Learning Through Imitation: a Biological Approach to Robotics
... are also able to engage in various types of social behavior that involve some form of cooperation and coordination among individuals [6]–[9]. The existence of true imitative behavior in the animal kingdom is still in debate [10]–[12], however, social learning can be found in a variety of species pro ...
... are also able to engage in various types of social behavior that involve some form of cooperation and coordination among individuals [6]–[9]. The existence of true imitative behavior in the animal kingdom is still in debate [10]–[12], however, social learning can be found in a variety of species pro ...
Temporal Sequence Detection with Spiking Neurons: Towards
... For a long time, dendrites have been thought to be the structures where complex neuronal computation takes place, but only recently have we begun to understand how they operate. The dendrites do not simply collect and pass synaptic inputs to the soma, but in most cases they actively shape and integr ...
... For a long time, dendrites have been thought to be the structures where complex neuronal computation takes place, but only recently have we begun to understand how they operate. The dendrites do not simply collect and pass synaptic inputs to the soma, but in most cases they actively shape and integr ...
THALAMUS
... spikes during waking and REM-sleep in behaving cats with chronic implants (D-F). Similar changes in firing pattern occur in vitro in these neurons in response to various neurotransmitters released by brainstem modulatory systems (Steriade et al., 1993). ...
... spikes during waking and REM-sleep in behaving cats with chronic implants (D-F). Similar changes in firing pattern occur in vitro in these neurons in response to various neurotransmitters released by brainstem modulatory systems (Steriade et al., 1993). ...
NeuroCube Help
... simulation. After setting all these parameters, click ‘Generate cube’ and the distribution of neurons will be created. Figure 2 shows the interface after clicking ‘Generate cube’. Instead of clicking ‘Generate cube’, you could also have clicked ‘Load cube’ if you wanted to load a neuron configurati ...
... simulation. After setting all these parameters, click ‘Generate cube’ and the distribution of neurons will be created. Figure 2 shows the interface after clicking ‘Generate cube’. Instead of clicking ‘Generate cube’, you could also have clicked ‘Load cube’ if you wanted to load a neuron configurati ...
AI in Automotive? - Linux Foundation Events
... Various deep learning architectures such as deep neural networks, convolutional deep neural networks, and deep belief networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, and music/audio signal recognition where they have been shown to ...
... Various deep learning architectures such as deep neural networks, convolutional deep neural networks, and deep belief networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, and music/audio signal recognition where they have been shown to ...
Down - 서울대 Biointelligence lab
... covering the periodic training domain. (A) Before training all nodes have the same relative weights between them. (B) After training the relative weight structure has changed with a few strong connections and some weaker connections. (C) The regularities of the interactions can be revealed when reor ...
... covering the periodic training domain. (A) Before training all nodes have the same relative weights between them. (B) After training the relative weight structure has changed with a few strong connections and some weaker connections. (C) The regularities of the interactions can be revealed when reor ...
Down
... covering the periodic training domain. (A) Before training all nodes have the same relative weights between them. (B) After training the relative weight structure has changed with a few strong connections and some weaker connections. (C) The regularities of the interactions can be revealed when reor ...
... covering the periodic training domain. (A) Before training all nodes have the same relative weights between them. (B) After training the relative weight structure has changed with a few strong connections and some weaker connections. (C) The regularities of the interactions can be revealed when reor ...
Reflections on agranular architecture: predictive coding in the motor
... equations describing the neuronal dynamics implied by generalised predictive coding (e.g., Equation 3 in [30]). Note the hierarchical structure: predictive coding involves recursive interactions among an arbitrary number of hierarchical levels, of which just one, level (i), is shown in full here. Th ...
... equations describing the neuronal dynamics implied by generalised predictive coding (e.g., Equation 3 in [30]). Note the hierarchical structure: predictive coding involves recursive interactions among an arbitrary number of hierarchical levels, of which just one, level (i), is shown in full here. Th ...
Linking Cognitive Tokens to Biological Signals: Dialogue Context Improves
... is because these levels cannot be considered in complete isolation in cases where higher-level processes have to interact with lower-level processes in real-time contexts with realworld inputs. Specifically, we claim that the nature and timecourse of low-level processes imposes significant constrain ...
... is because these levels cannot be considered in complete isolation in cases where higher-level processes have to interact with lower-level processes in real-time contexts with realworld inputs. Specifically, we claim that the nature and timecourse of low-level processes imposes significant constrain ...
Dynamics of Learning and Recall ... Recurrent Synapses and Cholinergic Modulation
... Models that use excitatory feedback to perform associative memory function commonly prevent runaway excitatory activity by limiting neuronaloutput with sigmoidinput-output functions (Anderson, 1983; Hopfield, 1984; Amit, 1988). Maximum neuronal firing rate is, indeed, limited by the dynamics of volt ...
... Models that use excitatory feedback to perform associative memory function commonly prevent runaway excitatory activity by limiting neuronaloutput with sigmoidinput-output functions (Anderson, 1983; Hopfield, 1984; Amit, 1988). Maximum neuronal firing rate is, indeed, limited by the dynamics of volt ...
Sequence Learning: From Recognition and Prediction to
... examine the field in a cross-disciplinary way and consider all these different perspectives. Accordingly, interdisciplinary sequence-learning gatherings have gone beyond narrowly focused meetings on only a specialized topic such as reinforcement learning or recurrent neural networks. They include re ...
... examine the field in a cross-disciplinary way and consider all these different perspectives. Accordingly, interdisciplinary sequence-learning gatherings have gone beyond narrowly focused meetings on only a specialized topic such as reinforcement learning or recurrent neural networks. They include re ...
A"computational"approach"towards"the"ontogeny"of" mirror"neurons
... the same action. They are modeled as inhibitory because the net flux of activity from PM to STS is known to be net inhibitory. However, the brain contains less inhibitory neurons (20% of all neurons) than excitatory neurons. Therefore, the total number of inhibitory connections is reduced by only al ...
... the same action. They are modeled as inhibitory because the net flux of activity from PM to STS is known to be net inhibitory. However, the brain contains less inhibitory neurons (20% of all neurons) than excitatory neurons. Therefore, the total number of inhibitory connections is reduced by only al ...
TagSpace: Semantic Embeddings from Hashtags
... consider, with the notable exception of Ding et al. (2012), which uses an unsupervised method. As mentioned in Section 1, many approaches learn unsupervised word embeddings. In our experiments we use word2vec (Mikolov et al., 2013) as a representative scalable model for unsupervised embeddings. W SA ...
... consider, with the notable exception of Ding et al. (2012), which uses an unsupervised method. As mentioned in Section 1, many approaches learn unsupervised word embeddings. In our experiments we use word2vec (Mikolov et al., 2013) as a representative scalable model for unsupervised embeddings. W SA ...
Cognitive Primitives for Automated Learning
... The technology with which input is 'scanned', converted, and subsequently used varies with application and models used for storing and matching the resident knowledge. Applications use algorithmic methods that result in accurate or approximate identification. Stored knowledge use raw images and dat ...
... The technology with which input is 'scanned', converted, and subsequently used varies with application and models used for storing and matching the resident knowledge. Applications use algorithmic methods that result in accurate or approximate identification. Stored knowledge use raw images and dat ...
A novel neuroprosthetic interface with the peripheral nervous system
... approach, which entirely avoids the risks associated with surgery, patients have demonstrated the ability to perform such tasks as cursor manipulation and even basic word processing. However, the poor information transfer rates associated with this technique makes its translation to the control of m ...
... approach, which entirely avoids the risks associated with surgery, patients have demonstrated the ability to perform such tasks as cursor manipulation and even basic word processing. However, the poor information transfer rates associated with this technique makes its translation to the control of m ...
Lexical Plasticity in Early Bilinguals Does Not Alter Phoneme
... What is the architecture of the speech processing system? How do the different types of lexical and sublexical information interact? For many years it has been accepted that the way the speech perception system deals with the problem of variability is that of ‘‘filtering it out,’’ thus representing ...
... What is the architecture of the speech processing system? How do the different types of lexical and sublexical information interact? For many years it has been accepted that the way the speech perception system deals with the problem of variability is that of ‘‘filtering it out,’’ thus representing ...
Segmentation of SBFSEM Volume Data of Neural Tissue by
... and anisotropic diffusion. The CNN does not require specification of a feature set and is, in this sense, less biased by prior expectations: Its main design parameter is its topology. On the other hand, training a CNN is quite expensive (in the order of weeks on a PC), whereas the strategy presented h ...
... and anisotropic diffusion. The CNN does not require specification of a feature set and is, in this sense, less biased by prior expectations: Its main design parameter is its topology. On the other hand, training a CNN is quite expensive (in the order of weeks on a PC), whereas the strategy presented h ...
Control of movement direction - Cognitive Science Research Group
... In the introduction to this chapter, it was mentioned that a long–standing controversy in biological motor control is the question about whether muscle dynamics or movement kinematics are represented in the motor cortex (Kalaska et al., 1992; Johnson et al., 2001; Flash and Sejnowski, 2001). The deb ...
... In the introduction to this chapter, it was mentioned that a long–standing controversy in biological motor control is the question about whether muscle dynamics or movement kinematics are represented in the motor cortex (Kalaska et al., 1992; Johnson et al., 2001; Flash and Sejnowski, 2001). The deb ...
On the computational architecture of the neocortex
... indicator of how high-level it is. This is confirmed by comparative neuroanatomy, in that lower mammals have almost all their cortex taken up by the primary motor and sensory areas 4, while an increasing amount of secondary tissue appears in mammals with greater intelligence. Secondly, direct stimul ...
... indicator of how high-level it is. This is confirmed by comparative neuroanatomy, in that lower mammals have almost all their cortex taken up by the primary motor and sensory areas 4, while an increasing amount of secondary tissue appears in mammals with greater intelligence. Secondly, direct stimul ...
Representation of naturalistic image structure in the primate visual
... brain. While the texture model described in the previous paragraph was not originally intended as a model for post-V1 physiology, two modifications allow it to be interpreted as such, at the abstract level of population representation. First, the statistics can be gathered locally, over regions corr ...
... brain. While the texture model described in the previous paragraph was not originally intended as a model for post-V1 physiology, two modifications allow it to be interpreted as such, at the abstract level of population representation. First, the statistics can be gathered locally, over regions corr ...
NEURAL NETWORKS
... receptive zones, constitute two types of cell filaments that are distinguished on morphological grounds; an axon has a smoother surface, fewer branches, and greater length, whereas a dendrite (so called because of its resemblance to a tree) has an irregular surface and more branches ...
... receptive zones, constitute two types of cell filaments that are distinguished on morphological grounds; an axon has a smoother surface, fewer branches, and greater length, whereas a dendrite (so called because of its resemblance to a tree) has an irregular surface and more branches ...
SOILIE: A Computational Model of 2D Visual Imagination
... multiple modules that together create a 2D visual scene from a user-input query. In its current state, the engine takes a single word query as input and returns an imagined 2D image containing several elements related to the initial query. The ultimate goal of SOILIE is to create imagined visual sce ...
... multiple modules that together create a 2D visual scene from a user-input query. In its current state, the engine takes a single word query as input and returns an imagined 2D image containing several elements related to the initial query. The ultimate goal of SOILIE is to create imagined visual sce ...
Physiology
... inhibition of the same neuron to shorten the duration of discharge and prevent any afterdischarge. This occurs, for example, with the spinal motor neurons (the ventral horn cells). Each spinal motor neuron regularly gives off a collateral branch which synapses with an inhibitory interneuron called " ...
... inhibition of the same neuron to shorten the duration of discharge and prevent any afterdischarge. This occurs, for example, with the spinal motor neurons (the ventral horn cells). Each spinal motor neuron regularly gives off a collateral branch which synapses with an inhibitory interneuron called " ...
Probing scale interaction in brain dynamics through synchronization
... The various aforementioned approaches deal with different scales of description, from the macroscopic to the microscopic level. Accordingly, different computational models have been developed to account for the activity at each scale. Single neurons, for instance, can be characterized by detailed bi ...
... The various aforementioned approaches deal with different scales of description, from the macroscopic to the microscopic level. Accordingly, different computational models have been developed to account for the activity at each scale. Single neurons, for instance, can be characterized by detailed bi ...
Artificial Neural Network in Drug Delivery and Pharmaceutical
... Where, Nhiddenis the number of hidden nodes; Ntrn is the number of training sample; R is a constant with values ranging from 5 to 10, Ninp is the number of inputs and Noutis the number of outputs. The final number of process variables and response units depends on the type of the problem and is dete ...
... Where, Nhiddenis the number of hidden nodes; Ntrn is the number of training sample; R is a constant with values ranging from 5 to 10, Ninp is the number of inputs and Noutis the number of outputs. The final number of process variables and response units depends on the type of the problem and is dete ...