Integrating Optogenetic and Pharmacological Approaches to Study
... 1997; Wanat et al., 2009), but the ability to mimic this firing pattern in vivo selectively and exclusively in dopaminergic neurons of the VTA required an optogenetic approach. Selective stimulation of VTA dopamine neurons in a burst-like fashion was sufficient to produce reward-seeking behavior, wh ...
... 1997; Wanat et al., 2009), but the ability to mimic this firing pattern in vivo selectively and exclusively in dopaminergic neurons of the VTA required an optogenetic approach. Selective stimulation of VTA dopamine neurons in a burst-like fashion was sufficient to produce reward-seeking behavior, wh ...
Single-trial decoding of intended eye movement goals from lateral
... where s is the target location, r is the response (e.g., spike counts), and the log is base 2. Spike counts were quantized using a maximum of 8 quantiles (Musallam et al. 2004), but often fewer quantiles were needed to represent all unique spike count values, especially when analyzing neurons with l ...
... where s is the target location, r is the response (e.g., spike counts), and the log is base 2. Spike counts were quantized using a maximum of 8 quantiles (Musallam et al. 2004), but often fewer quantiles were needed to represent all unique spike count values, especially when analyzing neurons with l ...
Computing with Spiking Neuron Networks
... implement a wide range of mathematical functions relating input states to output states: With algorithms for setting the weights between neurons, these artificial neural networks can “learn” such relations. A large number of learning rules have been proposed, both for teaching a network explicitly t ...
... implement a wide range of mathematical functions relating input states to output states: With algorithms for setting the weights between neurons, these artificial neural networks can “learn” such relations. A large number of learning rules have been proposed, both for teaching a network explicitly t ...
Contraction Properties of VLSI Cooperative Competitive Neural
... of coupled (feed–forward and feed–back) subsystems that are individually contracting, then it is possible to find a sufficient condition for contraction without computing the system’s full Jacobian. In addition it is possible to compute a lower bound for the full system’s contraction rate. Let Fs be ...
... of coupled (feed–forward and feed–back) subsystems that are individually contracting, then it is possible to find a sufficient condition for contraction without computing the system’s full Jacobian. In addition it is possible to compute a lower bound for the full system’s contraction rate. Let Fs be ...
Probing scale interaction in brain dynamics through synchronization
... down to single-neuron responses. Moreover, studies of the global activity of the brain usually focus for convenience on specific cognitive or motor tasks, in order to compare them with a control state such as spontaneous activity at rest. The various aforementioned approaches deal with different sca ...
... down to single-neuron responses. Moreover, studies of the global activity of the brain usually focus for convenience on specific cognitive or motor tasks, in order to compare them with a control state such as spontaneous activity at rest. The various aforementioned approaches deal with different sca ...
The Information Processing Mechanism of the Brain
... > The pattern that is recalled is not an exact replica of the original pattern. Some cross-over effects between patterns stored in the network occur, but if the stored patterns are approximately orthogonal (constituting a unique set of activities), the recalled pattern can be distinguished as the or ...
... > The pattern that is recalled is not an exact replica of the original pattern. Some cross-over effects between patterns stored in the network occur, but if the stored patterns are approximately orthogonal (constituting a unique set of activities), the recalled pattern can be distinguished as the or ...
Evolutionary Ensembles with Negative Correlation Learning 1
... what subtasks should be performed by which NNs. In the case of some speech and image processing tasks, such task decomposition was done manually. While manual design and a xed ensemble architecture may be appropriate when there are experienced human experts with sucient prior knowledge of the prob ...
... what subtasks should be performed by which NNs. In the case of some speech and image processing tasks, such task decomposition was done manually. While manual design and a xed ensemble architecture may be appropriate when there are experienced human experts with sucient prior knowledge of the prob ...
Analysis and Classification of EEG signals using Mixture of
... Electroencephalography (EEG) signal is the recording of spontaneous electrical activity of the brain over a small period of time [1]. The term EEG refers that the brain activity emits the signal from head and being drawn. It is produced by bombardment of neurons within the brain. It is measured for ...
... Electroencephalography (EEG) signal is the recording of spontaneous electrical activity of the brain over a small period of time [1]. The term EEG refers that the brain activity emits the signal from head and being drawn. It is produced by bombardment of neurons within the brain. It is measured for ...
Connectivity in Real and Simulated Associative Memories
... In our model we need the units to have a geometry, so that there is a distance defined between any pair of units. We take the simplest approach (as in the original smallworld model (Watts & Strogatz, 1998) and place the units in a 1-D ring. The distance between any two nodes on the ring is simply th ...
... In our model we need the units to have a geometry, so that there is a distance defined between any pair of units. We take the simplest approach (as in the original smallworld model (Watts & Strogatz, 1998) and place the units in a 1-D ring. The distance between any two nodes on the ring is simply th ...
Pointing the way toward target selection
... present elsewhere (Fig. 2b). This corresponds to choosing a general region of interest and then allowing the visual system to select a target within this region. Recurrent networks can perform a number of other computations of relevance to sensory processing. For example, if the recurrent connection ...
... present elsewhere (Fig. 2b). This corresponds to choosing a general region of interest and then allowing the visual system to select a target within this region. Recurrent networks can perform a number of other computations of relevance to sensory processing. For example, if the recurrent connection ...
1 1 1 1 - UPM ASLab
... The Φ calculation has to be done for all subsets and all cuts in all subsets to discover the least Φ which is the Φ for the whole network. Gamez has shown that to predict the Φ of a 30-neuron network it would take a state-of-the-art computer 1010 years (!) ...
... The Φ calculation has to be done for all subsets and all cuts in all subsets to discover the least Φ which is the Φ for the whole network. Gamez has shown that to predict the Φ of a 30-neuron network it would take a state-of-the-art computer 1010 years (!) ...
A theory: parts of the brain control other parts
... involves a lot of trial-and-error with various learning parameters and that kind of external intervention (or human baby-sitting) of learning algorithms is definitely not a property of the brain, because no one ever supplies to the brain the kind of algorithmic information (e.g. network designs, lea ...
... involves a lot of trial-and-error with various learning parameters and that kind of external intervention (or human baby-sitting) of learning algorithms is definitely not a property of the brain, because no one ever supplies to the brain the kind of algorithmic information (e.g. network designs, lea ...
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.