2,502 research outputs found
Two-photon imaging and analysis of neural network dynamics
The glow of a starry night sky, the smell of a freshly brewed cup of coffee
or the sound of ocean waves breaking on the beach are representations of the
physical world that have been created by the dynamic interactions of thousands
of neurons in our brains. How the brain mediates perceptions, creates thoughts,
stores memories and initiates actions remains one of the most profound puzzles
in biology, if not all of science. A key to a mechanistic understanding of how
the nervous system works is the ability to analyze the dynamics of neuronal
networks in the living organism in the context of sensory stimulation and
behaviour. Dynamic brain properties have been fairly well characterized on the
microscopic level of individual neurons and on the macroscopic level of whole
brain areas largely with the help of various electrophysiological techniques.
However, our understanding of the mesoscopic level comprising local populations
of hundreds to thousands of neurons (so called 'microcircuits') remains
comparably poor. In large parts, this has been due to the technical
difficulties involved in recording from large networks of neurons with
single-cell spatial resolution and near- millisecond temporal resolution in the
brain of living animals. In recent years, two-photon microscopy has emerged as
a technique which meets many of these requirements and thus has become the
method of choice for the interrogation of local neural circuits. Here, we
review the state-of-research in the field of two-photon imaging of neuronal
populations, covering the topics of microscope technology, suitable fluorescent
indicator dyes, staining techniques, and in particular analysis techniques for
extracting relevant information from the fluorescence data. We expect that
functional analysis of neural networks using two-photon imaging will help to
decipher fundamental operational principles of neural microcircuits.Comment: 36 pages, 4 figures, accepted for publication in Reports on Progress
in Physic
Novel convolution-based signal processing techniques for an artificial olfactory mucosa
As our understanding of the human olfactory system has grown, so has our ability to design artificial devices that mimic its functionality, so called electronic noses (e-noses). This has led to the development of a more sophisticated biomimetic system known as an artificial olfactory mucosa (e-mucosa) that comprises a large distributed sensor array and artificial mucous layer. In order to exploit fully this new architecture, new approaches are required to analyzing the rich data sets that it generates. In this paper, we propose a novel convolution based approach to processing signals from the e-mucosa. Computer simulations are performed to investigate the robustness of this approach when subjected to different real-world problems, such as sensor drift and noise. Our results demonstrate a promising ability to classify odors from poor sensor signals
A neuromorphic model of olfactory processing and sparse coding in the Drosophila larva brain
Animal nervous systems are highly efficient in processing sensory input. The neuromorphic computing paradigm aims at the hardware implementation of neural network computations to support novel solutions for building brain-inspired computing systems. Here, we take inspiration from sensory processing in the nervous system of the fruit fly larva. With its strongly limited computational resources of <200 neurons and <1.000 synapses the larval olfactory pathway employs fundamental computations to transform broadly tuned receptor input at the periphery into an energy efficient sparse code in the central brain. We show how this approach allows us to achieve sparse coding and increased separability of stimulus patterns in a spiking neural network, validated with both software simulation and hardware emulation on mixed-signal real-time neuromorphic hardware. We verify that feedback inhibition is the central motif to support sparseness in the spatial domain, across the neuron population, while the combination of spike frequency adaptation and feedback inhibition determines sparseness in the temporal domain. Our experiments demonstrate that such small, biologically realistic neural networks, efficiently implemented on neuromorphic hardware, can achieve parallel processing and efficient encoding of sensory input at full temporal resolution
Odors Pulsed at Wing Beat Frequencies are Tracked by Primary Olfactory Networks and Enhance Odor Detection
Each down stroke of an insect's wings accelerates axial airflow over the antennae. Modeling studies suggest that this can greatly enhance penetration of air and air-born odorants through the antennal sensilla thereby periodically increasing odorant-receptor interactions. Do these periodic changes result in entrainment of neural responses in the antenna and antennal lobe (AL)? Does this entrainment affect olfactory acuity? To address these questions, we monitored antennal and AL responses in the moth Manduca sexta while odorants were pulsed at frequencies from 10–72 Hz, encompassing the natural wingbeat frequency. Power spectral density (PSD) analysis was used to identify entrainment of neural activity. Statistical analysis of PSDs indicates that the antennal nerve tracked pulsed odor up to 30 Hz. Furthermore, at least 50% of AL local field potentials (LFPs) and between 7–25% of unitary spiking responses also tracked pulsed odor up to 30 Hz in a frequency-locked manner. Application of bicuculline (200 μM) abolished pulse tracking in both LFP and unitary responses suggesting that GABAA receptor activation is necessary for pulse tracking within the AL. Finally, psychophysical measures of odor detection establish that detection thresholds are lowered when odor is pulsed at 20 Hz. These results suggest that AL networks can respond to the oscillatory dynamics of stimuli such as those imposed by the wing beat in a manner analogous to mammalian sniffing
Computational physics of the mind
In the XIX century and earlier such physicists as Newton, Mayer, Hooke, Helmholtz and Mach were actively engaged in the research on psychophysics, trying to relate psychological sensations to intensities of physical stimuli. Computational physics allows to simulate complex neural processes giving a chance to answer not only the original psychophysical questions but also to create models of mind. In this paper several approaches relevant to modeling of mind are outlined. Since direct modeling of the brain functions is rather limited due to the complexity of such models a number of approximations is introduced. The path from the brain, or computational neurosciences, to the mind, or cognitive sciences, is sketched, with emphasis on higher cognitive functions such as memory and consciousness. No fundamental problems in understanding of the mind seem to arise. From computational point of view realistic models require massively parallel architectures
Generalised additive multiscale wavelet models constructed using particle swarm optimisation and mutual information for spatio-temporal evolutionary system representation
A new class of generalised additive multiscale wavelet models (GAMWMs) is introduced for high dimensional spatio-temporal evolutionary (STE) system identification. A novel two-stage hybrid learning scheme is developed for constructing such an additive wavelet model. In the first stage, a new orthogonal projection pursuit (OPP) method, implemented using a particle swarm optimisation(PSO) algorithm, is proposed for successively augmenting an initial coarse wavelet model, where relevant parameters of the associated wavelets are optimised using a particle swarm optimiser. The resultant network model, obtained in the first stage, may however be a redundant model. In the second stage, a forward orthogonal regression (FOR) algorithm, implemented using a mutual information method, is then applied to refine and improve the initially constructed wavelet model. The proposed two-stage hybrid method can generally produce a parsimonious wavelet model, where a ranked list of wavelet functions, according to the capability of each wavelet to represent the total variance in the desired system output signal is produced. The proposed new modelling framework is applied to real observed images, relative to a chemical reaction exhibiting a spatio-temporal evolutionary behaviour, and the associated identification results show that the new modelling framework is applicable and effective for handling high dimensional identification problems of spatio-temporal evolution sytems
Response and transcriptional regulation of rice SUMOylation system during development and stress conditions
Modification of proteins by the reversible covalent addition of the small ubiquitin like modifier (SUMO) protein has important consequences affecting target protein stability, sub-cellular localization, and protein-protein interactions. SUMOylation involves a cascade of enzymatic reactions, which resembles the process of ubiquitination. In this study, we characterized the SUMOylation system from an important crop plant, rice, and show that it responds to cold, salt and ABA stress conditions on a protein level via the accumulation of SUMOylated proteins. We also characterized the transcriptional regulation of individual SUMOylation cascade components during stress and development. During stress conditions, majority of the SUMO cascade components are transcriptionally down regulated. SUMO conjugate proteins and SUMO cascade component transcripts accumulated differentially in various tissues during plant development with highest levels in reproductive tissues. Taken together, these data suggest a role for SUMOylation in rice development and stress responses
Braitenberg Vehicles as Developmental Neurosimulation
The connection between brain and behavior is a longstanding issue in the
areas of behavioral science, artificial intelligence, and neurobiology.
Particularly in artificial intelligence research, behavior is generated by a
black box approximating the brain. As is standard among models of artificial
and biological neural networks, an analogue of the fully mature brain is
presented as a blank slate. This model generates outputs and behaviors from a
priori associations, yet this does not consider the realities of biological
development and developmental learning. Our purpose is to model the development
of an artificial organism that exhibits complex behaviors. We will introduce
our approach, which is to use Braitenberg Vehicles (BVs) to model the
development of an artificial nervous system. The resulting developmental BVs
will generate behaviors that range from stimulus responses to group behavior
that resembles collective motion. Next, we will situate this work in the domain
of artificial brain networks. Then we will focus on broader themes such as
embodied cognition, feedback, and emergence. Our perspective will then be
exemplified by three software instantiations that demonstrate how a BV-genetic
algorithm hybrid model, multisensory Hebbian learning model, and multi-agent
approaches can be used to approach BV development. We introduce use cases such
as optimized spatial cognition (vehicle-genetic algorithm hybrid model), hinges
connecting behavioral and neural models (multisensory Hebbian learning model),
and cumulative classification (multi-agent approaches). In conclusion, we will
revisit concepts related to our approach and how they might guide future
development.Comment: 32 pages, 8 figures, 2 table
- …