HTM Neuron paper 12-1
... Neocortical neurons have thousands of excitatory synapses. It is a mystery how neurons integrate the input from so many synapses and what kind of large-scale network behavior this enables. It has been previously proposed that non-linear properties of dendrites enable neurons to recognize multiple pa ...
... Neocortical neurons have thousands of excitatory synapses. It is a mystery how neurons integrate the input from so many synapses and what kind of large-scale network behavior this enables. It has been previously proposed that non-linear properties of dendrites enable neurons to recognize multiple pa ...
Synaptic Plasticity and Connectivity Requirements to
... measure the distribution of selectivity across cells before and after training. When comparing multiple networks, we use the mean of the stimulus-pair selectivity across cells. In order to determine whether or not the information about stimulus-pairs within a given associative network is sufficient ...
... measure the distribution of selectivity across cells before and after training. When comparing multiple networks, we use the mean of the stimulus-pair selectivity across cells. In order to determine whether or not the information about stimulus-pairs within a given associative network is sufficient ...
Learning Bayesian Networks: A Unification for Discrete and
... p(GB.IB:,�). In the remainder of this section, we show how to compute p(DI8B•,B:, �). A method for computing this term follows from our random-sample assumption. Namely, given hypothe sis B:, it follows that D can be separated into a set of random samples, where these random samples are determined ...
... p(GB.IB:,�). In the remainder of this section, we show how to compute p(DI8B•,B:, �). A method for computing this term follows from our random-sample assumption. Namely, given hypothe sis B:, it follows that D can be separated into a set of random samples, where these random samples are determined ...
Dynamic traffic splitting to parallel wireless networks with partial information: a Bayesian approach
... datagrams at the link layer [7, 21], and also by using multiple TCP sessions in parallel to a file server [31]. In the latter case, each available network transports part of the requested data in a separate TCP session. Previous work has indicated that downloading from multiple networks concurrently ...
... datagrams at the link layer [7, 21], and also by using multiple TCP sessions in parallel to a file server [31]. In the latter case, each available network transports part of the requested data in a separate TCP session. Previous work has indicated that downloading from multiple networks concurrently ...
input output - Brian Nils Lundstrom
... observations have shown that the firing rate responses of some neurons are very sensitive to input fluctuations, while responses of others are not [18, 21]. Interestingly, we found that commonly used neuron models (single-compartment, biophysical models) do not replicate this diversity with their st ...
... observations have shown that the firing rate responses of some neurons are very sensitive to input fluctuations, while responses of others are not [18, 21]. Interestingly, we found that commonly used neuron models (single-compartment, biophysical models) do not replicate this diversity with their st ...
Image Texture Classification using Gray Level Co
... parallel structure and its ability to learn from experience. Neural networks could be used for fairly accurate classification of input data into categories when they are fully trained. A fully trained network is said to be converged when some predetermined specification had been achieved, typically ...
... parallel structure and its ability to learn from experience. Neural networks could be used for fairly accurate classification of input data into categories when they are fully trained. A fully trained network is said to be converged when some predetermined specification had been achieved, typically ...
Contributions and challenges for network models in cognitive
... strategy based on measures of connectional homogeneity that can be uniformly applied across the brain. In another example, comparative studies have shown that the spatial scale of the nodal parcellation affects subsequent network analyses22. One way in which this issue has been addressed involves ne ...
... strategy based on measures of connectional homogeneity that can be uniformly applied across the brain. In another example, comparative studies have shown that the spatial scale of the nodal parcellation affects subsequent network analyses22. One way in which this issue has been addressed involves ne ...
Efficient Deep Feature Learning and Extraction via StochasticNets
... seen, the neural connectivity for randomly realized receptive field Kl,1 is completely different from randomly realized receptive field Kl,2 . The response of each randomly realized receptive field leads to an output in new channel in layer l + 1. A realized deep convolutional StochasticNet can then ...
... seen, the neural connectivity for randomly realized receptive field Kl,1 is completely different from randomly realized receptive field Kl,2 . The response of each randomly realized receptive field leads to an output in new channel in layer l + 1. A realized deep convolutional StochasticNet can then ...
Journal of Cognitive Neuroscience 10:1
... interfaced naturally with the arm model used to generate the input data. Real arms have redundant degrees of freedom, allowing multiple postures to reach the same point in space. We used a two-dimensional arm to avoid this ill-posed problem because we believe it is orthogonal to the questions about ...
... interfaced naturally with the arm model used to generate the input data. Real arms have redundant degrees of freedom, allowing multiple postures to reach the same point in space. We used a two-dimensional arm to avoid this ill-posed problem because we believe it is orthogonal to the questions about ...
Copy of the full paper
... signals by precisely computing the neurons’ asynchronous spikes. Neuron models can precisely describe the biophysics of spikes (action potentials) by computing the currents flowing through cell membrane and synaptic nodes. It is possible to reduce the size of these models to facilitate their computa ...
... signals by precisely computing the neurons’ asynchronous spikes. Neuron models can precisely describe the biophysics of spikes (action potentials) by computing the currents flowing through cell membrane and synaptic nodes. It is possible to reduce the size of these models to facilitate their computa ...
Computational Intelligence Methods
... An operational definition can be computed in reasonable (polynomial) time. Need to learn an operational approximation to the ideal evaluation function. Target function can be represented in many ways: lookup table, symbolic rules, numerical function, neural network. There is a trade-off between the ...
... An operational definition can be computed in reasonable (polynomial) time. Need to learn an operational approximation to the ideal evaluation function. Target function can be represented in many ways: lookup table, symbolic rules, numerical function, neural network. There is a trade-off between the ...
Institute of Psychology C.N.R.
... We have already described some simulations using such a model of development for neural networks [Nolfi and Parisi, in press]. However, in that work the environment had no role in the developmental changes that occurred in the individual under genetic control. In the present model the genetic materi ...
... We have already described some simulations using such a model of development for neural networks [Nolfi and Parisi, in press]. However, in that work the environment had no role in the developmental changes that occurred in the individual under genetic control. In the present model the genetic materi ...
Neural basis of sensorimotor learning: modifying
... changes the categorical mapping between a given sensory cue and a known movement. The learning, however, need not affect the parameters of the movement itself. Therefore, such learning modifies early stages in sensorymotor processing (Figure 1b, right side). Owing to technical and conceptual limitat ...
... changes the categorical mapping between a given sensory cue and a known movement. The learning, however, need not affect the parameters of the movement itself. Therefore, such learning modifies early stages in sensorymotor processing (Figure 1b, right side). Owing to technical and conceptual limitat ...
On the synergy of network science and artificial intelligence
... Most of the science done throughout the human evolution uses the traditional reductionism paradigm, which attempts to explain the behavior of any type of system by summing the behavior of its constituent elements and the interactions between them. Consequently, nowadays we have extremely specialized ...
... Most of the science done throughout the human evolution uses the traditional reductionism paradigm, which attempts to explain the behavior of any type of system by summing the behavior of its constituent elements and the interactions between them. Consequently, nowadays we have extremely specialized ...
Fast Propagation of Firing Rates through Layered Networks of Noisy
... The SD, or noise component, of the current prevents synchronization by ensuring that each neuron is in a different state when the input current arrives. Therefore, each neuron independently estimates the stimulus, and the temporal structure of the response is maintained. Background activity itself i ...
... The SD, or noise component, of the current prevents synchronization by ensuring that each neuron is in a different state when the input current arrives. Therefore, each neuron independently estimates the stimulus, and the temporal structure of the response is maintained. Background activity itself i ...
Digital Selection and Analogue Amplification Coexist in a cortex-inspired silicon circuit
... When we changed the amplitude of the background, the population response remained at the same location with much the same shape in Fig. 2a, but with an amplitude that varied with background amplitude in an approximately linear way (Fig. 2b). Thus, the background modulated the amplitude of the tuning ...
... When we changed the amplitude of the background, the population response remained at the same location with much the same shape in Fig. 2a, but with an amplitude that varied with background amplitude in an approximately linear way (Fig. 2b). Thus, the background modulated the amplitude of the tuning ...
Chunking of Action Sequences in the Cortex
... There is a plethora of different models that try to better mimic the function of the basal ganglia. The level of detail has increased over time. Houk et al. (1995) presented one of the first models. The problem with it was that it did not account for the timed depression of dopamine when an expected r ...
... There is a plethora of different models that try to better mimic the function of the basal ganglia. The level of detail has increased over time. Houk et al. (1995) presented one of the first models. The problem with it was that it did not account for the timed depression of dopamine when an expected r ...
Synchronous vs. Conjunctive Binding: A False Dichotomy? Robert F. Hadley ()
... considerable notice, both because of its biological basis (cf. Singer, 1999, 2003) and because it (purportedly) avoids the proliferation of nodes attributed to conjunctive coding. Evidence for the role of synchronously firing neurons in sensory feature binding is substantial (Singer, 1999), and sync ...
... considerable notice, both because of its biological basis (cf. Singer, 1999, 2003) and because it (purportedly) avoids the proliferation of nodes attributed to conjunctive coding. Evidence for the role of synchronously firing neurons in sensory feature binding is substantial (Singer, 1999), and sync ...
Machine Learning for Medical Diagnosis
... Good performance: The algorithm has to be able to extract significant information from the available data. The diagnostic accuracy on new cases has to be as high as possible. Typically, most of the algorithms perform at least as well as the physicians and often the classification accuracy of machine ...
... Good performance: The algorithm has to be able to extract significant information from the available data. The diagnostic accuracy on new cases has to be as high as possible. Typically, most of the algorithms perform at least as well as the physicians and often the classification accuracy of machine ...
Networks of Spiking Neurons: The Third Generation of
... gates. They give rise to a variety of neural network models such as multilayer perceptrons (also called threshold circuits), Hopfield nets, and Boltzmann machines. A characteristic feature of these models is that they can only give digital output. In fact they are universal for computations with dig ...
... gates. They give rise to a variety of neural network models such as multilayer perceptrons (also called threshold circuits), Hopfield nets, and Boltzmann machines. A characteristic feature of these models is that they can only give digital output. In fact they are universal for computations with dig ...
Catastrophic interference
Catastrophic Interference, also known as catastrophic forgetting, is the tendency of a artificial neural network to completely and abruptly forget previously learned information upon learning new information. Neural networks are an important part of the network approach and connectionist approach to cognitive science. These networks use computer simulations to try and model human behaviours, such as memory and learning. Catastrophic interference is an important issue to consider when creating connectionist models of memory. It was originally brought to the attention of the scientific community by research from McCloskey and Cohen (1989), and Ractcliff (1990). It is a radical manifestation of the ‘sensitivity-stability’ dilemma or the ‘stability-plasticity’ dilemma. Specifically, these problems refer to the issue of being able to make an artificial neural network that is sensitive to, but not disrupted by, new information. Lookup tables and connectionist networks lie on the opposite sides of the stability plasticity spectrum. The former remains completely stable in the presence of new information but lacks the ability to generalize, i.e. infer general principles, from new inputs. On the other hand, connectionst networks like the standard backpropagation network are very sensitive to new information and can generalize on new inputs. Backpropagation models can be considered good models of human memory insofar as they mirror the human ability to generalize but these networks often exhibit less stability than human memory. Notably, these backpropagation networks are susceptible to catastrophic interference. This is considered an issue when attempting to model human memory because, unlike these networks, humans typically do not show catastrophic forgetting. Thus, the issue of catastrophic interference must be eradicated from these backpropagation models in order to enhance the plausibility as models of human memory.