A Feedback Model of Visual Attention
... The Reynolds and Desimone model, in common with others (e.g., Olshausen et al., 1993), uses top-down signals to multiplicatively modulate the synaptic strengths of inter-regional connections so that attended information can be selectively routed to higher cortical regions. Equivalent results can be ...
... The Reynolds and Desimone model, in common with others (e.g., Olshausen et al., 1993), uses top-down signals to multiplicatively modulate the synaptic strengths of inter-regional connections so that attended information can be selectively routed to higher cortical regions. Equivalent results can be ...
Deep Sparse Rectifier Neural Networks
... the input (although a large enough change can trigger a discrete change of the active set of neurons). The function computed by each neuron or by the network output in terms of the network input is thus linear by parts. We can see the model as an exponential number of linear models that share parame ...
... the input (although a large enough change can trigger a discrete change of the active set of neurons). The function computed by each neuron or by the network output in terms of the network input is thus linear by parts. We can see the model as an exponential number of linear models that share parame ...
Deep Belief Networks Learn Context Dependent Behavior Florian Raudies *
... varied systematically across different contexts. The correct response depended on the stimulus (A,B,C,D) and context quadrant (1,2,3,4). The possible 16 stimulus-context combinations were associated with one of two responses (X,Y), one of which was correct for half of the combinations. The correct r ...
... varied systematically across different contexts. The correct response depended on the stimulus (A,B,C,D) and context quadrant (1,2,3,4). The possible 16 stimulus-context combinations were associated with one of two responses (X,Y), one of which was correct for half of the combinations. The correct r ...
Representation of Number in Animals and Humans: A Neural Model
... strongly to) a specific number of objects. The critical properties of these number-selective neurons are the following. First, they act like filters over numerosity: Neurons that are most responsive to a particular numerosity x also react somewhat weaker to numerosities x 1 and x + 1, still somew ...
... strongly to) a specific number of objects. The critical properties of these number-selective neurons are the following. First, they act like filters over numerosity: Neurons that are most responsive to a particular numerosity x also react somewhat weaker to numerosities x 1 and x + 1, still somew ...
Chapter 1: Application of Artificial Intelligence in Construction
... irrefutable reasoning processes. His famous syllogisms provided patterns for argument structures that always gave correct conclusions given correct premises. For example, ``Socrates is a man; all men are mortal; therefore Socrates is mortal.'' These laws of thought were supposed to govern the operat ...
... irrefutable reasoning processes. His famous syllogisms provided patterns for argument structures that always gave correct conclusions given correct premises. For example, ``Socrates is a man; all men are mortal; therefore Socrates is mortal.'' These laws of thought were supposed to govern the operat ...
6. Data-Based Models
... continue in the future even given our continually improving digital processing technology capabilities. While recent developments in information technology (IT) have mastered and outperformed much of the information processing one can do just using brain power, IT has not mastered the reasoning powe ...
... continue in the future even given our continually improving digital processing technology capabilities. While recent developments in information technology (IT) have mastered and outperformed much of the information processing one can do just using brain power, IT has not mastered the reasoning powe ...
Intelligent Systems - Teaching-WIKI
... – They have directed cycles with delays: they have internal states (like flip flops), can oscillate, etc. – The response to an input depends on the initial state which may depend on previous inputs. – This creates an internal state of the network which allows it to exhibit dynamic temporal behaviour ...
... – They have directed cycles with delays: they have internal states (like flip flops), can oscillate, etc. – The response to an input depends on the initial state which may depend on previous inputs. – This creates an internal state of the network which allows it to exhibit dynamic temporal behaviour ...
A New Local Move Operator for Reconstructing Gene Regulatory
... and sequence polymorphisms observed by genetic markers [1] [2](Chap. 4). Among the many existing frameworks used to infer GRN, we choose probabilistic graphical models and more specifically static Bayesian Networks (BN) [3]. Learning BN structures from data is a NP-hard problem [4] and several appro ...
... and sequence polymorphisms observed by genetic markers [1] [2](Chap. 4). Among the many existing frameworks used to infer GRN, we choose probabilistic graphical models and more specifically static Bayesian Networks (BN) [3]. Learning BN structures from data is a NP-hard problem [4] and several appro ...
The Relevance of Artificial Intelligence for Human Cognition
... first-order theories by neural networks. We claim that appropriate solutions in artificial intelligence can provide explanations in cognitive science by using well-established formal methods, the rigorous specification of the problem, and the practical realization in a computer program. More precise ...
... first-order theories by neural networks. We claim that appropriate solutions in artificial intelligence can provide explanations in cognitive science by using well-established formal methods, the rigorous specification of the problem, and the practical realization in a computer program. More precise ...
Analogy-based Reasoning With Memory Networks - CEUR
... function l(el , er ) = zTl M zr , where zl and zr are the concatenated word embeddings xs , xvl , xo and xs , xvr , xo , respectively, and parameter matrix M ∈ R3d×3d . We denote this model as Bai2009. We also test three neural network architecture that were proposed in different contexts. The model ...
... function l(el , er ) = zTl M zr , where zl and zr are the concatenated word embeddings xs , xvl , xo and xs , xvr , xo , respectively, and parameter matrix M ∈ R3d×3d . We denote this model as Bai2009. We also test three neural network architecture that were proposed in different contexts. The model ...
A Taxonomy of Artificial Intelligence Approaches for Adaptive
... the input layer, zero or more hidden layers, and the output layer. Nodes in the input layer receive data from the environment, and nodes in the output layer produce the network’s learned response to the given input. The hidden layers lie between the input and output layers and are “hidden” in that t ...
... the input layer, zero or more hidden layers, and the output layer. Nodes in the input layer receive data from the environment, and nodes in the output layer produce the network’s learned response to the given input. The hidden layers lie between the input and output layers and are “hidden” in that t ...
Specification of Cerebral Cortical Areas
... across the fetal cerebral wall from the beginning of corticogenesis but are most prominent during midgestation when many of them temporarily stop dividing (13). During the migratory period, cohorts of cells originating in individual proliferative units follow a radial pathway consisting of a single ...
... across the fetal cerebral wall from the beginning of corticogenesis but are most prominent during midgestation when many of them temporarily stop dividing (13). During the migratory period, cohorts of cells originating in individual proliferative units follow a radial pathway consisting of a single ...
PDF
... the processes of learning in the cognitive system and the neurological functions of the brain and capable of predicting new patterns (on specific attributes) from other patterns (on the same or other attributes) after executing a process of so-called learning from existing data (Haykin, 2009). Multi ...
... the processes of learning in the cognitive system and the neurological functions of the brain and capable of predicting new patterns (on specific attributes) from other patterns (on the same or other attributes) after executing a process of so-called learning from existing data (Haykin, 2009). Multi ...
Transfer Learning of Latin and Greek Characters in
... Artificial neural networks (ANN) have had a resurgence in the popularity in recent years. This can, in part, be attributed to both improvements in computational capability (parallel computation) as well as new learning methods and network architectures. The ancestor of the modern neural network was ...
... Artificial neural networks (ANN) have had a resurgence in the popularity in recent years. This can, in part, be attributed to both improvements in computational capability (parallel computation) as well as new learning methods and network architectures. The ancestor of the modern neural network was ...
2. HNN - Academic Science,International Journal of Computer Science
... implementation of a pattern recognizer remains mysterious goal [20]. The best pattern recognizers in most instances are humans, yet the understanding how humans recognize patterns is not known. Human brain has been the basic motivation in the endeavor to building intelligent machine in the field of ...
... implementation of a pattern recognizer remains mysterious goal [20]. The best pattern recognizers in most instances are humans, yet the understanding how humans recognize patterns is not known. Human brain has been the basic motivation in the endeavor to building intelligent machine in the field of ...
CH08_withFigures
... unsupervised mode – Kohonen’s algorithm forms “feature maps,” where neighborhoods of neurons are constructed – These neighborhoods are organized such that topologically close neurons are sensitive to similar inputs into the model – Self-organizing maps, or self organizing feature maps, can sometimes ...
... unsupervised mode – Kohonen’s algorithm forms “feature maps,” where neighborhoods of neurons are constructed – These neighborhoods are organized such that topologically close neurons are sensitive to similar inputs into the model – Self-organizing maps, or self organizing feature maps, can sometimes ...
GENERAL CONCLUSIONS
... glomeruli, those which get a weak or intermediate RN input revealed a decreased sensitivity, leading to reduced or even no responses in their PNs (CHAPTER I, IV). This indicates specific inhibitory interactions within the AL. Thus, the strong glomerular responses are further emphasized by the specif ...
... glomeruli, those which get a weak or intermediate RN input revealed a decreased sensitivity, leading to reduced or even no responses in their PNs (CHAPTER I, IV). This indicates specific inhibitory interactions within the AL. Thus, the strong glomerular responses are further emphasized by the specif ...
BC34333339
... Mix designs often use volume as a key parameter because of the importance of the need to over fill the voids between the aggregate particles. Some methods try to fit available constituents to an optimized grading envelope. Another method is to evaluate and optimize the flow and stability of first th ...
... Mix designs often use volume as a key parameter because of the importance of the need to over fill the voids between the aggregate particles. Some methods try to fit available constituents to an optimized grading envelope. Another method is to evaluate and optimize the flow and stability of first th ...
Dynamic `frees: A Structured Variational Method Giving Efficient
... Given this, it would appear sensible to model objects hierarchically. A simple deterministic model will not capture the variability in object structure between dif ferent images or parts of images, thus a probabilistic model is more appropriate. Using a tree-structured directed graph (see figure 1) ...
... Given this, it would appear sensible to model objects hierarchically. A simple deterministic model will not capture the variability in object structure between dif ferent images or parts of images, thus a probabilistic model is more appropriate. Using a tree-structured directed graph (see figure 1) ...
System and Method for Deep Learning with Insight
... • Idea: deep learning networks are good at learning many different things. Why not use a deep learning network to learn how to communicate with deep learning networks? • Introducing the concept of a Socratic coach: A Socratic coach is a second deep learning system associated with a primary deep lear ...
... • Idea: deep learning networks are good at learning many different things. Why not use a deep learning network to learn how to communicate with deep learning networks? • Introducing the concept of a Socratic coach: A Socratic coach is a second deep learning system associated with a primary deep lear ...
Course : Artificial Intelligence
... 35. Using TMS, if a group of people is planning to make a trip, the system is going to make a compromise between all the participants to choose the free day for all. The chosen day has to be warm and either Monday, Tuesday or Wednesday. Show some of the nodes that might be produced by the system in ...
... 35. Using TMS, if a group of people is planning to make a trip, the system is going to make a compromise between all the participants to choose the free day for all. The chosen day has to be warm and either Monday, Tuesday or Wednesday. Show some of the nodes that might be produced by the system in ...
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE)
... data, sending images and physiological signals over internet by store and forward method and final videoconferencing all improved drastically tele-medicine setup for referral health services mainly. But everywhere, data, image and signals were transmitted for the ultimate decision taking by human br ...
... data, sending images and physiological signals over internet by store and forward method and final videoconferencing all improved drastically tele-medicine setup for referral health services mainly. But everywhere, data, image and signals were transmitted for the ultimate decision taking by human br ...
How to Get from Interpolated Keyframes to Neural
... selector. Tiňo et al. [13] discuss a more sophisticated approach to implement a Finite State Machine (FSM) using a recurrent neural network. A third consideration by Afraimovich et al. [1] is based on stable heteroclinic sequences of a dynamical system. In comparison to the two methods mentioned ab ...
... selector. Tiňo et al. [13] discuss a more sophisticated approach to implement a Finite State Machine (FSM) using a recurrent neural network. A third consideration by Afraimovich et al. [1] is based on stable heteroclinic sequences of a dynamical system. In comparison to the two methods mentioned ab ...
A Machine Learning Approach for Abstraction based on the Idea of
... training data with the target to auto associate the input vector’s states. The higher the number of input vectors, the more unlikely it is that even one of the input vectors can be associated correctly. However, after the training has finished, the weighs of the current Boltzmann machine are copied ...
... training data with the target to auto associate the input vector’s states. The higher the number of input vectors, the more unlikely it is that even one of the input vectors can be associated correctly. However, after the training has finished, the weighs of the current Boltzmann machine are copied ...
Hierarchical temporal memory
Hierarchical temporal memory (HTM) is an online machine learning model developed by Jeff Hawkins and Dileep George of Numenta, Inc. that models some of the structural and algorithmic properties of the neocortex. HTM is a biomimetic model based on the memory-prediction theory of brain function described by Jeff Hawkins in his book On Intelligence. HTM is a method for discovering and inferring the high-level causes of observed input patterns and sequences, thus building an increasingly complex model of the world.Jeff Hawkins states that HTM does not present any new idea or theory, but combines existing ideas to mimic the neocortex with a simple design that provides a large range of capabilities. HTM combines and extends approaches used in Sparse distributed memory, Bayesian networks, spatial and temporal clustering algorithms, while using a tree-shaped hierarchy of nodes that is common in neural networks.