12,655 research outputs found
A roadmap to integrate astrocytes into Systems Neuroscience.
Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease
Origins of choice-related activity in mouse somatosensory cortex.
During perceptual decisions about faint or ambiguous sensory stimuli, even identical stimuli can produce different choices. Spike trains from sensory cortex neurons can predict trial-to-trial variability in choice. Choice-related spiking is widely studied as a way to link cortical activity to perception, but its origins remain unclear. Using imaging and electrophysiology, we found that mouse primary somatosensory cortex neurons showed robust choice-related activity during a tactile detection task. Spike trains from primary mechanoreceptive neurons did not predict choices about identical stimuli. Spike trains from thalamic relay neurons showed highly transient, weak choice-related activity. Intracellular recordings in cortex revealed a prolonged choice-related depolarization in most neurons that was not accounted for by feed-forward thalamic input. Top-down axons projecting from secondary to primary somatosensory cortex signaled choice. An intracellular measure of stimulus sensitivity determined which neurons converted choice-related depolarization into spiking. Our results reveal how choice-related spiking emerges across neural circuits and within single neurons
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CA1-projecting subiculum neurons facilitate object-place learning.
Recent anatomical evidence suggests a functionally significant back-projection pathway from the subiculum to the CA1. Here we show that the afferent circuitry of CA1-projecting subicular neurons is biased by inputs from CA1 inhibitory neurons and the visual cortex, but lacks input from the entorhinal cortex. Efferents of the CA1-projecting subiculum neurons also target the perirhinal cortex, an area strongly implicated in object-place learning. We identify a critical role for CA1-projecting subicular neurons in object-location learning and memory, and show that this projection modulates place-specific activity of CA1 neurons and their responses to displaced objects. Together, these experiments reveal a novel pathway by which cortical inputs, particularly those from the visual cortex, reach the hippocampal output region CA1. Our findings also implicate this circuitry in the formation of complex spatial representations and learning of object-place associations
Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex
Neocortical neurons have thousands of excitatory synapses. It is a mystery
how neurons integrate the input from so many synapses and what kind of
large-scale network behavior this enables. It has been previously proposed that
non-linear properties of dendrites enable neurons to recognize multiple
patterns. In this paper we extend this idea by showing that a neuron with
several thousand synapses arranged along active dendrites can learn to
accurately and robustly recognize hundreds of unique patterns of cellular
activity, even in the presence of large amounts of noise and pattern variation.
We then propose a neuron model where some of the patterns recognized by a
neuron lead to action potentials and define the classic receptive field of the
neuron, whereas the majority of the patterns recognized by a neuron act as
predictions by slightly depolarizing the neuron without immediately generating
an action potential. We then present a network model based on neurons with
these properties and show that the network learns a robust model of time-based
sequences. Given the similarity of excitatory neurons throughout the neocortex
and the importance of sequence memory in inference and behavior, we propose
that this form of sequence memory is a universal property of neocortical
tissue. We further propose that cellular layers in the neocortex implement
variations of the same sequence memory algorithm to achieve different aspects
of inference and behavior. The neuron and network models we introduce are
robust over a wide range of parameters as long as the network uses a sparse
distributed code of cellular activations. The sequence capacity of the network
scales linearly with the number of synapses on each neuron. Thus neurons need
thousands of synapses to learn the many temporal patterns in sensory stimuli
and motor sequences.Comment: Submitted for publicatio
Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of neoHebbian Three-Factor Learning Rules
Most elementary behaviors such as moving the arm to grasp an object or
walking into the next room to explore a museum evolve on the time scale of
seconds; in contrast, neuronal action potentials occur on the time scale of a
few milliseconds. Learning rules of the brain must therefore bridge the gap
between these two different time scales.
Modern theories of synaptic plasticity have postulated that the co-activation
of pre- and postsynaptic neurons sets a flag at the synapse, called an
eligibility trace, that leads to a weight change only if an additional factor
is present while the flag is set. This third factor, signaling reward,
punishment, surprise, or novelty, could be implemented by the phasic activity
of neuromodulators or specific neuronal inputs signaling special events. While
the theoretical framework has been developed over the last decades,
experimental evidence in support of eligibility traces on the time scale of
seconds has been collected only during the last few years.
Here we review, in the context of three-factor rules of synaptic plasticity,
four key experiments that support the role of synaptic eligibility traces in
combination with a third factor as a biological implementation of neoHebbian
three-factor learning rules
Spine Calcium Transients Induced by Synaptically-Evoked Action Potentials Can Predict Synapse Location and Establish Synaptic Democracy
CA1 pyramidal neurons receive hundreds of synaptic inputs at different distances from the soma. Distance-dependent synaptic scaling enables distal and proximal synapses to influence the somatic membrane equally, a phenomenon called “synaptic democracy”. How this is established is unclear. The backpropagating action potential (BAP) is hypothesised to provide distance-dependent information to synapses, allowing synaptic strengths to scale accordingly. Experimental measurements show that a BAP evoked by current injection at the soma causes calcium currents in the apical shaft whose amplitudes decay with distance from the soma. However, in vivo action potentials are not induced by somatic current injection but by synaptic inputs along the dendrites, which creates a different excitable state of the dendrites. Due to technical limitations, it is not possible to study experimentally whether distance information can also be provided by synaptically-evoked BAPs. Therefore we adapted a realistic morphological and electrophysiological model to measure BAP-induced voltage and calcium signals in spines after Schaffer collateral synapse stimulation. We show that peak calcium concentration is highly correlated with soma-synapse distance under a number of physiologically-realistic suprathreshold stimulation regimes and for a range of dendritic morphologies. Peak calcium levels also predicted the attenuation of the EPSP across the dendritic tree. Furthermore, we show that peak calcium can be used to set up a synaptic democracy in a homeostatic manner, whereby synapses regulate their synaptic strength on the basis of the difference between peak calcium and a uniform target value. We conclude that information derived from synaptically-generated BAPs can indicate synapse location and can subsequently be utilised to implement a synaptic democracy
Interpreting wde-band neural activity using convolutional neural networks
Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires considerable knowledge about the nature of the representation and often depends on manual operations. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning framework able to decode sensory and behavioral variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviors, brain regions, and recording techniques. Once trained, it can be analyzed to determine elements of the neural code that are informative about a given variable. We validated this approach using electrophysiological and calcium-imaging data from rodent auditory cortex and hippocampus as well as human electrocorticography (ECoG) data. We show successful decoding of finger movement, auditory stimuli, and spatial behaviors – including a novel representation of head direction - from raw neural activity
Novel Cardiac Mapping Approaches and Multimodal Techniques to Unravel Multidomain Dynamics of Complex Arrhythmias Towards a Framework for Translational Mechanistic-Based Therapeutic Strategies
[ES] Las arritmias cardĂacas son un problema importante para los sistemas de salud en el mundo desarrollado debido a su alta incidencia y prevalencia a medida que la poblaciĂłn envejece. La fibrilaciĂłn auricular (FA) y la fibrilaciĂłn ventricular (FV) se encuentran entre las arritmias más complejas observadas en la práctica clĂnica. Las consecuencias clĂnicas de tales alteraciones arrĂtmicas incluyen el desarrollo de eventos cardioembĂłlicos complejos en la FA, y repercusiones dramáticas debido a procesos fibrilatorios sostenidos que amenazan la vida infringiendo daño neurolĂłgico tras paro cardĂaco por FV, y que pueden provocar la muerte sĂşbita cardĂaca (MSC). Sin embargo, a pesar de los avances tecnolĂłgicos de las Ăşltimas dĂ©cadas, sus mecanismos intrĂnsecos se comprenden de forma incompleta y, hasta la fecha, las estrategias terapĂ©uticas carecen de una base mecanicista suficiente y poseen bajas tasas de Ă©xito.
Entre los mecanismos implicados en la inducciĂłn y perpetuaciĂłn de arritmias cardĂacas, como la FA, se cree que las dinámicas de las fuentes focales y reentrantes de alta frecuencia, en sus diferentes modalidades, son las fuentes primarias que mantienen la arritmia. Sin embargo, se sabe poco sobre los atractores, asĂ como, de la dinámica espacio-temporal de tales fuentes fibrilatorias primarias, especĂficamente, las fuentes focales o rotacionales dominantes que mantienen la arritmia. Por ello, se ha desarrollado una plataforma computacional, para comprender los factores (activos, pasivos y estructurales) determinantes, y moduladores de dicha dinámica. Esto ha permitido establecer un marco para comprender la compleja dinámica de los rotores con Ă©nfasis en sus propiedades deterministas para desarrollar herramientas basadas en los mecanismos para ayuda diagnĂłstica y terapĂ©utica.
Comprender los procesos fibrilatorios es clave para desarrollar marcadores y herramientas fisiolĂłgica- y clĂnicamente relevantes para la ayuda de diagnĂłstico temprano. EspecĂficamente, las propiedades espectrales y de tiempo-frecuencia de los procesos fibrilatorios han demostrado resaltar el comportamiento determinista principal de los mecanismos intrĂnsecos subyacentes a las arritmias y el impacto de tales eventos arrĂtmicos. Esto es especialmente relevante para determinar el pronĂłstico temprano de los supervivientes comatosos despuĂ©s de un paro cardĂaco debido a fibrilaciĂłn ventricular (FV).
Las tĂ©cnicas de mapeo electrofisiolĂłgico, el mapeo elĂ©ctrico y Ăłptico cardĂaco, han demostrado ser recursos muy valiosos para dar forma a nuevas hipĂłtesis y desarrollar nuevos enfoques mecanicistas y estrategias terapĂ©uticas mejoradas. Esta tecnologĂa permite además el trabajo multidisciplinar entre clĂnicos y bioingenieros, para el desarrollo y validaciĂłn de dispositivos y metodologĂas para identificar biomarcadores multi-dominio que permitan rastrear con precisiĂłn la dinámica de las arritmias identificando fuentes dominantes y atractores con alta precisiĂłn para ser dianas de estrategias terapeĂşticas innovadoras. Es por ello que uno de los objetivos fundamentales ha sido la implantaciĂłn y validaciĂłn de nuevos sistemas de mapeo en distintas configuraciones que sirvan de plataforma de desarrollo de nuevas estrategias terapeĂşticas. Aunque el mapeo panorámico es el mĂ©todo principal y más completo para rastrear simultáneamente biomarcadores electrofisiolĂłgicos, su adopciĂłn por la comunidad cientĂfica es limitada principalmente debido al coste elevado de la tecnologĂa. Aprovechando los avances tecnolĂłgicos recientes, nos hemos enfocado en desarrollar, y validar, sistemas de mapeo Ăłptico de alta resoluciĂłn para registro panorámico cardĂaco, utilizando modelos clĂnicamente relevantes para la investigaciĂłn básica y la bioingenierĂa.[CA] Les arĂtmies cardĂaques sĂłn un problema important per als sistemes de salut del mĂłn desenvolupat a causa de la seva alta incidència i prevalença a mesura que la poblaciĂł envelleix. La fibril·laciĂł auricular (FA) i la fibril·laciĂł ventricular (FV), es troben entre les arĂtmies mĂ©s complexes observades a la prĂ ctica clĂnica. Les conseqüències clĂniques d'aquests trastorns arĂtmics inclouen el desenvolupament d'esdeveniments cardioembòlics complexos en FA i repercussions dramĂ tiques a causa de processos fibril·latoris sostinguts que posen en perill la vida amb danys neurològics posteriors a la FV, que condueixen a una aturada cardĂaca i a la mort cardĂaca sobtada (SCD). Tanmateix, malgrat els avanços tecnològics de les darreres dècades, els seus mecanismes intrĂnsecs s'entenen de forma incompleta i, fins a la data, les estratègies terapèutiques no tenen una base mecanicista suficient i tenen baixes taxes d'èxit.
La majoria dels avenços en el desenvolupament de biomarcadors òptims i noves estratègies terapèutiques en aquest camp provenen de tècniques valuoses en la investigaciĂł de mecanismes d'arĂtmia. Entre els mecanismes implicats en la inducciĂł i perpetuaciĂł de les arĂtmies cardĂaques, es creu que les fonts primĂ ries subjacents a l'arĂtmia sĂłn les fonts focals reingressants d'alta freqüència dinĂ mica i AF, en les seves diferents modalitats. Tot i això, se sap poc sobre els atractors i la dinĂ mica espaciotemporal d'aquestes fonts primĂ ries fibril·ladores, especĂficament les fonts rotacionals o focals dominants que mantenen l'arĂtmia. Per tant, s'ha desenvolupat una plataforma computacional per entendre determinants actius, passius, estructurals i moduladors d'aquestes dinĂ miques. Això va permetre establir un marc per entendre la complexa dinĂ mica multidomini dels rotors amb ènfasi en les seves propietats deterministes per desenvolupar enfocaments mecanicistes per a l'ajuda i la terĂ pia diagnòstiques.
La comprensiĂł dels processos fibril·latoris Ă©s clau per desenvolupar puntuacions i eines rellevants fisiològicament i clĂnicament per ajudar al diagnòstic precoç. Concretament, les propietats espectrals i de temps-freqüència dels processos fibril·latoris han demostrat destacar un comportament determinista important dels mecanismes intrĂnsecs subjacents a les arĂtmies i l'impacte d'aquests esdeveniments arĂtmics. Mitjançant coneixements previs, processament de senyals, tècniques d'aprenentatge automĂ tic i anĂ lisi de dades, es va desenvolupar una puntuaciĂł de risc mecanicista a la aturada cardĂaca per FV.
Les tècniques de cartografia òptica cardĂaca i electrofisiològica han demostrat ser recursos inestimables per donar forma a noves hipòtesis i desenvolupar nous enfocaments mecanicistes i estratègies terapèutiques. Aquesta tecnologia ha permès durant molts anys provar noves estratègies terapèutiques farmacològiques o ablatives i desenvolupar mètodes multidominis per fer un seguiment precĂs de la dinĂ mica d'arrĂmies que identifica fonts i atractors dominants. Tot i que el mapatge panorĂ mic Ă©s el mètode principal per al seguiment simultani de parĂ metres electrofisiològics, la seva adopciĂł per part de la comunitat multidisciplinĂ ria d'investigaciĂł cardiovascular estĂ limitada principalment pel cost de la tecnologia. Aprofitant els avenços tecnològics recents, ens centrem en el desenvolupament i la validaciĂł de sistemes de mapes òptics de baix cost per a imatges panorĂ miques mitjançant models clĂnicament rellevants per a la investigaciĂł bĂ sica i la bioenginyeria.[EN] Cardiac arrhythmias are a major problem for health systems in the developed world due to their high incidence and prevalence as the population ages. Atrial fibrillation (AF) and ventricular fibrillation (VF), are amongst the most complex arrhythmias seen in the clinical practice. Clinical consequences of such arrhythmic disturbances include developing complex cardio-embolic events in AF, and dramatic repercussions due to sustained life-threatening fibrillatory processes with subsequent neurological damage under VF, leading to cardiac arrest and sudden cardiac death (SCD). However, despite the technological advances in the last decades, their intrinsic mechanisms are incompletely understood, and, to date, therapeutic strategies lack of sufficient mechanistic basis and have low success rates.
Most of the progress for developing optimal biomarkers and novel therapeutic strategies in this field has come from valuable techniques in the research of arrhythmia mechanisms. Amongst the mechanisms involved in the induction and perpetuation of cardiac arrhythmias such AF, dynamic high-frequency re-entrant and focal sources, in its different modalities, are thought to be the primary sources underlying the arrhythmia. However, little is known about the attractors and spatiotemporal dynamics of such fibrillatory primary sources, specifically dominant rotational or focal sources maintaining the arrhythmia. Therefore, a computational platform for understanding active, passive and structural determinants, and modulators of such dynamics was developed. This allowed stablishing a framework for understanding the complex multidomain dynamics of rotors with enphasis in their deterministic properties to develop mechanistic approaches for diagnostic aid and therapy.
Understanding fibrillatory processes is key to develop physiologically and clinically relevant scores and tools for early diagnostic aid. Specifically, spectral and time-frequency properties of fibrillatory processes have shown to highlight major deterministic behaviour of intrinsic mechanisms underlying the arrhythmias and the impact of such arrhythmic events. Using prior knowledge, signal processing, machine learning techniques and data analytics, we aimed at developing a reliable mechanistic risk-score for comatose survivors of cardiac arrest due to VF.
Cardiac optical mapping and electrophysiological mapping techniques have shown to be unvaluable resources to shape new hypotheses and develop novel mechanistic approaches and therapeutic strategies. This technology has allowed for many years testing new pharmacological or ablative therapeutic strategies, and developing multidomain methods to accurately track arrhymia dynamics identigying dominant sources and attractors. Even though, panoramic mapping is the primary method for simultaneously tracking electrophysiological parameters, its adoption by the multidisciplinary cardiovascular research community is limited mainly due to the cost of the technology. Taking advantage of recent technological advances, we focus on developing and validating low-cost optical mapping systems for panoramic imaging using clinically relevant models for basic research and bioengineering.Calvo Saiz, CJ. (2022). Novel Cardiac Mapping Approaches and Multimodal Techniques to Unravel Multidomain Dynamics of Complex Arrhythmias Towards a Framework for Translational Mechanistic-Based Therapeutic Strategies [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/182329TESI
Prefrontal cortex output circuits guide reward seeking through divergent cue encoding
The prefrontal cortex is a critical neuroanatomical hub for controlling motivated behaviours across mammalian species. In addition to intra-cortical connectivity, prefrontal projection neurons innervate subcortical structures that contribute to reward-seeking behaviours, such as the ventral striatum and midline thalamus. While connectivity among these structures contributes to appetitive behaviours, how projection-specific prefrontal neurons encode reward-relevant information to guide reward seeking is unknown. Here we use in vivo two-photon calcium imaging to monitor the activity of dorsomedial prefrontal neurons in mice during an appetitive Pavlovian conditioning task. At the population level, these neurons display diverse activity patterns during the presentation of reward-predictive cues. However, recordings from prefrontal neurons with resolved projection targets reveal that individual corticostriatal neurons show response tuning to reward-predictive cues, such that excitatory cue responses are amplified across learning. By contrast, corticothalamic neurons gradually develop new, primarily inhibitory responses to reward-predictive cues across learning. Furthermore, bidirectional optogenetic manipulation of these neurons reveals that stimulation of corticostriatal neurons promotes conditioned reward-seeking behaviour after learning, while activity in corticothalamic neurons suppresses both the acquisition and expression of conditioned reward seeking. These data show how prefrontal circuitry can dynamically control reward-seeking behaviour through the opposing activities of projection-specific cell populations
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