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Mechanisms of development: cell movement
... The story of the enteric nervous system (the longest crest migration, and the most complex part of the PNS) ...
... The story of the enteric nervous system (the longest crest migration, and the most complex part of the PNS) ...
Drivers and modulators from push-pull and balanced synaptic input
... constant, and !(x) is a step function that takes the value 1 if x>0 and zero otherwise. Equation 1 gives the firing rate in terms of an input current, or equivalently the effective steady-state potential it produces. This formula is valid in the absence of ‘‘noise’’, which means non-variable synapti ...
... constant, and !(x) is a step function that takes the value 1 if x>0 and zero otherwise. Equation 1 gives the firing rate in terms of an input current, or equivalently the effective steady-state potential it produces. This formula is valid in the absence of ‘‘noise’’, which means non-variable synapti ...
Algorithms in nature: the convergence of systems biology and
... MIS members are connected to each other. An MIS in an ad-hoc wireless network serves as a routing backbone by which nodes can communicate. It also corresponds to a distributed partitioning of the nodes into clusters that can be used to optimize network bandwidth and resource distribution in the netw ...
... MIS members are connected to each other. An MIS in an ad-hoc wireless network serves as a routing backbone by which nodes can communicate. It also corresponds to a distributed partitioning of the nodes into clusters that can be used to optimize network bandwidth and resource distribution in the netw ...
Foundations for a Circuit Complexity Theory of Sensory
... “threshold gates” or “winner-take-all gates” of inputs, that take one unit of time for their computation like all the other gates, but which occupy each a set of intersection points of the grid that are all connected by an undirected wire (whose length contributes to the total wire length) in some a ...
... “threshold gates” or “winner-take-all gates” of inputs, that take one unit of time for their computation like all the other gates, but which occupy each a set of intersection points of the grid that are all connected by an undirected wire (whose length contributes to the total wire length) in some a ...
Neural Networks
... section "Getting started with Snipe" – you will find an easy step-by-step guide conSNIPE 1 is a well-documented JAVA li- cerning Snipe and its documentation, as brary that implements a framework for well as some examples. neural networks in a speedy, feature-rich and usable way. It is available at n ...
... section "Getting started with Snipe" – you will find an easy step-by-step guide conSNIPE 1 is a well-documented JAVA li- cerning Snipe and its documentation, as brary that implements a framework for well as some examples. neural networks in a speedy, feature-rich and usable way. It is available at n ...
Article
... events are fundamental to sensory processing. However, the mechanisms by which the brain measures time over ranges of milliseconds to seconds remain unclear. The dominant model of temporal processing proposes that an oscillator emits events that are integrated to provide a linear metric of time. We ...
... events are fundamental to sensory processing. However, the mechanisms by which the brain measures time over ranges of milliseconds to seconds remain unclear. The dominant model of temporal processing proposes that an oscillator emits events that are integrated to provide a linear metric of time. We ...
Neural Networks
... section "Getting started with Snipe" – you will find an easy step-by-step guide conSNIPE 1 is a well-documented JAVA li- cerning Snipe and its documentation, as brary that implements a framework for well as some examples. neural networks in a speedy, feature-rich and usable way. It is available at n ...
... section "Getting started with Snipe" – you will find an easy step-by-step guide conSNIPE 1 is a well-documented JAVA li- cerning Snipe and its documentation, as brary that implements a framework for well as some examples. neural networks in a speedy, feature-rich and usable way. It is available at n ...
Walk-based measure of balance in signed networks
... contributions to balance [9]. A method for computing the degree of unbalance of a signed network was proposed by Facchetti et al. [10] by using ground-state calculations in largescale Ising spin glasses. Using their approach for undirected versions of three online social networks, they concluded tha ...
... contributions to balance [9]. A method for computing the degree of unbalance of a signed network was proposed by Facchetti et al. [10] by using ground-state calculations in largescale Ising spin glasses. Using their approach for undirected versions of three online social networks, they concluded tha ...
Unit 6 Day 5 Anatomy
... potentials make the neuron MORE likely to fire. (raise) • Inhibitory Postsynaptic potentials make the neuron LESS likey to fire.(more -) ...
... potentials make the neuron MORE likely to fire. (raise) • Inhibitory Postsynaptic potentials make the neuron LESS likey to fire.(more -) ...
Synaptic reverberation underlying mnemonic persistent activity
... neural circuits that encode directional or spatial information, such as headdirection cellsn and place cellso. Parametric working memory Figure Ic shows a delayed somatosensory discrimination task, in which the monkey was trained to compare and discriminate the frequencies of two vibrotactile stimul ...
... neural circuits that encode directional or spatial information, such as headdirection cellsn and place cellso. Parametric working memory Figure Ic shows a delayed somatosensory discrimination task, in which the monkey was trained to compare and discriminate the frequencies of two vibrotactile stimul ...
Dynamics of Learning and Recall ... Recurrent Synapses and Cholinergic Modulation
... inhibitory interneurons. For inhibitory interneurons, the membrane potential is represented by h. W’ represents the matrix of excitatory synapses arising from cortical pyramidal cells and synapsing on inhibitory interneurons and H’ represents the matrix of inhibitory synapses between inhibitory neur ...
... inhibitory interneurons. For inhibitory interneurons, the membrane potential is represented by h. W’ represents the matrix of excitatory synapses arising from cortical pyramidal cells and synapsing on inhibitory interneurons and H’ represents the matrix of inhibitory synapses between inhibitory neur ...
Applying Transcranial Alternating Current Stimulation to the Study of Spike Timing Dependent Plasticity in Neural Networks
... It has been seen clinically that tACS applied at the resting frequency of a neural system causes an increase in synaptic weights and synchrony between the neurons: an effect that remains for approximately an hour after tACS ceases [1]. If the effects of tACS could be made to be semi-permanent it has ...
... It has been seen clinically that tACS applied at the resting frequency of a neural system causes an increase in synaptic weights and synchrony between the neurons: an effect that remains for approximately an hour after tACS ceases [1]. If the effects of tACS could be made to be semi-permanent it has ...
Complex Cell-like Direction Selectivity through Spike
... 0.003pS) from its preceding and successor neurons (Fig 20, "Before Learning"). Excitatory and inhibitory synaptic currents were calculated using kinetic models of synaptic transmission based on properties of AMPA and GABA, (yaminobutyric acid A) receptors as determined from wholecell recordings (see ...
... 0.003pS) from its preceding and successor neurons (Fig 20, "Before Learning"). Excitatory and inhibitory synaptic currents were calculated using kinetic models of synaptic transmission based on properties of AMPA and GABA, (yaminobutyric acid A) receptors as determined from wholecell recordings (see ...
Stockholm University
... The simplest model for describing multi-neuron spike statistics is the pairwise Ising model [1,2]. To start, one divides the spike trains into small time bins, and to each neuron i and each time bin t assigns a binary variables si(t) = -1 if neuron i has not emitted any spikes in that time bin and 1 ...
... The simplest model for describing multi-neuron spike statistics is the pairwise Ising model [1,2]. To start, one divides the spike trains into small time bins, and to each neuron i and each time bin t assigns a binary variables si(t) = -1 if neuron i has not emitted any spikes in that time bin and 1 ...
Modeling Neural Mechanisms of Cognitive-Affective Interaction Abninder Litt () Chris Eliasmith ()
... influences both cognitive planning and emotional state. The model provides a neurological explanation of loss aversion in humans, and suggests particular mechanisms by which serotonin influences affective appraisal and risky behavior. Specific empirical predictions of the model include a correlation ...
... influences both cognitive planning and emotional state. The model provides a neurological explanation of loss aversion in humans, and suggests particular mechanisms by which serotonin influences affective appraisal and risky behavior. Specific empirical predictions of the model include a correlation ...
Here - Statistical Analysis of Neuronal Data
... Many studies have attempted to examine the rhythmic modulation of the firing of individual neurons from extracellular recordings. In the rodent hippocampus, neurons are known to have a strong relationship to theta rhythm (6-12 Hz) oscillations in the local field potential and to be intrinsically rhy ...
... Many studies have attempted to examine the rhythmic modulation of the firing of individual neurons from extracellular recordings. In the rodent hippocampus, neurons are known to have a strong relationship to theta rhythm (6-12 Hz) oscillations in the local field potential and to be intrinsically rhy ...
Sensory Integration and Density Estimation
... the conditional distribution. Then Tz (Y) would be sufficient for X as well as for Z, and the proof complete. But this sense of “form of the conditional distribution” is stronger than Eq. 4. If, for example, the image of z under ψ(·) is lower-dimensional than the image of x under φ(·), then the cond ...
... the conditional distribution. Then Tz (Y) would be sufficient for X as well as for Z, and the proof complete. But this sense of “form of the conditional distribution” is stronger than Eq. 4. If, for example, the image of z under ψ(·) is lower-dimensional than the image of x under φ(·), then the cond ...
Proceedings - Neuroscience Meetings
... Besides intraneuronal factors affecting KCC2 function and, thus, the chloride gradient, there is a number of extra-neuronal factors suggested to have a great influence over the distribution of chloride across the neuronal membrane. The very recent experimental findings point to the extracellular mat ...
... Besides intraneuronal factors affecting KCC2 function and, thus, the chloride gradient, there is a number of extra-neuronal factors suggested to have a great influence over the distribution of chloride across the neuronal membrane. The very recent experimental findings point to the extracellular mat ...
Decision Making in Recurrent Neuronal Circuits
... learning flexible sensorimotor associations, and reward-based economic choice behaviors such as foraging or interactive games. These models are similar in their basic assumptions. Recurrent synaptic excitation is assumed to be sufficiently strong to generate multiple self-sustained stable states of ...
... learning flexible sensorimotor associations, and reward-based economic choice behaviors such as foraging or interactive games. These models are similar in their basic assumptions. Recurrent synaptic excitation is assumed to be sufficiently strong to generate multiple self-sustained stable states of ...
Integrate-and-Fire Neurons and Networks
... Most biological neurons communicate by short electrical pulses, called action potentials or spikes. In contrast to the standard neuron model used in artificial neural networks, integrate-and-fire neurons do not rely on a temporal average over the pulses. In integrate-and-fire and similar spiking neu ...
... Most biological neurons communicate by short electrical pulses, called action potentials or spikes. In contrast to the standard neuron model used in artificial neural networks, integrate-and-fire neurons do not rely on a temporal average over the pulses. In integrate-and-fire and similar spiking neu ...