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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

Geometrical structure of perceptual color space: mental representations and adaptation invariance

A central issue in neuroscience is to understand how the brain builds structured representations of percepts that facilitate useful inferences about the world. Similarity between percepts is used to accomplish many everyday tasks, e.g. object identification, so is widely used to construct geometrical spaces that represent stimulus qualities, but the intrinsic validity of the geometry is not tested critically. We introduce an experimental approach to equating relative similarities by setting perceived midpoints between pairs of stimuli. Midpoint settings are used with Varignons Theorem to test the intrinsic geometry of a representation space, and its mapping to a physical space of stimuli. For perceptual color space, we demonstrate that geometrical structure depends on the mental representation used in judging similarity: an affine geometry is valid only when observers use an opponent-color mental representation. An affine geometry implies that similarity can be judged within straight lines and across parallel lines, and its neural coding could involve ratios of responses. We show that this perceptual space is invariant to changes in illumination color, providing a formal justification to generalize to all of color space, color constancy results measured for color categories. Our midpoint measurements deviate significantly from midpoints in the \"uniform\" color spaces CIELAB and CIELUV, used extensively in industry and research, so these spaces do not provide adequate metric representation of perceived colors. Our paradigm can thus test for intrinsic geometrical assumptions underlying the representation space for many perceptual modalities, and for the extrinsic perceptual geometry of the space of physical stimuli.\n\nSignificanceMathematical spaces based on similarity judgments are widely used to represent stimulus qualities in perception, cognition and neuroscience. We introduce a perceptual approach to equate relative similarities, and use them to test the geometry of a perceptual space and its mapping to a physical space of stimuli. For color perception, our results show that perceptual geometry depends on the mental representation used in judging similarity, and it has an affine structure when observers use an opponent-color representation. An affine geometry implies that neural coding of similarity could involve simple ratios of responses. Our measurements also reveal that the uniform color spaces CIELAB and CIELUV, used extensively in industrial applications, do not provide adequate representation of similarity between moderately spaced colors.

neuroscience↗

bioRxiv2016

Expanding perspectives on cognition in humans, animals, and machines

Over the past decade neuroscience has been attacking the problem of cognition with increasing vigor. Yet, what exactly is cognition, beyond a general signifier of anything seemingly complex the brain does? Here, we briefly review attempts to define, describe, explain, build, enhance and experience cognition. We highlight perspectives including psychology, molecular biology, computation, dynamical systems, machine learning, behavior and phenomenology. This survey of the landscape reveals not a clear target for explanation but a pluralistic and evolving scene with diverse opportunities for grounding future research. We argue that rather than getting to the bottom of it, over the next century, by deconstructing and redefining cognition, neuroscience will and should expand rather than merely reduce our concept of the mind.

Animal Behavior and Cognition↗

bioRxiv2016

The Social Networks of Neural Progenitor Cells

The architecture of the mammalian brain has been characterized through decades of innovation in the field of network neuroscience. However, the assembly of the brain from progenitor cells is an immensely complex process, and a quantitative understanding of how neural progenitor cells (NPCs) form neural networks has proven elusive. Here, we introduce a method that integrates graph-theory with long-term imaging of differentiating human NPCs to characterize the evolution of spatial and functional network features in NPCs during the formation of neural networks in vitro. We find that the rise and fall in spatial network efficiency is a characteristic feature of the transition from immature NPC networks to mature neural networks. Furthermore, networks at intermediate stages of differentiation that display high spatial network efficiency also show high levels of network-wide spontaneous electrical activity. These results support the view that network-wide signaling in immature progenitor cells gives way to a hierarchical form of communication in mature neural networks. We also leverage graph theory to study the spatial features of individual cell types in developing cultures, uncovering spatial features of polarized neuroepithelium. Finally, we employ our method to uncover aberrant network features in a neurodevelopmental disorder using induced pluripotent stem cell (iPSC) models. The \"Living Neural Networks\" method bridges the gap between developmental neurobiology and network neuroscience, and offers insight into the relationship between developing and mature neural networks.

Systems Biology↗

bioRxiv2018

Panama: a tool for ion channel biophysics simulation

I have created an online tool and an R library that simulates biophysics of voltage-gated ion channels. It is made publicly available as an R library called Panama at github.com/anuj2054/panama and as a web app at neuronsimulator.com. A need for such a tool was observed after surveying available software packages. I found that the available packages are either not robust enough to simulate multiple ion channels, too complicated, usable only as desktop software, not optimized for mobile devices, not interactive, lacking intuitive graphical controls, or not appropriate for undergraduate education. My app simulates the physiology of 11 different channels - voltage-gated sodium, potassium, and chloride channels; channels causing A-current, M-current, and After-HyperPolarization (AHP) current; calcium-activated potassium channels; low threshold T type calcium channels and high threshold L type calcium channels; leak sodium and leak potassium channels. It can simulate these channels under both current clamp and voltage clamp conditions. As we change the input values on the app, the output can be instantaneously visualized on the web browser and downloaded as a data table to be further analyzed in a spreadsheet program. The app is a first of its kind, mobile-friendly and touch-screen-friendly online tool that can be used to teach undergraduate neuroscience classes. It can also be used by researchers on their local computers as part of an R library. It has intuitive touch-optimized controls, instantaneous graphical output, and yet is pedagogically robust for education and casual research purposes.\n\nNeuroscience education, ion channel biophysics, Hodgkin-Huxley simulation, web app for neuroscience

bioinformatics↗

bioRxiv2017

Increased prefrontal activity with aging reflects nonspecific neural responses rather than compensation

Elevated prefrontal cortex activity is often observed in healthy older adults despite declines in their memory and other cognitive functions. According to one view, this activity reflects a compensatory functional posterior-to-anterior shift, which contributes to maintenance of cognitive performance when posterior cortical function is impaired. Alternatively, the increased prefrontal activity may be less efficient or less specific, owing to structural and neurochemical changes accompanying aging. These accounts are difficult to distinguish on the basis of average activity levels within brain regions. Instead, we used a novel model-based multivariate analysis technique, applied to two independent functional magnetic resonance imaging datasets from an adult-lifespan human sample (N=123 and N=115; approximately half female). Standard analysis replicated the age-related increase in average prefrontal activation, but multivariate tests revealed that this activity did not carry additional information. The results contradict the hypothesis of a compensatory posterior-to-anterior shift. Instead, they suggest that the increased prefrontal activation reflects reduced efficiency or specificity, rather than compensation.\n\nSignificance statementFunctional brain imaging studies have often shown increased activity in prefrontal brain regions in older adults. This has been proposed to reflect a compensatory shift to greater reliance on prefrontal cortex, helping to maintain cognitive function. Alternatively, activity may become less specific as people age. This is a key question in the neuroscience of aging. In this study, we used novel tests of how different brain regions contribute to long- and short-term memory. We found increased activity in prefrontal cortex in older adults, but this activity carried less information about memory outcomes than activity in visual regions. These findings are relevant for understanding why cognitive abilities decline with age, suggesting that optimal function depends on successful brain maintenance rather than compensation.

neuroscience↗

bioRxiv2018

Motor learning decline with age is related to differences in the explicit memory system

The ability to adapt ones movements to changes in the environment is fundamental in everyday life, but this ability changes across the lifespan. Although often regarded as an implicit process, recent research has also linked motor adaptation with explicit learning processes. To understand how these processes contribute to differences in motor adaptation with age, we combined a visuomotor learning paradigm with cognitive tasks that measure implicit and explicit processes, and structural brain imaging. In a large population-based cohort from the Cambridge Centre for Ageing and Neuroscience (n=322, aged 18-89 years) we first confirmed that the degree of adaptation to an angular perturbation of visual feedback declined with age. There were no associations between adaptation and sensory attenuation, which has been previously hypothesised to contribute to implicit motor learning. However, interactions between age and scores on two independent memory tasks showed that explicit memory performance was a progressively stronger determinant of motor learning with age. Similarly, interactions between age and grey matter volume in the medial temporal lobe, amygdala and hippocampus showed that grey matter volume in these regions became a stronger determinant of adaptation in older adults. The convergent behavioural and structural imaging results suggest that age-related differences in the explicit memory system is a contributor to the decline in motor adaptation in older age. These results may reflect the more general compensatory reliance on cognitive strategies to maintain motor performance with age.\n\nSIGNIFICANCE STATEMENTThe central nervous system has a remarkable capacity to learn new motor skills and adapt to new environmental dynamics. This capacity is impaired with age, and in many brain disorders. We find that explicit memory performance and its associated medial temporal brain regions deteriorate with age, but the association between this brain system and individual differences in motor learning becomes stronger in older adults. We propose that these results reflect an increased reliance on cognition in order to maintain adaptive motor skill performance. This difference in learning strategy has implications for interventions to improve motor skills in older adults.

neuroscience↗

bioRxiv2013

Simultaneous optogenetic manipulation and calcium imaging in freely moving C. elegans

Editor:\n\nA fundamental goal of systems neuroscience is to probe the dynamics of neural activity that generate behavior. Here we present an instrument to simultaneously manipulate and monitor neural activity and behavior in the freely moving nematode Caenorhabditis elegans. We use the instrument to directly observe the relationship between sensory neuron activation, interneuron dynamics and locomotion in the mechanosensory circuit.\n\nPreviously in this journal, we presented an optogenetic illumination system capable of real-time light delivery with high spatial resolution to stimulate or inhibit specified targets in freely moving C. elegans [1]. This \"Colbert\" system and others like it [2] have been instrumental in defining neural coding of several behaviors in C. elegans including chemotaxis [3], nociception [4] and the escape response [5]. Here we integrate the Colbert s ...

Neuroscience↗

bioRxiv2014

Preparation of Synaptosomes from Postmortem Human Prefrontal Cortex

Synaptosomes are a popular type of isolated synaptic fraction intensively used in neuroscience and cell biology. They are prepared by layering on density gradients and thought to consist largely of axonal endings with attached postsynaptic structures (Morgan, 1976), in contrast to synaptoneurosomes (Hollingsworth et al, 1985) which are prepared by filtration and are thought to consist largely of pinched-off dendritic spines with attached presynaptic structures. Although most studies of synaptosomes have utilized rodent or primate tissue, a score of studies have employed human samples derived from surgical specimens or postmortem brain. We recently described the isolation of synaptosomes from human postmortem prefrontal cortex to study the expression of synaptic microRNAs and other small RNAs in depression, schizophrenia and bipolar disorder (Smalheiser et al, 2014). This protocol alluded to methods and modifications that are scattered among several publications, and did not explain the reasons for the procedures chosen. Because our protocol differs from other published human synaptosome protocols in a variety of respects, we present here a detailed description of synaptosome preparation that should facilitate the use of this standardized synaptic fraction by other workers.

Neuroscience↗

bioRxiv2014

Bonsai: An event-based framework for processing and controlling data streams

The design of modern scientific experiments requires the control and monitoring of many parallel data streams. However, the serial execution of programming instructions in a computer makes it a challenge to develop software that can deal with the asynchronous, parallel nature of scientific data. Here we present Bonsai, a modular, high-performance, open-source visual programming framework for the acquisition and online processing of data streams. We describe Bonsais core principles and architecture and demonstrate how it allows for flexible and rapid prototyping of integrated experimental designs in neuroscience. We specifically highlight different possible applications which require the combination of many different hardware and software components, i0ncluding behaviour video tracking, electrophysiology and closed-loop control of stimulation parameters.

Neuroscience↗

bioRxiv2014

Local Fields of the Hippocampus: More than Meets the Eye

A standard methodology in systems neuroscience is to establish correlations between the brain and the world. These correlations have been robustly observed when probing the activity of single neurons. For example, there are cells that activate vigorously when subjects are presented with images of certain celebrities (e.g. the \"Jennifer Aniston\" neuron, Quiroga et al., 2005). While single neuron measurements provide remarkable insights into the functional specificity of different brain areas, it remains unclear how single cell activity arises from, and contributes to, the collective activity of brain networks. New technologies allow for the simultaneous recording of spikes from hundreds of cells, yet we are still far from observing entire neuronal circuits in action.\n\nAn alternative approach in monitoring neuronal populations is to measure local field potentials (LFPs). These potentials arise from the coordinated activity of thousands of nearby neurons. LFPs often exhibit oscillations of various frequencies, which correlate with different states of awareness, such as alertness, relaxation, or drowsiness. However, establishing correlations between LFPs and specific behavioral events can be challenging, because the contributions of different neurons to the LFP usually cannot be distinguished. In this chapter, we examine LFPs recorded from the rat hippocampus. At first glance, these LFPs appear to bear little relation to the subjects changing environment. However, through appropriate signal processing, we find that carried in the structure of the LFP is a remarkably precise stream of information (Agarwal et al., 2014).

Neuroscience↗

bioRxiv2015

Building the Ferretome

Databases of structural connections of the mammalian brain, such as CoCoMac (cocomac.g-node.org) or BAMS (brancusi.usc.edu), are valuable resources for the analysis of brain connectivity and the modeling of brain dynamics in species such as the non-human primate or the rodent, and have also contributed to the computational modeling of the human brain. Another model species that is widely used in electrophysiological or developmental studies is the ferret; however, no systematic compilation of brain connectivity is currently available for this species. Thus, we have started developing a database of anatomical connections and architectonic features of the ferret brain (the Ferretome, www.ferretome.org). The main goals of this database project are: (1) to assemble structural information on the ferret brain that is currently widely distributed in the literature or in different in-house laboratory databases into a single resource which is open to the scientific community; (2) to try and build an extendable community resource that is beneficial to researchers in neuroinformatics and computational neuroscience, as well as to neuroanatomists, by adding value to their data through algorithms for efficient data representation, analysis and visualization; (3) to create techniques for the representation of quantitative and raw data; and (4) to expand existing database ontologies in order to accommodate further neuroarchitectural information for identifying essential relations between brain structure and connections.\n\nThe Ferretome database has adapted essential features of the CoCoMac methodology and legacy. In particular, its data model is derived from CoCoMac. It also uses a semantic parcellation of ferret brain regions as well as a logical brain maps transformation algorithm (objective relational transformation, ORT). The database is being developed in MySQL and has been populated with literature reports on tract tracing observations in the ferret brain using a custom-designed web interface that allows efficient and validated simultaneous input and proofreading by multiple curators. The database is also equipped with a web interface for generating output data that was designed with non-computer science specialist users in mind. This interface can be extended to produce connectivity matrices in several formats including a graphical representation superimposed on established ferret brain maps. An important feature of the Ferretome database is the possibility to trace back entries in connectivity matrices to the original studies archived in the system.\n\nCurrently, the Ferretome contains 50 reports on connections comprising 20 injection reports with more than 150 labeled source and target areas, the majority reflecting connectivity of subcortical nuclei. We hope that the Ferretome database will become a useful resource for neuroinformatics and neural modeling, and will support studies of the ferret brain as well as facilitate advances in comparative studies of mesoscopic brain connectivity.

Neuroscience↗

bioRxiv2015

Crossmodal integration improves sensory detection thresholds in the ferret

During the last two decades ferrets (Mustela putorius) have been established as a highly efficient animal model in different fields in neuroscience. Here we asked whether ferrets integrate sensory information according to the same principles established for other species. Since only few methods and protocols are available for behaving ferrets we developed a head-free, body-restrained approach allowing a standardized stimulation position and the utilization of the ferrets natural response behavior. We established a behavioral paradigm to test audiovisual integration in the ferret. Animals had to detect a brief auditory and/or visual stimulus presented either left or right from their midline. We first determined detection thresholds for auditory amplitude and visual contrast. In a second step, we combined both modalities and compared psychometric fits and the reaction times between all conditions. We employed Maximum Likelihood Estimation (MLE) to model bimodal psychometric curves and to investigate whether ferrets integrate modalities in an optimal manner. Furthermore, to test for a redundant signal effect we pooled the reaction times of all animals to calculate a race model. We observed that bimodal detection thresholds were reduced and reaction times were faster in the bimodal compared to unimodal conditions. The race model and MLE modeling showed that ferrets integrate modalities in a statistically optimal fashion. Taken together, the data indicate that principles of multisensory integration previously demonstrated in other species also apply to crossmodal processing in the ferret.

Neuroscience↗

bioRxiv2015

A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes

ObjectiveBrain-machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI.\n\nApproachSpikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together.\n\nMain ResultsLMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor.\n\nSignificanceThese findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs.\n\nSuggested PACS 87.19.L-Neuroscience\n87.19.lu motor systems\n87.19.rs Movement\n87.19.R-Mechanical and electrical properties of tissues and organs\n87.85.E-Neural Prosthetics\n87.85.Wc Neural engineering\n87.85.dd brain-machine interface\n87.85.Ng Biological signal processing\n\n\nIOP Subjects Medical Physics, Biological Physics

Neuroscience↗

bioRxiv2015

Fast ventral stream neural activity enables rapid visual categorization

AO_SCPLOWBSTRACTC_SCPLOWPrimates can recognize objects embedded in complex natural scenes in a glimpse. Rapid categorization paradigms have been extensively used to study our core perceptual abilities when the visual system is forced to operate under strong time constraints. However, the neural underpinning of rapid categorization remains to be understood, and the incredible speed of sight has yet to be reconciled with modern ventral stream cortical theories of shape processing.\n\nHere we recorded multichannel subdural electrocorticogram (ECoG) signals from intermediate areas (V4/PIT) of the ventral stream of the visual cortex while monkeys were actively engaged in detecting the presence or absence of animal targets in natural scenes. Using multivariate pattern analysis (MVPA) techniques, we quantified at millisecond precision task-relevant signals conveyed by ECoG data. Reliable neural decoding was possible shortly after stimulus onset from single trials with a degree of generalization to experimental manipulations closely mimicking monkeys accuracy and reaction time.\n\nTogether, the present study suggests that rapid ventral stream neural activity induces a selective task-relevant signal subsequently used to drive visual categorization.\n\nClassificationBiological sciences / Neuroscience

Neuroscience↗

bioRxiv2015

Long term recordings with immobile silicon probes in the mouse cortex

A key experimental approach in neuroscience involves measuring neuronal activity in behaving animals with extracellular chronic recordings. Such chronic recordings were initially made with single electrodes and tetrodes, and are now increasingly performed with high-density, high-count silicon probes. A common way to achieve long-term chronic recording is to attach the probes to microdrives that progressively advance them into the brain and isolate them from mechanical forces. Here we report, however, that such microdrives are not strictly necessary. Indeed, we obtained high-quality recordings in both head-fixed and freely moving mice for several months following the implantation of immobile chronic probes. Probes implanted into the primary visual cortex yielded well-isolated single units whose spike waveform and orientation tuning were highly reproducible over time. Although electrode drift was not completely absent, at least 70% of neurons retained their waveform across days. Thus, immobile silicon probes represent a straightforward and reliable technique to obtain stable, long-term population recordings in mice, and to follow the activity of populations of well-isolated neurons over multiple days.

Neuroscience↗

bioRxiv2015

A New method to detect Event-Related Potentials based on Pearson’s Correlation

Event-Related Potentials (ERPs) are widely used in Brain-Computer Interface applications and in neuroscience.\n\nNormal EEG activity is rich in background noise and therefore, in order to detect ERPs, it is usually necessary to take the average from multiple trials to reduce the effects of this noise.\n\nThe noise produced by EEG activity itself is not correlated with the ERP waveform and so, by calculating the average, the noise is decreased by a factor inversely proportional to the square root of N, where N is the number of averaged epochs.\n\nThis is the easiest strategy currently used to detect ERPs, which is based on calculating the average of each ERPs waveform, these waveforms being time-and phase-locked.\n\nIn this paper a new method called GW6 is proposed, which calculates the ERP using a mathematical method based only on Pearson's Correlation.\n\nThis results in a graph with the same time resolution as the classical ERP and which contains only positive peaks representing the increase -in consonance to the stimuli - in EEG signal correlation over all channels.\n\nThis new method is also useful for selectively identifying and highlighting any hidden components of the ERP response that are not phase-locked, and that are usually hidden in the standard and simple method based on the averaging of all the epochs.\n\nThese hidden components seem to be caused by variations (between each successive stimulus) of the ERP's inherent phase latency period (jitter), although the same stimulus across all EEG channels produces a reasonably constant phase.\n\nFor this reason, this new method could be very helpful to investigate these hidden components of the ERP response and to develop applications for scientific and medical purposes.\n\nMoreover, this new method is more resistant to EEG artifacts than the standard calculations of the average.\n\nThe method we are proposing can be directly used in the form of a process written in the well known Matlab programming language and can be easily and quickly written in any other software language.

Neuroscience↗

bioRxiv2015

Estimating directed connectivity from cortical recordings and reconstructed sources

In cognitive neuroscience, electrical brain activity is most commonly recorded at the scalp. In order to infer the contributions and connectivity of underlying neuronal sources within the brain, it is necessary to reconstruct sensor data at the source level. Several approaches to this reconstruction have been developed, thereby solving the so-called implicit inverse problem (Michel et al. 2004). However, a unifying premise against which to validate these source reconstructions is seldom available. The dataset provided in this work, in which brain activity is simultaneously recorded on the scalp (non-invasively) by electroencephalography (EEG) and on the cortex (invasively) by electrocorticography (ECoG), can be of a great help in this direction. These multimodal recordings were obtained from a macaque monkey under wakefulness and sedation. Our primary goal was to establish the connectivity architecture between two sources of interest (frontal and parietal), and to assess how their coupling changes over the conditions. We chose these sources because previous studies have shown that the connections between them are modified by anaesthesia (Boly et al. 2012). Our secondary goal was to evaluate the consistency of the connectivity results when analyzing sources recorded from invasive data (128 implanted ECoG sources) and source activity reconstructed from scalp recordings (19 EEG sensors) at the same locations as the ECoG sources. We conclude that the directed connectivity in the frequency domain between cortical sources reconstructed from scalp EEG is qualitatively similar to the connectivity inferred directly from cortical recordings, using both data-driven (directed transfer function; DTF) and biologically grounded (dynamic causal modelling; DCM) methods. Furthermore, the connectivity changes identified were consistent with previous findings (Boly et al. 2012). Our findings suggest that inferences about directed connectivity based upon non-invasive electrophysiological data have construct validity in relation to invasive recordings.

Neuroscience↗

bioRxiv2015

Recovery of dynamics and function in spiking neural networks by closed-loop control

There is a growing interest in developing novel brain stimulation methods to control disease-related aberrant neural activity and to address basic neuroscience questions. Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and, crucially, do not restore the original computations performed by the network. Thus, they are often accompanied by undesired side-effects. Here, we introduce delayed feedback control (DFC), a conceptually simple but effective method, to control pathological oscillations in spiking neural networks. Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity. Importantly, DFC besides steering the system back to a healthy state, it also recovers the computations performed by the underlying network. Finally, using our theory we isolate the role of single neuron and synapse properties in determining the stability of the closed-loop system.

Neuroscience↗