Spike-timing dependent plasticity and the cognitive map
... Since the discovery of place cells – single pyramidal neurons that encode spatial location – it has been hypothesized that the hippocampus may act as a cognitive map of known environments. This putative function has been extensively modeled using auto-associative networks, which utilize rate-coded s ...
... Since the discovery of place cells – single pyramidal neurons that encode spatial location – it has been hypothesized that the hippocampus may act as a cognitive map of known environments. This putative function has been extensively modeled using auto-associative networks, which utilize rate-coded s ...
Down - 서울대 Biointelligence lab
... Fig. 9.13 Another example of a twodimensional self-organizing feature map. In this example we trained the network on 1000 random training examples from the lower left quadrants. The new training examples were then chosen randomly from the lower-left and upper-right quadrant. The parameter t specifie ...
... Fig. 9.13 Another example of a twodimensional self-organizing feature map. In this example we trained the network on 1000 random training examples from the lower left quadrants. The new training examples were then chosen randomly from the lower-left and upper-right quadrant. The parameter t specifie ...
Attractor concretion as a mechanism for the formation of context
... the AN to the wait state. The AN encodes the CS–US associations by making CS triggered transitions to the state that represents the value of the predicted US. The CS–US associations are learned by biasing the competition between the positive and the negative state. In particular, the competition bia ...
... the AN to the wait state. The AN encodes the CS–US associations by making CS triggered transitions to the state that represents the value of the predicted US. The CS–US associations are learned by biasing the competition between the positive and the negative state. In particular, the competition bia ...
final scientific program
... Welcome to AREADNE 2014, the fifth AREADNE Conference on Research in Encoding and Decoding of Neural Ensembles. One of the fundamental problems in neuroscience today is to understand how the activation of large populations of neurons gives rise to the higher order functions of the brain including le ...
... Welcome to AREADNE 2014, the fifth AREADNE Conference on Research in Encoding and Decoding of Neural Ensembles. One of the fundamental problems in neuroscience today is to understand how the activation of large populations of neurons gives rise to the higher order functions of the brain including le ...
Methods for reducing interference in the Complementary Learning
... between pterodactyl appearances were sufficiently long, one runs the risk that—in between appearances—interference from other memories would erode the original memory, in which ...
... between pterodactyl appearances were sufficiently long, one runs the risk that—in between appearances—interference from other memories would erode the original memory, in which ...
State-Dependent Computation Using Coupled Recurrent Networks
... mechanisms that support this processing. In a step toward solving this problem, we demonstrate by theoretical analysis and simulation how networks of richly interconnected neurons, such as those observed in the superficial layers of the neocortex, can embed reliable, robust finite state machines. We ...
... mechanisms that support this processing. In a step toward solving this problem, we demonstrate by theoretical analysis and simulation how networks of richly interconnected neurons, such as those observed in the superficial layers of the neocortex, can embed reliable, robust finite state machines. We ...
Doubly stochastic processes: an approach for understanding central
... Abstract— In this paper we argue that doubly stochastic processes are a natural tool for understanding certain types of information processing in the central nervous system. Doubly stochastic processes themselves are not new and have been investigated in a mathematical context; however, they have no ...
... Abstract— In this paper we argue that doubly stochastic processes are a natural tool for understanding certain types of information processing in the central nervous system. Doubly stochastic processes themselves are not new and have been investigated in a mathematical context; however, they have no ...
The neural subjective frame: from bodily signals to perceptual
... triggered by stimulus processing, be it availability in a global frontoparietal workspace [3,22], integration/segregation [23] or recurrent processing [24]. The field remains either mute or elusive on the issue of the subjective dimension of experience, although new behavioural measures based on fir ...
... triggered by stimulus processing, be it availability in a global frontoparietal workspace [3,22], integration/segregation [23] or recurrent processing [24]. The field remains either mute or elusive on the issue of the subjective dimension of experience, although new behavioural measures based on fir ...
ppt
... THEN immediately cease transmitting the frame. • The first station to detect a collision sends a jam signal to all stations to indicate that there has been a collision. • After receiving a jam signal, a station that was attempting to transmit waits a random amount of time before attempting to retran ...
... THEN immediately cease transmitting the frame. • The first station to detect a collision sends a jam signal to all stations to indicate that there has been a collision. • After receiving a jam signal, a station that was attempting to transmit waits a random amount of time before attempting to retran ...
An Optogenetic Approach to Understanding the Neural Circuits of Fear
... circuits and the identification of sites of neural plasticity in these circuits. Over the past 30 years, studies using lesion, electrophysiological, pharmacological, and biochemical/molecular techniques have revealed a great deal about the neural mechanisms of fear learning (1–7,11–13). Despite this ...
... circuits and the identification of sites of neural plasticity in these circuits. Over the past 30 years, studies using lesion, electrophysiological, pharmacological, and biochemical/molecular techniques have revealed a great deal about the neural mechanisms of fear learning (1–7,11–13). Despite this ...
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.