Spike-timing-dependent plasticity: common themes
... Eventually, a column defined wholly by the feed-forward connections forms (Fig. 3E), completing the process of the transfer of connectivity information from the recurrent connections in the network to the feed-forward connections. The transfer of information from the recurrent connections to the feed ...
... Eventually, a column defined wholly by the feed-forward connections forms (Fig. 3E), completing the process of the transfer of connectivity information from the recurrent connections in the network to the feed-forward connections. The transfer of information from the recurrent connections to the feed ...
Condition interference in rats performing a choice task with switched
... Variable-reward-condition trials with the same rewardprobability setting were referred to as a block; a block consisted of at least 20 trials. Subsequently, the block was changed when the rat selected the more rewarding hole in ≥80% of the last 20 variable-reward-condition trials (Ito and Doya, 2009 ...
... Variable-reward-condition trials with the same rewardprobability setting were referred to as a block; a block consisted of at least 20 trials. Subsequently, the block was changed when the rat selected the more rewarding hole in ≥80% of the last 20 variable-reward-condition trials (Ito and Doya, 2009 ...
Total Wiring Length Minimization of C. elegans Neural
... not significantly vary from animal to animal. In particular, number of neurons (302) in the hermaphrodite worm is consistent across the individuals [1, 2], and synapses (both chemical synapses and electric gap junctions) are stereotypical with more than 75% reproducibility [3]. This consistency make ...
... not significantly vary from animal to animal. In particular, number of neurons (302) in the hermaphrodite worm is consistent across the individuals [1, 2], and synapses (both chemical synapses and electric gap junctions) are stereotypical with more than 75% reproducibility [3]. This consistency make ...
Rich-club organization in effective connectivity among cortical neurons
... of communication, it is virtually unknown how information is transferred in local cortical networks, consisting of hundreds of closely spaced neurons. To address this, it is important to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and ...
... of communication, it is virtually unknown how information is transferred in local cortical networks, consisting of hundreds of closely spaced neurons. To address this, it is important to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and ...
Altered neural reward and loss processing and
... neural responses during anticipation and receipt of gains and losses and related PE-signals. Additionally, we assessed the relationship between neural responsivity during gain/loss processing and hedonic capacity. When compared with healthy controls, depressed individuals showed reduced fronto-stria ...
... neural responses during anticipation and receipt of gains and losses and related PE-signals. Additionally, we assessed the relationship between neural responsivity during gain/loss processing and hedonic capacity. When compared with healthy controls, depressed individuals showed reduced fronto-stria ...
A thalamic reticular networking model of consciousness
... [Background]: It is reasonable to consider the thalamus a primary candidate for the location of consciousness, given that the thalamus has been referred to as the gateway of nearly all sensory inputs to the corresponding cortical areas. Interestingly, in an early stage of brain development, communic ...
... [Background]: It is reasonable to consider the thalamus a primary candidate for the location of consciousness, given that the thalamus has been referred to as the gateway of nearly all sensory inputs to the corresponding cortical areas. Interestingly, in an early stage of brain development, communic ...
View/Open - Minerva Access
... owl. We demonstrate a similar selective potentiation for the recurrent connections in a network with axonal delays corresponding to the period of incoming oscillatory activity with frequencies in the range of 100-300Hz. For lower frequency oscillations, such as gamma (60Hz), we show that multiple, r ...
... owl. We demonstrate a similar selective potentiation for the recurrent connections in a network with axonal delays corresponding to the period of incoming oscillatory activity with frequencies in the range of 100-300Hz. For lower frequency oscillations, such as gamma (60Hz), we show that multiple, r ...
Neural Encoding I: Firing Rates and Spike Statistics
... Neurons are remarkable among the cells of the body in their ability to propagate signals rapidly over large distances. They do this by generating characteristic electrical pulses called action potentials, or more simply spikes, that can travel down nerve fibers. Neurons represent and transmit inform ...
... Neurons are remarkable among the cells of the body in their ability to propagate signals rapidly over large distances. They do this by generating characteristic electrical pulses called action potentials, or more simply spikes, that can travel down nerve fibers. Neurons represent and transmit inform ...
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... system architecture have been implemented. These modules and models were developed as collaborations between the computational partners of Nancy, Ulm and Sunderland. This was done based on motivation from the biological partners of MRC, Cambridge and Parma. 5.1 Sentence input and output The MirrorBo ...
... system architecture have been implemented. These modules and models were developed as collaborations between the computational partners of Nancy, Ulm and Sunderland. This was done based on motivation from the biological partners of MRC, Cambridge and Parma. 5.1 Sentence input and output The MirrorBo ...
SOM
... • Initialization: choose random small values for weight vectors such that wj(0) is different for all neurons j. • Sampling: drawn a sample example x from the ...
... • Initialization: choose random small values for weight vectors such that wj(0) is different for all neurons j. • Sampling: drawn a sample example x from the ...
Solving the Distal Reward Problem through
... where sd is the time constant of DA uptake and DA(t) models the source of DA due to the activity of dopaminergic neurons in the midbrain structures VTA and substantia nigra pars compacta. A better description of DA kinetics, based on Michaelis--Menten formalism, was recently suggested by Montague et ...
... where sd is the time constant of DA uptake and DA(t) models the source of DA due to the activity of dopaminergic neurons in the midbrain structures VTA and substantia nigra pars compacta. A better description of DA kinetics, based on Michaelis--Menten formalism, was recently suggested by Montague et ...
Neuronal Regulation Implements Efficient Synaptic Pruning
... This paper studies one of the fundamental puzzles in brain development: the massive synaptic pruning observed in mammals during childhood , removing more than half of the synapses until puberty (see [1] for review) . This phenomenon is observed in various areas of the brain both in animal studies an ...
... This paper studies one of the fundamental puzzles in brain development: the massive synaptic pruning observed in mammals during childhood , removing more than half of the synapses until puberty (see [1] for review) . This phenomenon is observed in various areas of the brain both in animal studies an ...
Cellular, synaptic and network effects of neuromodulation
... action potentials separated by long interburst intervals. When we consider that biological neurons may display eight, ten, or more different voltage-dependent currents, and that the subunit composition of each channel type can regulate its kinetics and voltage-dependence (Hille, 2001), it is clear t ...
... action potentials separated by long interburst intervals. When we consider that biological neurons may display eight, ten, or more different voltage-dependent currents, and that the subunit composition of each channel type can regulate its kinetics and voltage-dependence (Hille, 2001), it is clear t ...
The control of rostrocaudal pattern in the developing spinal cord
... the demonstration that the expression of LIM homeodomain (HD) proteins defines each of the columnar subclasses of chick MNs, prior to target innervation (Tsuchida et al., 1994). Moreover, genetic analyses have shown that LIM-HD proteins control neuronal differentiation and axonal pathfinding in both ...
... the demonstration that the expression of LIM homeodomain (HD) proteins defines each of the columnar subclasses of chick MNs, prior to target innervation (Tsuchida et al., 1994). Moreover, genetic analyses have shown that LIM-HD proteins control neuronal differentiation and axonal pathfinding in both ...
Artificial neural network
In machine learning and cognitive science, artificial neural networks (ANNs) are a family of statistical learning models inspired by biological neural networks (the central nervous systems of animals, in particular the brain) and are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Artificial neural networks are generally presented as systems of interconnected ""neurons"" which exchange messages between each other. The connections have numeric weights that can be tuned based on experience, making neural nets adaptive to inputs and capable of learning.For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. This determines which character was read.Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition.