5 research outputs found

    Dynamic causal communication channels between neocortical areas

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    Processing of sensory information depends on the interactions between hierarchically connected neocortical regions, but it remains unclear how the activity in one area causally influences the activity dynamics in another and how rapidly such interactions change with time. Here, we show that the communication between the primary visual cortex (V1) and high-order visual area LM is context-dependent and surprisingly dynamic over time. By momentarily silencing one area while recording activity in the other, we find that both areas reliably affected changing subpopulations of target neurons within one hundred milliseconds while mice observed a visual stimulus. The influence of LM feedback on V1 responses became even more dynamic when the visual stimuli predicted a reward, causing fast changes in the geometry of V1 population activity and affecting stimulus coding in a context-dependent manner. Therefore, the functional interactions between cortical areas are not static but unfold through rapidly shifting communication subspaces whose dynamics depend on context when processing sensory information

    Neural dynamics of perceptual detection under temporal uncertainty

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    Tesis doctoral in茅dita le铆da en la Universidad Aut贸noma de Madrid, Facultad de Ciencias, Departamento de F铆sica Te贸rica. Fecha de lectura: 17-10-2014Among the large number of functions that compose our mental life, perception is arguably the most fundamental one. Perception is the cognitive process by which external sensory signals are transformed into meaningful information that represents our environment and guide decisions and behavior. What is the neural basis of this transformation? Several key issues limit our understanding of the neurobiology of perception and perceptual decision-making. First, the neural codes used by the brain to represent sensory information are still unclear. Second, perceptual decisions presumably arise from the coordinated activity of populations of neurons. However, the analytical tools best suited to study decision signals in neuronal populations remain unknown. Third, perception is not a passive process. On the contrary, external stimuli and internal brain states dynamically interact to give rise to percepts. In this thesis, I address these questions using computational simulations and neural data recorded while monkeys perform a vibrotactile detection task. Three fundamental issues are examined: (1) the dynamics of correlated variability, (2) the decoding of decisions from neural population's activity and (3) the neural mechanisms underlying the use of temporal expectations. I study the dynamics of choice-conditioned noise correlations and show that they reveal an internal component of the decision-making process. By developing novel statistical measures, I quantify how predictive is the activity of populations of cortical neurons about the subject's decision. As a result, I nd that a speci c subset of premotor cortex neurons unequivocally predict the animal's decision report. The vibrotactile detection task studied in this work requires subjects to make decisions under temporal uncertainty. I nd that subjects bene t from temporal expectations by modulating their response criterion over the course of a trial. I show that this modulation is represented by the population dynamics of premotor cortex neurons. A trained recurrent neural network reproduces the experimental ndings and reveals the dynamical mechanism implementing a exible response criterion. Knowledge about the probability of stimulation over time, acquired during training, is intrinsically encoded in the neural population activity, allowing a dynamic control of the response criterion to improve performanceEntre el gran n煤mero funciones cognitivas que componen nuestra vida mental, la percepci贸n es, quiz谩, la m谩s fundamental. La percepci贸n es el proceso mediante el cual el cerebro interpreta, organiza y da sentido a la gran cantidad de se帽ales sensoriales que recibe del mundo exterior. De esta forma, la informaci贸n sensorial es transformada en una representaci 贸n relevante de nuestro entorno, 煤til para gu铆ar nuestro comportamiento. 驴Cu谩l es el correlato neuronal de esta transformaci贸n? Hay varias cuestiones clave que limitan nuestro entendimiento de la neurobiolog铆a de la percepci贸n y de las decisiones perceptuales. En primer lugar, el c贸digo neuronal que el cerebro utiliza para representar informaci贸n sensorial no es del todo claro. En segundo lugar, las decisiones presumiblemente emergen de la actividad conjunta de un gran n煤mero de neuronas. Sin embargo, las herramientas anal铆ticas m谩s adecuadas para estudiar estas se帽ales poblacionales todava no son enteramente conocidas. En tercer lugar, la percepci贸n no es un proceso pasivo. Por el contrario, los est铆mulos externos y los estados internos del cerebro interact煤an din谩micamente para construir nuestra experiencia subjetiva. En esta tesis, abordo estos asuntos utilizando simulaciones computacionales y analizando registros neuronales obtenidos en monos mientras realizan una tarea de detecci贸n vibrot谩ctil. Tres cuestiones fundamentales son examinadas: (1) la din谩mica de la variabilidad neuronal correlacionada, (2) la decodi_- caci贸n de se帽ales de decisi贸n a partir de la actividad de poblaciones de neuronas y (3) los mecanismos neuronales que subyacen a la incorporaci 贸n de expectativas temporales en el proceso de decisi贸n. Estudiando la din谩mica de las correlaciones del ruido, muestro que 茅st谩s revelan una componente interna del proceso de decisi贸n. Mediante el desarrollo de nuevas medidas estad铆sticas, cuantifico el poder predictivo de la actividad de conjuntos de neuronas acerca de las decisiones del sujeto. Como resultado, encuentro que la decisi贸n del animal puede predecirse inequ铆vocamente a partir de la actividad de poblaciones espec铆ficas de neuronas de la corteza premotora. La tarea de detecci贸n estudiada en esta tesis require que los animales tomen decisiones en un contexto de incertidumbre temporal. En esta tesis muestro que los sujetos construyen y utilizan expectativas temporales para aumentar su rendimiento mediante la modulaci贸n de su criterio de respuesta a trav茅s del tiempo. Adem谩s, encuentro que la actividad de las neuronas de la corteza premotora es consistente con un mecanismo neuronal espec铆fico para implementar esta modulaci贸n. Finalmente, derivo un modelo de red recurrente que reproduce los resultados experimentales y permite estudiar la estructura din谩mica subyacente. El conocimiento previo acerca de la probabilidad de estimulaci贸n como funci贸n del tiempo, adquirido durante el entrenamiento, puede ser intr铆nsecamente codificado por una poblaci贸n de neuronas, permitiendo el control din谩mico del criterio de durante el proceso de decisi贸n

    Taming neuronal noise with large networks

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    How does reliable computation emerge from networks of noisy neurons? While individual neurons are intrinsically noisy, the collective dynamics of populations of neurons taken as a whole can be almost deterministic, supporting the hypothesis that, in the brain, computation takes place at the level of neuronal populations. Mathematical models of networks of noisy spiking neurons allow us to study the effects of neuronal noise on the dynamics of large networks. Classical mean-field models, i.e., models where all neurons are identical and where each neuron receives the average spike activity of the other neurons, offer toy examples where neuronal noise is absorbed in large networks, that is, large networks behave like deterministic systems. In particular, the dynamics of these large networks can be described by deterministic neuronal population equations. In this thesis, I first generalize classical mean-field limit proofs to a broad class of spiking neuron models that can exhibit spike-frequency adaptation and short-term synaptic plasticity, in addition to refractoriness. The mean-field limit can be exactly described by a multidimensional partial differential equation; the long time behavior of which can be rigorously studied using deterministic methods. Then, we show that there is a conceptual link between mean-field models for networks of spiking neurons and latent variable models used for the analysis of multi-neuronal recordings. More specifically, we use a recently proposed finite-size neuronal population equation, which we first mathematically clarify, to design a tractable Expectation-Maximization-type algorithm capable of inferring the latent population activities of multi-population spiking neural networks from the spike activity of a few visible neurons only, illustrating the idea that latent variable models can be seen as partially observed mean-field models. In classical mean-field models, neurons in large networks behave like independent, identically distributed processes driven by the average population activity -- a deterministic quantity, by the law of large numbers. The fact the neurons are identically distributed processes implies a form of redundancy that has not been observed in the cortex and which seems biologically implausible. To show, numerically, that the redundancy present in classical mean-field models is unnecessary for neuronal noise absorption in large networks, I construct a disordered network model where networks of spiking neurons behave like deterministic rate networks, despite the absence of redundancy. This last result suggests that the concentration of measure phenomenon, which generalizes the ``law of large numbers'' of classical mean-field models, might be an instrumental principle for understanding the emergence of noise-robust population dynamics in large networks of noisy neurons

    The Neural Basis of a Cognitive Map

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    It has been proposed that as animals explore their environment they build and maintain a cognitive map, an internal representation of their surroundings (Tolman, 1948). We tested this hypothesis using a task designed to assess the ability of rats to make a spatial inference (take a novel shortcut)(Roberts et al., 2007). Our findings suggest that rats are unable to make a spontaneous spatial inference. Furthermore, they bear similarities to experiments which have been similarly unable to replicate or support Tolman鈥檚 (1948) findings. An inability to take novel shortcuts suggests that rats do not possess a cognitive map (Bennett, 1996). However, we found evidence of alternative learning strategies, such as latent learning (Tolman & Honzik, 1930b) , which suggest that rats may still be building such a representation, although it does not appear they are able to utilise this information to make complex spatial computations. Neurons found in the hippocampus show remarkable spatial modulation of their firing rate and have been suggested as a possible neural substrate for a cognitive map (O'Keefe & Nadel, 1978). However, the firing of these place cells often appears to be modulated by features of an animal鈥檚 behaviour (Ainge, Tamosiunaite, et al., 2007; Wood, Dudchenko, Robitsek, & Eichenbaum, 2000). For instance, previous experiments have demonstrated that the firing rate of place fields in the start box of some mazes are predictive of the animal鈥檚 final destination (Ainge, Tamosiunaite, et al., 2007; Ferbinteanu & Shapiro, 2003). We sought to understand whether this prospective firing is in fact related to the goal the rat is planning to navigate to or the route the rat is planning to take. Our results provide strong evidence for the latter, suggesting that rats may not be aware of the location of specific goals and may not be aware of their environment in the form of a contiguous map. However, we also found behavioural evidence that rats are aware of specific goal locations, suggesting that place cells in the hippocampus may not be responsible for this representation and that it may reside elsewhere (Hok, Chah, Save, & Poucet, 2013). Unlike their typical activity in an open field, place cells often have multiple place fields in geometrically similar areas of a multicompartment environment (Derdikman et al., 2009; Spiers et al., 2013). For example, Spiers et al. (2013) found that in an environment composed of four parallel compartments, place cells often fired similarly in multiple compartments, despite the active movement of the rat between them. We were able to replicate this phenomenon, furthermore, we were also able to show that if the compartments are arranged in a radial configuration this repetitive firing does not occur as frequently. We suggest that this place field repetition is driven by inputs from Boundary Vector Cells (BVCs) in neighbouring brain regions which are in turn greatly modulated by inputs from the head direction system. This is supported by a novel BVC model of place cell firing which predicts our observed results accurately. If place cells form the neural basis of a cognitive map one would predict spatial learning to be difficult in an environment where repetitive firing is observed frequently (Spiers et al., 2013). We tested this hypothesis by training animals on an odour discrimination task in the maze environments described above. We found that rats trained in the parallel version of the task were significantly impaired when compared to the radial version. These results support the hypothesis that place cells form the neural basis of a cognitive map; in environments where it is difficult to discriminate compartments based on the firing of place cells, rats find it similarly difficult to discriminate these compartments as shown by their behaviour. The experiments reported here are discussed in terms of a cognitive map, the likelihood that such a construct exists and the possibility that place cells form the neural basis of such a representation. Although the results of our experiments could be interpreted as evidence that animals do not possess a cognitive map, ultimately they suggest that animals do have a cognitive map and that place cells form a more than adequate substrate for this representation

    Amplification in cortical networks

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    "The brain is active all the time: it displays substantial spontaneous activity in awake and sleeping animals which doesn鈥檛 receive sensory inputs. There are many open questions regarding this spontaneous activity: How is it generated? What is its function? What is its impact on sensory processing?(...)
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