Applying Transcranial Alternating Current Stimulation to the Study of Spike Timing Dependent Plasticity in Neural Networks
... physiologically accurate than the similar, but more complex, Hodgkin Huxley model, the FN model’s simplicity to implement and accurate mimicking of general neural spiking behavior make it an ideal first choice for the purposes of generating a neural micro-network. The specific FN model used here was ...
... physiologically accurate than the similar, but more complex, Hodgkin Huxley model, the FN model’s simplicity to implement and accurate mimicking of general neural spiking behavior make it an ideal first choice for the purposes of generating a neural micro-network. The specific FN model used here was ...
Analyzing Neural Responses to Natural Signals: Maximally
... are relevant for setting the probability of generating a single spike at one moment in time. From an information-theoretic point of view, asking for stimulus features that capture the mutual information between the stimulus and the arrival times of single spikes is a well-posed question even if succ ...
... are relevant for setting the probability of generating a single spike at one moment in time. From an information-theoretic point of view, asking for stimulus features that capture the mutual information between the stimulus and the arrival times of single spikes is a well-posed question even if succ ...
Copy of the full paper
... A great deal is now known about how the small circuits that generate rhythmic behaviour in invertebrates are organized and about how they function42,43. This is because it is relatively easy to determine which neurons are ‘part of the circuit’ and to identify how they are connected as these circuits ...
... A great deal is now known about how the small circuits that generate rhythmic behaviour in invertebrates are organized and about how they function42,43. This is because it is relatively easy to determine which neurons are ‘part of the circuit’ and to identify how they are connected as these circuits ...
Neuronal oscillations and brain wave dynamics in a LIF model
... might arise from sensory input. In other words, how might structured signals be derived from chaotic input? The topology of the model is illustrated in figure 1, and is loosely based on that in the visual cortex. Every neuron is either excitatory or inhibitory. At the bottom is a layer of input neur ...
... might arise from sensory input. In other words, how might structured signals be derived from chaotic input? The topology of the model is illustrated in figure 1, and is loosely based on that in the visual cortex. Every neuron is either excitatory or inhibitory. At the bottom is a layer of input neur ...
Logic and Complexity in Cognitive Science
... The Wason selection task [137, 138] is very simple. Subjects are shown four cards and told that all cards have a number on one side and a letter on the other. The faces visible to the subject read D, K , 3, and 7. The subject is then told “Every card which has a D on one side has a 3 on the other” a ...
... The Wason selection task [137, 138] is very simple. Subjects are shown four cards and told that all cards have a number on one side and a letter on the other. The faces visible to the subject read D, K , 3, and 7. The subject is then told “Every card which has a D on one side has a 3 on the other” a ...
Application of Data Mining Using Artificial Neural Network: Survey
... value. The answer is Data mining. Effective Data mining required four things: right data, right tool, high quality, and sufficient sample size. Neural network also play important role to solve problem of data mining. Its characteristic are robustness, parallel processing, fault tolerance, distribute ...
... value. The answer is Data mining. Effective Data mining required four things: right data, right tool, high quality, and sufficient sample size. Neural network also play important role to solve problem of data mining. Its characteristic are robustness, parallel processing, fault tolerance, distribute ...
Art.-Schoenbaum (R) - UCSD Cognitive Science
... Comparison of neural activity on positive go and negative go trials revealed that a substantial population of neurons in both OFC and ABL fired differentially depending on whether the subsequent outcome was to be the rewarding sucrose solution or the aversive quinine. This comparison of activity was ...
... Comparison of neural activity on positive go and negative go trials revealed that a substantial population of neurons in both OFC and ABL fired differentially depending on whether the subsequent outcome was to be the rewarding sucrose solution or the aversive quinine. This comparison of activity was ...
A visual processing task: Retina and V1
... The Hubel and Wiesel experiments saw a clear response of the simple cells to a bar. But is this all that the cell does? How well does a bigger bar work, how about a ’T’-shape instead of a bar? This raises the general question: how to measure the receptive field of a cell without biasing the result, ...
... The Hubel and Wiesel experiments saw a clear response of the simple cells to a bar. But is this all that the cell does? How well does a bigger bar work, how about a ’T’-shape instead of a bar? This raises the general question: how to measure the receptive field of a cell without biasing the result, ...
Ten years research and the art of Chemometrics
... Chemometrics What is difficult in doing chemometrics is how to determine if a particular technique is appropriate for a given problem and how to properly interpret the results provided by that technique. This is what is called the “ art “ of chemometrics ...
... Chemometrics What is difficult in doing chemometrics is how to determine if a particular technique is appropriate for a given problem and how to properly interpret the results provided by that technique. This is what is called the “ art “ of chemometrics ...
Proceedings of 2013 BMI the Second International Conference on
... There is a growing body of research about the outcomes of using virtual avatars (and other mediated self-representations). For example, the Proteus Effect suggests that people behave in ways that conform to their avatars' characteristics, even after avatar use, e.g., using taller avatars leads to mo ...
... There is a growing body of research about the outcomes of using virtual avatars (and other mediated self-representations). For example, the Proteus Effect suggests that people behave in ways that conform to their avatars' characteristics, even after avatar use, e.g., using taller avatars leads to mo ...
Inferring spike-timing-dependent plasticity from spike train data
... well as the activity of a pre-synaptic neuron. A synaptic weight determining the strength of coupling between the two neurons changes over time depending on the relative spike-timing (Fig 1A). In the simulations that follow we consider three types of modification functions: 1) a traditional double-e ...
... well as the activity of a pre-synaptic neuron. A synaptic weight determining the strength of coupling between the two neurons changes over time depending on the relative spike-timing (Fig 1A). In the simulations that follow we consider three types of modification functions: 1) a traditional double-e ...
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.