SOM
... • Neural networks for unsupervised learning attempt to discover special patterns from available data without using external help (i.e. RISK FUNCTION). – There is no information about the desired class (or output ) d of an example x. So only x is given. – Self Organising Maps (SOM) are neural network ...
... • Neural networks for unsupervised learning attempt to discover special patterns from available data without using external help (i.e. RISK FUNCTION). – There is no information about the desired class (or output ) d of an example x. So only x is given. – Self Organising Maps (SOM) are neural network ...
computational and in vitro studies of persistent activity
... Neuroscience. (D) NMDA receptors also contribute to rate control in the active state. This is accomplished by means of the saturation properties of NMDA receptors as illustrated here. Upper panels show the input– output relationships (convex lines) for a neuron with asynchronous synaptic inputs of v ...
... Neuroscience. (D) NMDA receptors also contribute to rate control in the active state. This is accomplished by means of the saturation properties of NMDA receptors as illustrated here. Upper panels show the input– output relationships (convex lines) for a neuron with asynchronous synaptic inputs of v ...
Memory Maintenance in Synapses with Calcium
... orders of magnitude; (ii) adding a bistability mechanism so that each synapse has two stable states at sufficiently low background activity leads to a further boost in memory time scale, since memory decay is no longer described by an exponential decay from an initial state, but by an escape from a ...
... orders of magnitude; (ii) adding a bistability mechanism so that each synapse has two stable states at sufficiently low background activity leads to a further boost in memory time scale, since memory decay is no longer described by an exponential decay from an initial state, but by an escape from a ...
Memory, aging and external memory aids
... correlate to each other suggests that a given task generally involve all three (Eysenck & Keane, 2010). Eysenck & Keane (2010) note that a function not involved in these three is dual tasking. It ...
... correlate to each other suggests that a given task generally involve all three (Eysenck & Keane, 2010). Eysenck & Keane (2010) note that a function not involved in these three is dual tasking. It ...
Hippocampal mechanisms for the context-dependent retrieval of episodes 2005 Special issue
... given location, the current place input cues the forward retrieval of all previously experienced sequences from that location (forward association). At the same time, temporal context activates units according to the time since a sequence was experienced. The convergence of temporal context with for ...
... given location, the current place input cues the forward retrieval of all previously experienced sequences from that location (forward association). At the same time, temporal context activates units according to the time since a sequence was experienced. The convergence of temporal context with for ...
Free recall and recognition in a network model of the... simulating effects of scopolamine on human memory function
... Fig. 3. (A) Anatomical connectivity of the hippocampal formation. Connections between the hippocampus and multimodal association cortices pass through the entorhinal cortex. (1) Fibers of the perforant path connect entorhinal cortex layers II and III with the dentate gyrus. (2) The dentate gyrus pro ...
... Fig. 3. (A) Anatomical connectivity of the hippocampal formation. Connections between the hippocampus and multimodal association cortices pass through the entorhinal cortex. (1) Fibers of the perforant path connect entorhinal cortex layers II and III with the dentate gyrus. (2) The dentate gyrus pro ...
Learning to classify complex patterns using a VLSI network of
... as problems of noisy, unmatched elementary devices. Although the reasons for the superiority of the nervous system in the real world are not completely understood, it is obvious that the main methods of neural computation in biology are very different from those of modern digital computers. In the b ...
... as problems of noisy, unmatched elementary devices. Although the reasons for the superiority of the nervous system in the real world are not completely understood, it is obvious that the main methods of neural computation in biology are very different from those of modern digital computers. In the b ...
Neurons, Brain Chemistry, and Neurotransmission
... from the balance outside the cell. This uneven distribution of ions creates an electrical potential across the cell membrane. This is called the resting membrane potential. In humans, the resting membrane potential ranges from –40 millivolts (mV) to –80 mV, with –65 mV as an average resting membrane ...
... from the balance outside the cell. This uneven distribution of ions creates an electrical potential across the cell membrane. This is called the resting membrane potential. In humans, the resting membrane potential ranges from –40 millivolts (mV) to –80 mV, with –65 mV as an average resting membrane ...
Introduction to Neural Networks "Energy" and attractor networks
... function as the neural activities evolve through time. This function is associated with energy because the mathematics in 3 some cases is identical to that describing the evolution of physical systems with declining free energy. The Ising model of ferromagnetism developed in the 1920’s is, as Hopfie ...
... function as the neural activities evolve through time. This function is associated with energy because the mathematics in 3 some cases is identical to that describing the evolution of physical systems with declining free energy. The Ising model of ferromagnetism developed in the 1920’s is, as Hopfie ...
Insect Bio-inspired Neural Network Provides New Evidence on How
... To evaluate the performance of our models, we simulated the theoretical responses of mushroom body Kenyon cells [16, 21] to a range of achromatic patterns previously used in honeybee behavioural experiments [18–20]. These particular experiments were selected primarily because of the complexity of th ...
... To evaluate the performance of our models, we simulated the theoretical responses of mushroom body Kenyon cells [16, 21] to a range of achromatic patterns previously used in honeybee behavioural experiments [18–20]. These particular experiments were selected primarily because of the complexity of th ...
associations
... •Again by using the threshold of 2 and a step function we can get the correct answers of (1100) and (0101). •However, keep in mind that there is only a limited number of patterns which can be stored before perfect recall fails. Typical capacity of an associator network is 20% of the total number of ...
... •Again by using the threshold of 2 and a step function we can get the correct answers of (1100) and (0101). •However, keep in mind that there is only a limited number of patterns which can be stored before perfect recall fails. Typical capacity of an associator network is 20% of the total number of ...
Impact of correlated inputs to neurons
... Petersen 2008; Gentet et al. 2010), but their spike responses were not (Gentet et al. 2010). Earlier theoretical work (de la Rocha et al. 2007; Shea-Brown et al. 2008; Tchumatchenko et al. 2010; Rosenbaum and Josic 2011) considered a pair of neurons receiving correlated inputs, leading to membrane p ...
... Petersen 2008; Gentet et al. 2010), but their spike responses were not (Gentet et al. 2010). Earlier theoretical work (de la Rocha et al. 2007; Shea-Brown et al. 2008; Tchumatchenko et al. 2010; Rosenbaum and Josic 2011) considered a pair of neurons receiving correlated inputs, leading to membrane p ...
Interactions between frontal cortex and basal ganglia in working
... memories there are also concomitant disadvantages. For example, because these memories do not involve structural changes, they are transient and, therefore, do not provide a suitable basis for long-term memories. Also, because information is encoded by the activation states of neurons, the capacity ...
... memories there are also concomitant disadvantages. For example, because these memories do not involve structural changes, they are transient and, therefore, do not provide a suitable basis for long-term memories. Also, because information is encoded by the activation states of neurons, the capacity ...
14. Development and Plasticity
... Poisson spike trains and the postsynaptic spike train (averaged over all presynaptic spike trains) in simulation of an IF-neuron with 1000 input channels. The spike trains that lead to the results shown by stars were generated with each weight value fixed to value 0.015. The cross-correlations are c ...
... Poisson spike trains and the postsynaptic spike train (averaged over all presynaptic spike trains) in simulation of an IF-neuron with 1000 input channels. The spike trains that lead to the results shown by stars were generated with each weight value fixed to value 0.015. The cross-correlations are c ...
14. Development and Plasticity
... Poisson spike trains and the postsynaptic spike train (averaged over all presynaptic spike trains) in simulation of an IF-neuron with 1000 input channels. The spike trains that lead to the results shown by stars were generated with each weight value fixed to value 0.015. The cross-correlations are c ...
... Poisson spike trains and the postsynaptic spike train (averaged over all presynaptic spike trains) in simulation of an IF-neuron with 1000 input channels. The spike trains that lead to the results shown by stars were generated with each weight value fixed to value 0.015. The cross-correlations are c ...
Classification using sparse representations
... Where e is a (m by 1) vector of error-detecting neuron activations; y is a (n by 1) vector of prediction neuron activations; W is a (n by m) matrix of feedforward synaptic weight values; V is a (m by n) matrix of feedback synaptic weight values; 1 and 2 are parameters; and and ⊗ indicate element ...
... Where e is a (m by 1) vector of error-detecting neuron activations; y is a (n by 1) vector of prediction neuron activations; W is a (n by m) matrix of feedforward synaptic weight values; V is a (m by n) matrix of feedback synaptic weight values; 1 and 2 are parameters; and and ⊗ indicate element ...
Multi-item Memory in the Primate Prefrontal Cortex
... One of the first hints that the prefrontal cortex was important for impulse control and the control of context-dependent behavior was the case of frontal damage in the patient Phineas Gage (Harlow, 1848, 1868). Gage recovered from the passage of an iron rod through his frontal cortex and exhibited ...
... One of the first hints that the prefrontal cortex was important for impulse control and the control of context-dependent behavior was the case of frontal damage in the patient Phineas Gage (Harlow, 1848, 1868). Gage recovered from the passage of an iron rod through his frontal cortex and exhibited ...
Inhibitory Plasticity Balances Excitation and Inhibition in Sensory
... sharp tuning is not a necessary feature for a sparse sensory representation (25, 26). The sparsity of the response to each signal was a direct consequence of the detailed balance of correlated excitatory and inhibitory synapses as described above, not of the specificity of the tuning curve. The self ...
... sharp tuning is not a necessary feature for a sparse sensory representation (25, 26). The sparsity of the response to each signal was a direct consequence of the detailed balance of correlated excitatory and inhibitory synapses as described above, not of the specificity of the tuning curve. The self ...
PRINCIPLES OF NEUROBIOLOGY CHAPTER 6
... noise in this case is assumed to be random, it can be ‘averaged out’ over time: by monitoring a low signal-to-noise ratio neuron for a long period of time, an observer can increase her confidence that an observed spiking rate or pattern is signal and not noise. Thus, in order to be equally confident ...
... noise in this case is assumed to be random, it can be ‘averaged out’ over time: by monitoring a low signal-to-noise ratio neuron for a long period of time, an observer can increase her confidence that an observed spiking rate or pattern is signal and not noise. Thus, in order to be equally confident ...
Learning place cells, grid cells and invariances: A unifying model
... fast enough to track changes of excitatory weights, so that excitation and inhibition are approximately balanced at all times. ...
... fast enough to track changes of excitatory weights, so that excitation and inhibition are approximately balanced at all times. ...
Implicit Memory for New Associations: An
... from the interpretive encoding operations applied to a word pair, it should be possible to demonstrate the effect without requiring subjects to engage in an extended elaborative processing task. All that should be necessary is to encourage subjects to initially encode members of a pair in relation t ...
... from the interpretive encoding operations applied to a word pair, it should be possible to demonstrate the effect without requiring subjects to engage in an extended elaborative processing task. All that should be necessary is to encourage subjects to initially encode members of a pair in relation t ...
Bayesian Spiking Neurons II: Learning
... free parameter, the other parameters being constrained by the statistics of the synaptic input st . 3 Learning the Parameters In this section, we show that the parameters of the generative model, ron , roff , wi , θ , corresponding respectively to the temporal dynamics, synaptic weights, and bias, c ...
... free parameter, the other parameters being constrained by the statistics of the synaptic input st . 3 Learning the Parameters In this section, we show that the parameters of the generative model, ron , roff , wi , θ , corresponding respectively to the temporal dynamics, synaptic weights, and bias, c ...
Here - Institute of Cognitive Neuroscience
... their environment. Here we attempt to develop a model of the uses of these internal representations in spatial memory, incorporating data from single-unit recording systems, neuroscience and behavioral studies, and describing how each relates to the other. Central questions in the cognitive neurosci ...
... their environment. Here we attempt to develop a model of the uses of these internal representations in spatial memory, incorporating data from single-unit recording systems, neuroscience and behavioral studies, and describing how each relates to the other. Central questions in the cognitive neurosci ...
Self-Organizing Feature Maps with Lateral Connections: Modeling
... The LISSOM network is a sheet of interconnected neurons ( gure 1). Through the excitatory a erent connections, every neuron receives the same vector of external input values. In addition, each neuron has reciprocal excitatory and inhibitory lateral connections with other neurons. Lateral excitatory ...
... The LISSOM network is a sheet of interconnected neurons ( gure 1). Through the excitatory a erent connections, every neuron receives the same vector of external input values. In addition, each neuron has reciprocal excitatory and inhibitory lateral connections with other neurons. Lateral excitatory ...
Memory, Learning, and Synaptic Plasticity
... corresponding output patterns, Y1, Y2, and Y3. In these input and Left, a highly simplified model is used to illustrate how a synaptic output patterns 1 and 0 represent an action potential or no action matrix can store memory. In this synaptic matrix, axons of five potential, respectively. The integ ...
... corresponding output patterns, Y1, Y2, and Y3. In these input and Left, a highly simplified model is used to illustrate how a synaptic output patterns 1 and 0 represent an action potential or no action matrix can store memory. In this synaptic matrix, axons of five potential, respectively. The integ ...