1,194 research outputs found
Neutral coding - A report based on an NRP work session
Neural coding by impulses and trains on single and multiple channels, and representation of information in nonimpulse carrier
Peripheral and Central Mechanisms of Temporal Pattern Recognition
Encoding information into the timing patterns of action potentials, or spikes, is a strategy used broadly in neural circuits. This type of coding scheme requires downstream neurons to be sensitive to the temporal patterns of presynaptic inputs. Indeed, neurons with temporal filtering properties have been found in a wide range of sensory pathways. However, how such response properties arise was previously not well understood. The goal of my dissertation research has been to elucidate how temporal filtering by single neurons contributes to the behavioral ability to recognize timing patterns in communication signals.
I have addressed this question using mormyrid weakly electric fish, which vary the time intervals between successive electric pulses to communicate. Fish detect these signals with sensory receptors in their skin. In the majority of species, these receptors fire a single spike in response to each electric pulse. Spiking receptors faithfully encode the interpulse intervals in communication signals into interspike intervals, which are then decoded by interval-selective midbrain neurons. Using in vivo intracellular recordings from awake fish during sensory stimulation, I found that short-term depression and temporal summation play important roles in establishing single-neuron interval selectivity. Moreover, the combination of short-term depression and temporal summation in the circuit resulted in greater diversity of interval tuning properties across the population of neurons, which would increase the population’s ability to detect temporally patterned communication signals. Indeed, I found that the responses of single interval-selective neurons were sensitive to subtle variation in the timing patterns of a specific communication display produced by different individuals.
A subset of mormyrid species has sensory receptors that produce spontaneously oscillating potentials. How the electrosensory system of these species established sensitivity to temporally patterned communication signals was completely unknown. Using in vivo extracellular recordings, I demonstrated that these receptors encode sensory stimuli into phase resets, which is the first clear instance of information coding by oscillatory phase reset. Furthermore, the ongoing oscillations conferred enhanced sensitivity to fast temporal patterns that are only found in the communication signals of a large group of fish. Behavioral playback experiments provided further support for the hypothesis that oscillating receptors are specialized for detecting communication signals produced by a group of conspecifics, which is a novel role for a sensory receptor. These findings demonstrate that temporal pattern sensitivity, which was previously thought to be a central processing problem, can also arise from peripheral filtering through a novel oscillatory phase reset mechanism
Spatial processing of conspecific signals in weakly electric fish: from sensory image to neural population coding
In this dissertation, I examine how an animal’s nervous system encodes spatially realistic conspecific signals in their environment and how the encoding mechanisms support behavioral sensitivity. I begin by modeling changes in the electrosensory signals exchanged by weakly electric fish in a social context. During this behavior, I estimate how the spatial structure of conspecific stimuli influences sensory responses at the electroreceptive periphery. I then quantify how space is represented in the hindbrain, specifically in the primary sensory area called the electrosensory lateral line lobe. I show that behavioral sensitivity is influenced by the heterogeneous properties of the pyramidal cell population. I further demonstrate that this heterogeneity serves to start segregating spatial and temporal information early in the sensory pathway. Lastly, I characterize the accuracy of spatial coding in this network and predict the role of network elements, such as correlated noise and feedback, in shaping the spatial information. My research provides a comprehensive understanding of spatial coding in the first stages of sensory processing in this system and allows us to better understand how network dynamics shape coding accuracy
Information processing in dissociated neuronal cultures of rat hippocampal neurons
One of the major aims of Systems Neuroscience is to understand how the nervous system
transforms sensory inputs into appropriate motor reactions. In very simple cases sensory neurons
are immediately coupled to motoneurons and the entire transformation becomes a simple reflex, in
which a noxious signal is immediately transformed into an escape reaction. However, in the most complex behaviours, the nervous system seems to analyse in detail the sensory inputs and is performing some kind of information processing (IP). IP takes place at many different levels of the nervous system: from the peripheral nervous system, where sensory stimuli are detected and converted into electrical pulses, to the central nervous system, where features of sensory stimuli are extracted, perception takes place and actions and motions are coordinated. Moreover, understanding the basic computational properties of the nervous system, besides being at the core of Neuroscience,
also arouses great interest even in the field of Neuroengineering and in the field of Computer
Science. In fact, being able to decode the neural activity can lead to the development of a new
generation of neuroprosthetic devices aimed, for example, at restoring motor functions in severely
paralysed patients (Chapin, 2004). On the other side, the development of Artificial Neural Networks (ANNs) (Marr, 1982; Rumelhart & McClelland, 1988; Herz et al., 1981; Hopfield, 1982; Minsky & Papert, 1988) has already proved that the study of biological neural networks may lead to the development and to the design of new computing algorithms and devices. All nervous systems are based on the same elements, the neurons, which are computing devices which, compared to silicon components, are much slower and much less reliable. How are nervous systems of all living species able to survive being based on slow and poorly reliable components? This obvious and na\uefve question is equivalent to characterizing IP in a more quantitative way.
In order to study IP and to capture the basic computational properties of the nervous system,
two major questions seem to arise. Firstly, which is the fundamental unit of information processing:
2 single neurons or neuronal ensembles? Secondly, how is information encoded in the neuronal firing? These questions - in my view - summarize the problem of the neural code.
The subject of my PhD research was to study information processing in dissociated neuronal
cultures of rat hippocampal neurons. These cultures, with random connections, provide a more general view of neuronal networks and assemblies, not depending on the circuitry of a neuronal network in vivo, and allow a more detailed and careful experimental investigation. In order to record the activity of a large ensemble of neurons, these neurons were cultured on multielectrode arrays (MEAs) and multi-site stimulation was used to activate different neurons and pathways of the
network. In this way, it was possible to vary the properties of the stimulus applied under a
controlled extracellular environment. Given this experimental system, my investigation had two
major approaches. On one side, I focused my studies on the problem of the neural code, where I studied in particular information processing at the single neuron level and at an ensemble level,
investigating also putative neural coding mechanisms. On the other side, I tried to explore the possibility of using biological neurons as computing elements in a task commonly solved by conventional silicon devices: image processing and pattern recognition. The results reported in the first two chapters of my thesis have been published in two
separate articles. The third chapter of my thesis represents an article in preparation
Dopaminergic Modulation Shapes Sensorimotor Processing in the Drosophila Mushroom Body
To survive in a complex and dynamic environment, animals must adapt their behavior based on their current needs and prior experiences. This flexibility is often mediated by neuromodulation within neural circuits that link sensory representations to alternative behavioral responses depending on contextual cues and learned associations. In Drosophila, the mushroom body is a prominent neural structure essential for olfactory learning. Dopaminergic neurons convey salient information about reward and punishment to the mushroom body in order to adjust synaptic connectivity between Kenyon cells, the neurons representing olfactory stimuli, and the mushroom body output neurons that ultimately influence behavior. However, we still lack a mechanistic understanding of how the dopaminergic neurons represent the moment-tomoment experience of a fly and drive changes in this sensory-to-motor transformation. Furthermore, very little is known about how the output neuron pathways lead to the execution of appropriate odor-related behaviors. We took advantage of the mushroom body’s modular circuit organization to investigate how the dopaminergic neuron population encodes different contextual cues. In vivo functional imaging of the dopaminergic neurons reveals that they represent both external reinforcement stimuli, like sugar rewards or punitive electric shock, as well as the fly’s motor state, through coordinated and partially antagonistic activity patterns across the population. This multiplexing of motor and reward signals by the dopaminergic neurons parallels the dual roles of dopaminergic inputs to the vertebrate basal ganglia, thus demonstrating a conserved link between these distantly related neural circuits. We proceed to demonstrate that this dopaminergic signal in the mushroom body modifies neurotransmission with synaptic specificity and temporal precision to coordinately regulate the propagation of sensory signals through the output neurons. To explore how these output pathways ultimately influence olfactory navigation we have developed a closed loop olfactory paradigm in which we can monitor and manipulate the mushroom body output neurons as a fly navigates in a virtual olfactory environment. We have begun to probe the mushroom body circuitry in the context of olfactory navigation. These preliminary investigations have led to the identification of putative pathways for linking mushroom body output with the circuits that implement odor-tracking behavior and the characterization of the complex sensorimotor representations in the dopaminergic network. Our work reveals that the Drosophila dopaminergic system modulates mushroom body output at both acute and enduring timescales to guide immediate behaviors and learned responses
Mechanisms and functional roles of glutamatergic synapse diversity in a cerebellar circuit
Synaptic currents display a large degree of heterogeneity of their temporal characteristics, but the functional role of such heterogeneities remains unknown. We investigated in rat cerebellar slices synaptic currents in Unipolar Brush Cells (UBCs), which generate intrinsic mossy fibers relaying vestibular inputs to the cerebellar cortex. We show that UBCs respond to sinusoidal modulations of their sensory input with heterogeneous amplitudes and phase shifts. Experiments and modeling indicate that this variability results both from the kinetics of synaptic glutamate transients and from the diversity of postsynaptic receptors. While phase inversion is produced by an mGluR2-activated outward conductance in OFF-UBCs, the phase delay of ON UBCs is caused by a late rebound current resulting from AMPAR recovery from desensitization. Granular layer network modeling indicates that phase dispersion of UBC responses generates diverse phase coding in the granule cell population, allowing climbing-fiber-driven Purkinje cell learning at arbitrary phases of the vestibular input
Evaluation and neurocomputational modelling of visual adaptation to optically induced distortions
Spatial geometrical distortions are major artefacts in vision aid optical spectacles. Progressive additional lenses (PALs) are among such spectacles incurring inherent distortions. Distortions alter perceived features of the natural environment and are one of the causes for visual discomforts, such as apparent motion perception and spatial disorientation, experienced by novice spectacle wearers. Thus, fast and efficient visual adaptation to the distortions is a necessity to increase the users’ comfort and consequently overcome the related problems, e.g. risk of fall in the elderly when using PALs. Inspired by this necessity, the work is targeted to investigate the visual mechanisms underlying adaptation to distortions, in particular in PALs. Psychophysical procedures are employed to probe the characteristics of the neural mechanisms underlying the adaptation process in natural viewing conditions. With psychophysical approaches, three main properties of distortion adaptation are revealed; its cortical origin, the reference frame in which it is achieved and its long-term temporal dynamics. In order to discern how the functional organization of neurons enables the visual system to carry out a robust distortion adaptation in a natural environment, biologically plausible recurrent neural network models are utilized. Prediction performance of model variants with different neural network complexity and temporal dynamics of operation were assessed. From the model simulations, major functional roles of recurrent bottom-up and top-down cortical interactions in neural response tuning and in mediating adaptation at different time scales were depicted. The outcomes would further contribute to suggest a solution for facilitating adaptation. The relevance of the research within these aforementioned studies is not restricted to PALs but extends to distortions in other daily used optical utilities, such as virtual reality (VR) displays. Optical distortions are also artefacts in artificial sensory systems, like lens distortions in cameras used in machine vision. Understanding the neural correlates of distortion adaptation in human vision will thereby elicit characteristic features of robust and flexible neural systems to be implemented in brain inspired artificial vision
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