Inhibitory interneurons in a cortical column form hot zones of
... hemispheres of four animals (P25–P36, both sexes) were analyzed. All the slices contained the center of D2 (n = 5) and either C2 (n = 3) or E2 (n = 2). Markers were manually placed in somata of neurons and INs [details, especially the correction for doublecounting between slices, are discussed in th ...
... hemispheres of four animals (P25–P36, both sexes) were analyzed. All the slices contained the center of D2 (n = 5) and either C2 (n = 3) or E2 (n = 2). Markers were manually placed in somata of neurons and INs [details, especially the correction for doublecounting between slices, are discussed in th ...
Hippocampal mechanisms for the context-dependent retrieval of episodes 2005 Special issue
... Behaviors ranging from delivering newspapers to waiting tables depend on remembering previous episodes to avoid incorrect repetition. Physiologically, this requires mechanisms for long-term storage and selective retrieval of episodes based on the time of occurrence, despite variable intervals and si ...
... Behaviors ranging from delivering newspapers to waiting tables depend on remembering previous episodes to avoid incorrect repetition. Physiologically, this requires mechanisms for long-term storage and selective retrieval of episodes based on the time of occurrence, despite variable intervals and si ...
An Efficient Hardware Implementation for AI applications
... the inference required. In order to make the grammar compatible with the chosen parser, we introduce the use of a dummy terminal symbol “d”. Consequently, the parser recognizes inputs strings of the form “dd…d|.”. The length of the input is problem length depended. Since ai=d for 1≤i≤n, the cells th ...
... the inference required. In order to make the grammar compatible with the chosen parser, we introduce the use of a dummy terminal symbol “d”. Consequently, the parser recognizes inputs strings of the form “dd…d|.”. The length of the input is problem length depended. Since ai=d for 1≤i≤n, the cells th ...
Neural Machines for Music Recognition
... The outside world is perceived by human beings through the senses. In the outside world all sorts of processes are going on that determine how the world is shaped. These processes generate patterns that are picked up by the senses to provide us with information about the state of the world. For inst ...
... The outside world is perceived by human beings through the senses. In the outside world all sorts of processes are going on that determine how the world is shaped. These processes generate patterns that are picked up by the senses to provide us with information about the state of the world. For inst ...
Directionally Selective Complex Cells and the Computation of
... FIGURE 1. Sampled motion in a directionally selective (DS) striate complex ("C") cell (cell 1-3). (A) Averaged responses to 180 upward (left bins) and downward (right bins) stepwise sequences of a conventional light bar moving across the RF, as shown in B, at a mean speed of 7.8°/sec (note upward pr ...
... FIGURE 1. Sampled motion in a directionally selective (DS) striate complex ("C") cell (cell 1-3). (A) Averaged responses to 180 upward (left bins) and downward (right bins) stepwise sequences of a conventional light bar moving across the RF, as shown in B, at a mean speed of 7.8°/sec (note upward pr ...
PDF - Tuan Anh Le
... These observed values induce a conditional probability distribution over the execution traces whose approximations and expected values we want to characterize by performing inference. An execution trace of a probabilistic program is obtained by successively executing the program deterministically, e ...
... These observed values induce a conditional probability distribution over the execution traces whose approximations and expected values we want to characterize by performing inference. An execution trace of a probabilistic program is obtained by successively executing the program deterministically, e ...
An investigation on local wrinkle-based extractor of age estimation
... this in mind, the experiment set was divided into two into two groups: group A was between age 0 and 20 while group B was above 20 years old. In the experiment 1 and 2, 20 images were selected from group A and 20 images of group B, all of which had frontal pose and clear texture. In the experiment 3 ...
... this in mind, the experiment set was divided into two into two groups: group A was between age 0 and 20 while group B was above 20 years old. In the experiment 1 and 2, 20 images were selected from group A and 20 images of group B, all of which had frontal pose and clear texture. In the experiment 3 ...
Edge of chaos and prediction of computational performance for
... distribution of electrical and biochemical signals impinging on a cortical microcircuit, we make in the present first step of this approach the following simplifying assumptions: 1. Particular neurons (“readout neurons”) learn via synaptic plasticity to extract specific information encoded in the sp ...
... distribution of electrical and biochemical signals impinging on a cortical microcircuit, we make in the present first step of this approach the following simplifying assumptions: 1. Particular neurons (“readout neurons”) learn via synaptic plasticity to extract specific information encoded in the sp ...
1993-Pruning Duplicate Nodes in Depth
... nodes that does not depend on stored nodes, but on another data structure that can detect duplicate nodes that have been generated in the search’s past, and nodes that will be generated in the future. This technique uses limited storage efficiently, uses only constant time per node searched, and red ...
... nodes that does not depend on stored nodes, but on another data structure that can detect duplicate nodes that have been generated in the search’s past, and nodes that will be generated in the future. This technique uses limited storage efficiently, uses only constant time per node searched, and red ...
Algorithmic Specified Complexity in the Game of Life
... specified complexity. We propose a model, algorithmic specified complexity (ACS), whereby specified complexity can be measured in bits. ASC was introduced by Dembski [12]. The topic has been developed and illustrated with a number of elementary examples [10], [11]. Durston et al.’s functional inform ...
... specified complexity. We propose a model, algorithmic specified complexity (ACS), whereby specified complexity can be measured in bits. ASC was introduced by Dembski [12]. The topic has been developed and illustrated with a number of elementary examples [10], [11]. Durston et al.’s functional inform ...
Structured Liquids in Liquid State Machines
... to their resting state. By slowing these neurons, they collect information over longer periods of time, although, at the same time, they produce output only rarely. Our hypothesis is that using a percentage of slow neurons should increase the length of the fading memory and thus increase LSM perform ...
... to their resting state. By slowing these neurons, they collect information over longer periods of time, although, at the same time, they produce output only rarely. Our hypothesis is that using a percentage of slow neurons should increase the length of the fading memory and thus increase LSM perform ...
Introduction to Artificial Intelligence State Space Search
... • In blind search the number of nodes can be extremely large – The order of expanding the nodes is arbitrary – Blind search does not use any properties of the problem being solved – Result is the combinatorial explosion ...
... • In blind search the number of nodes can be extremely large – The order of expanding the nodes is arbitrary – Blind search does not use any properties of the problem being solved – Result is the combinatorial explosion ...
Abstracts - BCCN 2009
... The Hodgkin and Huxley model of a neuron, when driven with constant input, spikes periodically, such that the dynamics trace out a stable, closed orbit in the system's state space, which is composed of the voltage and the gating variables. If the input is not constant, but varies in time around a me ...
... The Hodgkin and Huxley model of a neuron, when driven with constant input, spikes periodically, such that the dynamics trace out a stable, closed orbit in the system's state space, which is composed of the voltage and the gating variables. If the input is not constant, but varies in time around a me ...
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 ...
Seminar Slides - CSE, IIT Bombay
... the question “Can machines think?” Turing's aim is to provide a method to assess whether or not a machine can think. ...
... the question “Can machines think?” Turing's aim is to provide a method to assess whether or not a machine can think. ...
A Novel Connectionist System for Unconstrained Handwriting
... CTC by combining it with a dictionary and language model to obtain word recognition scores that can be compared directly with other systems. Although CTC can be used with any type of RNN, best results are given by networks able to incorporate as much context as possible. For this reason we chose the ...
... CTC by combining it with a dictionary and language model to obtain word recognition scores that can be compared directly with other systems. Although CTC can be used with any type of RNN, best results are given by networks able to incorporate as much context as possible. For this reason we chose the ...
Using Sentence-Level LSTM Language Models for Script Inference
... X Y Z, only X is provided as input. We also present results for systems of the form a X Y , which signifies that the system is trained to decode Y from X with the addition of an attention mechanism. We use the attention mechanism of Vinyals et al. (2015). In short, these models have additional ...
... X Y Z, only X is provided as input. We also present results for systems of the form a X Y , which signifies that the system is trained to decode Y from X with the addition of an attention mechanism. We use the attention mechanism of Vinyals et al. (2015). In short, these models have additional ...
Index Data Structure for Fast Subset and Superset Queries
... and in word. The first IF statement (line 1) checks if a subset of word is found in the tree i.e. the current node of a tree is the last element of subset. The second IF statement (line 4) checks if word has run of the elements. The third IF statement (line 8) verifies if the parallel descend in wor ...
... and in word. The first IF statement (line 1) checks if a subset of word is found in the tree i.e. the current node of a tree is the last element of subset. The second IF statement (line 4) checks if word has run of the elements. The third IF statement (line 8) verifies if the parallel descend in wor ...
link to pdf of article - UCSF Center for Integrative Neuroscience
... Houghton & Hartley, 1996). Associative chaining theories postulate that serial order is stored through learned connections between cells representing successive sequence elements and that each nodeʼs activation in turn causes activation of the subsequent node, enabling sequential readout. In their s ...
... Houghton & Hartley, 1996). Associative chaining theories postulate that serial order is stored through learned connections between cells representing successive sequence elements and that each nodeʼs activation in turn causes activation of the subsequent node, enabling sequential readout. In their s ...
ppt
... • needs no conspiracy to achieve its current value V0, (which must be the current value of at least one of the children) • can achieve any greater value that any of its child nodes may achieve only when all its child nodes have achieved at least that value • its ascending bounds sequence {V0, V1 … V ...
... • needs no conspiracy to achieve its current value V0, (which must be the current value of at least one of the children) • can achieve any greater value that any of its child nodes may achieve only when all its child nodes have achieved at least that value • its ascending bounds sequence {V0, V1 … V ...
Applications of Artificial Neural Networks: A Review
... output is right or not. If the feedback signal says that output is wrong then it does not give any hint as what the right output should be. ...
... output is right or not. If the feedback signal says that output is wrong then it does not give any hint as what the right output should be. ...
VARIABLE BINDING IN BIOLOGICALLY PLAUSIBLE NEURAL
... (i.e., atomic formulae) and complex formulae that consist of combinations of atomic formulae and logical connectives. The issue of representation coincides with another heated philosophical debate, namely, whether “meanings” are represented at all. The sentence John loves Mary may be encoded using ...
... (i.e., atomic formulae) and complex formulae that consist of combinations of atomic formulae and logical connectives. The issue of representation coincides with another heated philosophical debate, namely, whether “meanings” are represented at all. The sentence John loves Mary may be encoded using ...
Practical Applications of Biological Realism in Artificial Neural
... This work reviews our current scientific understanding of intelligence, learning, and memory in biological systems as an aeronautical engineer looks at birds, bats, and flying insects – not with the intention of duplicating them exactly, but of taking functional inspiration and intuition to build b ...
... This work reviews our current scientific understanding of intelligence, learning, and memory in biological systems as an aeronautical engineer looks at birds, bats, and flying insects – not with the intention of duplicating them exactly, but of taking functional inspiration and intuition to build b ...
Yarn tenacity modeling using artificial neural networks and
... included 800, 90, and 100 samples, respectively. The test data was used in the final to assess our neural model. The statistical features of the mentioned data shown in Table 2. 4.1. Neural network structure Previous studies focused on modeling have shown that feed forward networks are suitable for ...
... included 800, 90, and 100 samples, respectively. The test data was used in the final to assess our neural model. The statistical features of the mentioned data shown in Table 2. 4.1. Neural network structure Previous studies focused on modeling have shown that feed forward networks are suitable for ...
Path Planner Application Manual
... for an object paired with or mapped to a unique key) operator [] // for convenient insertion (and retrieval) of objects using a key find // checking if a value has been mapped to a key ...
... for an object paired with or mapped to a unique key) operator [] // for convenient insertion (and retrieval) of objects using a key find // checking if a value has been mapped to a key ...
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.