http://precedings.nature.com/tags/neurobiology Recently posted documents tagged with 'neurobiology' Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://precedings.nature.com/documents/3720/version/1 The most conspicuous signal in the human EEG is the so-called alpha wave, oscillations in the frequency range of 8 to 12 Hz. Visual stimulation of the retina suppresses the amplitude of alpha waves (Berger effect), and increased attention can reduce them. Here I show that one more parameter significantly affects the amplitudes of alpha waves: the intention to act by a motor response. Together with data from the literature, these results show that alpha waves are not part of the visual processing network but rather part of a long-range neuromodulatory network. The modulation modifies latencies in perception or motor response. The relevant mechanisms are located in early cortical visual areas; their activity may contribute to hemodynamic changes in these areas and thus explain dissociations between Bold signals and spike activities mentioned in the literature. http://precedings.nature.com/documents/3720/version/1 Fri, 04 Sep 2009 11:17:14 UTC The modulation of alpha-wave amplitude in human EEG by the intention to act with a motor response hdl:10101/npre.2009.3720.1 2009-09-04 Kuno Kirschfeld Nature Precedings 2009-09-04T11:17:14Z Manuscript Neuroscience http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3338.1 The responses of rat medial entorhinal cortical neurons form characteristic grid patterns as a function of the animal’s position. A recent model of grid fields proposes a mechanism based on intrinsic single cell properties. It relies on interference patterns emerging from multiple distinct and independent oscillations maintained in the dendritic tree of the cell. Here we examine the requirements necessary to implement this idealized mechanism in a biophysically realistic model. We find that appropriate grid field-formation by a single cell is exquisitely sensitive to intra-dendritic interactions. Mathematical analysis shows how these effects depend on properties of the dendritic oscillators and the (active) membrane segments that connect them. We provide requirements on the ion channel distributions that would be necessary for grid-fields. We implement these requirements in a compartmental model of a spiny stellate cell. We find that with realistic cell properties the intra-dendritic coupling is insufficiently weak to maintain grid field activity. Rather, the cell acts as a single oscillator as opposed to maintaining several independent oscillators. This work gives explicit requirements for a single cell implementation of grid-field activity and hints at a possible circuit level origin for grid pattern formation. http://dx.doi.org/10.1038/npre.2009.3338.1 Fri, 12 Jun 2009 14:26:51 UTC Requirements for a single cell mechanism of entorhinal “grid field” activity: role of dendritic oscillators and coupling doi:10.1038/npre.2009.3338.1 2009-06-12 Boris S. Gutkin Nature Precedings 2009-06-12T14:26:51Z Poster Neuroscience http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/3264/version/1 The statistics of the environment seem to exert optimal influence on the organization of functions subserving decision making. In order to make decisions about ambiguous sensory information, predictive coding models suggest that brain generate a template against which to match observed sensory evidence. Here we challenge this notion providing evidence that stochastic choices about the magnitude judgment of visual duration are triggered by bottom-up sensorimotor information. http://precedings.nature.com/documents/3264/version/1 Wed, 20 May 2009 13:29:44 UTC Sensorimotor states affect choice in the magnitude judgment of ambiguous durations hdl:10101/npre.2009.3264.1 2009-05-20 Carmelo M. Vicario Nature Precedings 2009-05-20T13:29:44Z Manuscript Neuroscience http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/2784/version/1 The objective of the present study was to elucidate evidence of and to revisit chaotic itinerancy in human brains by means of noninvasive scalp electroencephalogram (EEG) in normal subjects; with the assumed tenet that chaotic itinerancy occurs in sequences of cortical states marked by state transitions that appear as temporal discontinuities in neural activity patterns. The present study was based on unprecedented advances in spatial and temporal resolution of the phase of oscillations in scalp EEG. The EEG data was processed and modeled by the technique of curve fitting and temporal resolution was advanced by the use of Hilbert Transform (in Matlab version 7.0), which re-affirmed the variations in phase and amplitude in all scalp EEG electrical signals from 0 through 99 Hz frequencies. The numerical derivative of the analytic phase revealed plateaus in phase. The plateaus were bracketed by sudden jumps in phase. The widespread synchrony of the jumps in analytic phase manifests a metastable cortical state in accord with the theory of self-organized criticality. The jumps appear to be subcritical bifurcations. They reflect the aperiodic evolution of brain states through sequences of attractors that on access support the experience of remembering. State changes resembling phase transitions occur continually everywhere in cortex. Only the largest and longest-lasting state appears in scalp EEG, giving the appearance of chaotic itinerancy. The 1/fα spatial and temporal spectra of scalp EEG denote that brain maintains a state of self-organized criticality (SOC) as the basis of for its capacity for rapid adjustment to environmental changes. http://precedings.nature.com/documents/2784/version/1 Wed, 14 Jan 2009 11:56:38 UTC Chaotic Neural Dynamics as Evinced from Scalp Electroencephalography (EEG) hdl:10101/npre.2009.2784.1 2009-01-14 Amitabh Dube Nature Precedings 2009-01-14T11:56:38Z Manuscript Neuroscience http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2008.2171.1 Our past experience is one of the primary sources of information when faced with a choice. We ask ourselves: “what will happen if I do this?” Accurately predicting the consequences of our actions is usually modeled by operant (instrumental) learning experiments. These types of experiments are often contrasted with classical (Pavlovian) conditioning experiments in a dichotomy. And indeed, different brain circuits mediate the acquisition of skills and habits (via operant/instrumental learning) and the acquisition of facts (via classical/Pavlovian learning). However, realistic learning situations always comprise interactions of skill- and fact-learning components (composite learning). Fixed flying Drosophila melanogaster at the torque meter provide one of the very few systems where the relationship of operant and classical predictors in composite learning can be studied with sufficient rigor. The latest experiments show that the textbook operant/classical dichotomy is misleading and that instead composite learning consists of multiple interacting memory systems. These interactions between predictive stimuli (classical component) and goal-directed actions (operant component) make composite conditioning more effective than the operant and classical components alone (learning-by-doing, generation effect). Rutabaga (rut) mutants are impaired in learning about the (classical) stimuli, but show improved (operant) behavior learning. This is the first evidence that operant and classical conditioning differ not only at the circuit, but also at the molecular level. The interaction between operant and classical components is reciprocal and hierarchical, such that the classical suppresses the operant component. Experiments with transgenic flies demonstrate that this suppression of operant learning is mediated by the mushroom-bodies and serves to ensure that the classical memories can be generalized for access by other behaviors. Extended training can overcome this suppression and transforms goal-directed actions into habitual responses. This interaction leads to efficient learning, enables generalization and prevents premature habit-formation. http://dx.doi.org/10.1038/npre.2008.2171.1 Wed, 17 Sep 2008 12:49:48 UTC Mushroom-bodies regulate habit formation in Drosophila doi:10.1038/npre.2008.2171.1 2008-09-17 Björn Brembs Nature Precedings 2008-09-17T12:49:48Z Presentation Genetics & Genomics Neuroscience http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/1891/version/1 In the present study, we demonstrated that D-aspartate acts as an in vitro and in vivo neuromodulatory molecule upon hippocampal NMDAR transmission. Accordingly, we showed that this D-amino acid, widely expressed during embryonic phase, was able to strongly influence hippocampus-related functions at adulthood. Thus, while up-regulated levels of D-aspartate increased LTP and spatial memory in four-month old adult mice, the prolonged deregulation of this molecule in thirteen-month old animals induced a substantial acceleration of age-dependent decay of synaptic plasticity and cognitive functions. Moreover, we highlighted a role for D-aspartate in enhancing NMDAR-dependent synaptic plasticity through an inducible "turn-on/turn-off-like mechanism". Strikingly, we also showed that D-aspartate, when administered to aged mice, strongly rescued their physiological synaptic decay and attenuated their cognitive deterioration. In conclusion, our data suggest a tantalizing hypothesis for which this in-embryo-occurring D-amino acid, might disclose plasticity windows in the aging brain. http://precedings.nature.com/documents/1891/version/1 Thu, 15 May 2008 13:41:48 UTC D-aspartate exerts an opposing role upon age-dependent NMDAR-related synaptic plasticity and memory decay hdl:10101/npre.2008.1891.1 2008-05-15 Alessandro Usiello Nature Precedings 2008-05-15T13:41:48Z Manuscript Genetics & Genomics Neuroscience http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/1703/version/1 The neuron, when considered as a signal processing device, itsinputs are the frequency of pulses received at the synapses, and its output is the frequency of action potentials generated- in essence, a neuron is a pulse frequency signal processing device. In comparison, electrical devices use either digital or analog signals for communication or processing, and the mathematics behind these subjects is well understood. However, in regards to pulse frequency processing devices, there has not yet been a clear and persuasive mathematical model to describe the functions of neurons. It goes without saying that such a model is very important, not only for understanding neuron and neural system behavior, but also for undeveloped potential applications in industry. This paper proposes a method for obtaining the mathematical relationship between the input and output signals of a neuron based on physiological facts. The proposed method focuses on the currents across the postsynaptic membrane of each synapse, and the key is to recognize that the net charge across the whole membrane of a neuron over each action potential cycle must equal to zero. By analyzing the relationship between the input of a synapse and the currents across the postsynaptic membranes, a dynamic pulse frequency model of the neuron can be obtained. Here, we show that the transfer function of a neuron depends on the function of thepostsynaptic current of each synapse in resting state, which can be found by detecting the postsynaptic current when a pulse is received at the synapse. The transfer function of a typical neuron generally includes addition and subtraction of feedthrough terms and/or first order lag functions. To focus on the most basic characteristics of a neuron, accommodation, adaptation, learning, etc. are not discussed in this paper. http://precedings.nature.com/documents/1703/version/1 Wed, 26 Mar 2008 15:35:23 UTC A Mathematical Model of a Neuron with Synapses based on Physiology hdl:10101/npre.2008.1703.1 2008-05-07 Xiaolin Zhang Nature Precedings 2008-03-26T15:35:23Z Manuscript Neuroscience http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/1254/version/1 This paper highlights ionic fluxes as information carriers in neurons. The theoretical framework regarding information transfer is presented as changes in the thermodynamic entropy that underlie specific computations determined by ionic flow. The removal or accumulation of information is analyzed in terms of ionic mass transfer related with changes in Shannon information entropy. Specifically, information transfer occurs during an action potential (AP) via the voltage gated ion channels in membranes and the same physical mechanism can be extended to various types of synapses. Since sequential APs from a selected neuron are not alike, then every spike may transfer slightly different amounts of information during their occurrence. The average efficiency in information transfer during APs is estimated using mutual information measures and Hodgkin-Huxley model. This general scheme of ions as carriers of information represents the required physical machinery for a dynamic information transfer that is missing in the current spike-timing description. http://precedings.nature.com/documents/1254/version/1 Thu, 25 Oct 2007 19:33:10 UTC Beyond Spike Timing Theory – Thermodynamics of Neuronal Computation hdl:10101/npre.2007.1254.1 2007-10-25 Dorian Aur Nature Precedings 2007-10-25T19:33:10Z Manuscript Neuroscience http://creativecommons.org/licenses/by/2.5/ http://dx.doi.org/10.1038/npre.2007.61.1 The present study reveals the existence of an intrinsic spatial code within neuronal spikes that predicts behavior. As rats learnt a T-maze procedural task, simultaneous changes in temporal occurrence of spikes and spike directivity are evidenced in “expert” neurons. While the number of spikes between the tone delivery and the beginning of turn phase reduced with learning, the generated spikes between these two events acquired behavioral meaning that is of highest value for action selection. Spike directivity is thus a hidden feature that reveals the semantics of each spike and in the current experiment, predicts the correct turn that the animal would subsequently make to obtain reward. Semantic representation of behavior can then be revealed as modulations in spike directivity during the time. This predictability of observed behavior based on subtle changes in spike directivity represents an important step towards reading and understanding the underlying neural code. http://dx.doi.org/10.1038/npre.2007.61.1 Mon, 18 Jun 2007 12:20:14 UTC Reading the Neural Code: What do Spikes Mean for Behavior? doi:10.1038/npre.2007.61.1 2007-06-18 Dorian Aur Nature Precedings 2007-06-18T12:20:14Z Manuscript Neuroscience http://creativecommons.org/licenses/by/2.5/