brains with multi-contact electrodes Dror Cohen1*, Angelique Christine Paulk2, Bruno Van Swinderen2 and Naotsugu Tsuchiya1 1 School of Psychology and Psychiatry, Monash University, Australia 2 Queensland Brain Institute, The University of Queensland, Australia Background Flickering stimuli that induce Steady State Visually Evoked Potentials (SSVEP) have been successfully used to study cognitive processes in humans and animals. In particular, covert shifts of attention have been shown to enhance the SSVEP responses for the attended flickers and to reduce those for the ignored flickers. Surprisingly, homologous attentional modulation has been reported in fruit flies using SSVEP. The SSVEP, combined with the genetic manipulations available for flies, opens up possible dissection of high-level cognitive processes such as attention at the neuro-circuit level. Here, we investigated the neuronal mechanisms of SSVEP in flies using electrophysiological techniques. Methods We recorded local neural activity (Local Field Potentials, LFP) in the brains of fruit flies (Drosophila Melanogaster), using a 16-channel micro linear array inserted laterally across the fly brain. During recording, a flicker stimulus was presented either at the left, right or the both sides of the visual field. The flicker frequency was either 13Hz or 16Hz. We characterised the physiological response properties of SSVEP by analysing the LFPs in time and frequency domains. To quantify the information about the stimulus represented in the fly brain, we used a multi-variate decoding technique; specifically, we investigated when, where, and at which frequencies, the information about the flickers was encoded in the LFPs. Results The analysis in time and frequency revealed precise and consistent responses to the flickers in the peripheral visual systems. However, significant responses were also observed in central brain areas. Notably, in addition to the expected SSVEP response at the flicker frequencies we observed strong responses at the harmonics (up to 7th harmonic) and inter-modulatory frequencies (e.g., 29Hz = 13Hz+16Hz). Decoding analyses confirmed highly localized information both in space and frequency and nearly perfect accuracy in classifying the stimuli. Discussion Our initial step towards understanding SSVEP in files is highly promising. Ongoing experiments will test the neuronal basis of attentional modulation using various paradigms. For example, we will make one flickering stimuli more salient by pairing it with reward/punishment. By combining with genetic manipulations of local neural circuits, we will extend our analysis to quantify how information about the attended and ignored flickers is represented in the brains. Keywords: SSVEP, Drosophila melanogaster, Attention, Decoding, local field potential (LFP), Intermodulation, multi-channel recording Conference: ACNS-2013 Australasian Cognitive Neuroscience Society Conference, Clayton, Melbourne, Australia, 28 Nov - 1 Dec, 2013. Presentation Type: Poster Topic: Sensation and Perception Citation: Cohen D, Paulk A, Van Swinderen B and Tsuchiya N (2013). The neuronal mechanisms of Steady State Visually Evoked Potential (SSVEP) studied in the fly brains with multi-contact electrodes. Conference Abstract: ACNS-2013 Australasian Cognitive Neuroscience Society Conference. doi: 10.3389/conf.fnhum.2013.212.00038 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 15 Oct 2013; Published Online: 25 Nov 2013. * Correspondence: Mr. Dror Cohen, School of Psychology and Psychiatry, Monash University, Melbourne, Australia, dror.cohen@monash.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Dror Cohen Angelique Christine Paulk Bruno Van Swinderen Naotsugu Tsuchiya Google Dror Cohen Angelique Christine Paulk Bruno Van Swinderen Naotsugu Tsuchiya Google Scholar Dror Cohen Angelique Christine Paulk Bruno Van Swinderen Naotsugu Tsuchiya PubMed Dror Cohen Angelique Christine Paulk Bruno Van Swinderen Naotsugu Tsuchiya Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

In this paper we compare the self organising capabilities of the Generative Topographic Map (GTM) [1] and Elastic Net (EN) [2]. We analytically compare the two algorithms and examine the different ways in which they preserve topography by considering their respective ‘state space trajectories’. We present simulations that demonstrate the differences between the two algorithms. We conclude by using the GTM to simulate the formation of Ocular Dominance (OD) stripes and compare against earlier simulations using the EN. Our findings indicate that the GTM produces patterns with some of the required characteristics and match results obtained with the EN to a degree.

In this paper we use the Elastic Net (EN) [9] as a visual category representation in feature space. We do this by training the EN on the high dimensional Pyramid Histogram of Visual Words (PHOW) features [2] often used in modern visual categorisation. By employing the topography preserving properties of the EN we visualise the features and draw some novel conclusions. We demonstrate how the EN can also be used as a Region of Interest detector [1]. Finally, inspired by biological vision we propose a new Visual Categorisation scheme that uses ENs as visual category representations. Our method shows promising results when tested on the Caltech101 [12] data set with several interesting future directions.