Preprint - University of Pennsylvania School of Arts and Sciences
... neural computations are techniques that 1) allow us to fit and evaluate biologically plausible descriptions of how the inputs to a brain area are transformed into its output responses in order to perform a specific task, 2) do not depend on a complete, quantitative description of how the inputs are ...
... neural computations are techniques that 1) allow us to fit and evaluate biologically plausible descriptions of how the inputs to a brain area are transformed into its output responses in order to perform a specific task, 2) do not depend on a complete, quantitative description of how the inputs are ...
cogsci200
... Each region encompasses a cortical surface area of roughly 2 mm2 and possesses a total of about 200,000 neurons. ...
... Each region encompasses a cortical surface area of roughly 2 mm2 and possesses a total of about 200,000 neurons. ...
Hierarchical Processing of Auditory Objects in Humans
... inference, Table 1 shows the group Bayes factor (GBF) for model 1 with respect to the other 15 models. Given candidate hypotheses (models) i and j, a Bayes factor of 150 corresponds to a belief of 99% in the statement that ‘‘hypothesis i is true’’. Following the usual conventions in Bayesian statist ...
... inference, Table 1 shows the group Bayes factor (GBF) for model 1 with respect to the other 15 models. Given candidate hypotheses (models) i and j, a Bayes factor of 150 corresponds to a belief of 99% in the statement that ‘‘hypothesis i is true’’. Following the usual conventions in Bayesian statist ...
Specific nonlinear models
... • The signals flow sequentially from the input to the output layer. • For each layer, each unit does the following: 1. scalar product between a vector of weights and the vector of outputs of the previous layer; 2. nonlinear function to each result to produce the input for the next layer ...
... • The signals flow sequentially from the input to the output layer. • For each layer, each unit does the following: 1. scalar product between a vector of weights and the vector of outputs of the previous layer; 2. nonlinear function to each result to produce the input for the next layer ...
Neural Network
... ● Initially consider w1 = -0.2 and w2 = 0.4 ● Training data say, x1 = 0 and x2 = 0, output is 0. ● Compute y = Step(w1*x1 + w2*x2) = 0. Output is correct so weights are not changed. ● For training data x1=0 and x2 = 1, output is 1 ● Compute y = Step(w1*x1 + w2*x2) = 0.4 = 1. Output is correct so wei ...
... ● Initially consider w1 = -0.2 and w2 = 0.4 ● Training data say, x1 = 0 and x2 = 0, output is 0. ● Compute y = Step(w1*x1 + w2*x2) = 0. Output is correct so weights are not changed. ● For training data x1=0 and x2 = 1, output is 1 ● Compute y = Step(w1*x1 + w2*x2) = 0.4 = 1. Output is correct so wei ...
LIONway-slides-chapter9
... • The signals flow sequentially from the input to the output layer. • For each layer, each unit does the following: 1. scalar product between a vector of weights and the vector of outputs of the previous layer; 2. nonlinear function to each result to produce the input for the next layer ...
... • The signals flow sequentially from the input to the output layer. • For each layer, each unit does the following: 1. scalar product between a vector of weights and the vector of outputs of the previous layer; 2. nonlinear function to each result to produce the input for the next layer ...
A Case Study: Improve Classification of Rare Events
... data that affect the model’s accuracy. Therefore, we can either set a smaller number of neighbors or a distance threshold to generate new cases only when two existing cases are close enough. As a further experiment on the Neural Network model, we set 3 as the limit for the distance to see if it can ...
... data that affect the model’s accuracy. Therefore, we can either set a smaller number of neighbors or a distance threshold to generate new cases only when two existing cases are close enough. As a further experiment on the Neural Network model, we set 3 as the limit for the distance to see if it can ...
section 4
... timing mechanisms performed by a limited set of neural patterns that represent temporal units. The first model to be discussed was proposed by Buonomano and Merzenich (1995). This relies on an uncommonly used neural properties termed slow inhibitory postsynaptic potential (Slow-IPSP) and paired puls ...
... timing mechanisms performed by a limited set of neural patterns that represent temporal units. The first model to be discussed was proposed by Buonomano and Merzenich (1995). This relies on an uncommonly used neural properties termed slow inhibitory postsynaptic potential (Slow-IPSP) and paired puls ...
Towards Adversarial Reasoning in Statistical Relational Domains
... this can be solved by standard MAP inference in an MLN. Adversarial relational reasoning can also be used to develop adversarially robust learning methods. For example, suppose we wish to learn the parameters of a webspam classification system that will be robust to adaptive spammers. In standard ma ...
... this can be solved by standard MAP inference in an MLN. Adversarial relational reasoning can also be used to develop adversarially robust learning methods. For example, suppose we wish to learn the parameters of a webspam classification system that will be robust to adaptive spammers. In standard ma ...
Adaptive Practice of Facts in Domains with Varied Prior Knowledge
... usually done in a realistic learning environment, but in a laboratory and in areas with little prior knowledge, e.g. learning of arbitrary word lists, nonsense syllables, obscure facts, or Japanese vocabulary [4, 16]. Such approach facilitates interpretation of the experimental results, but the deve ...
... usually done in a realistic learning environment, but in a laboratory and in areas with little prior knowledge, e.g. learning of arbitrary word lists, nonsense syllables, obscure facts, or Japanese vocabulary [4, 16]. Such approach facilitates interpretation of the experimental results, but the deve ...
Bonaiuto_Progress-Report_3.31.07
... striatum performs reward prediction in the adaptive critic. Brown et al., (1999) present a biologically plausible neural network that produces dopaminergic neuron firing rates corresponding to TD error. In this model, dopaminergic neurons of the SNc are excited by unconditioned stimuli (US) via the ...
... striatum performs reward prediction in the adaptive critic. Brown et al., (1999) present a biologically plausible neural network that produces dopaminergic neuron firing rates corresponding to TD error. In this model, dopaminergic neurons of the SNc are excited by unconditioned stimuli (US) via the ...
CS 561a: Introduction to Artificial Intelligence
... continuous function. Intuition of proof: decompose function to be approximated into a sum of localized “bumps.” The bumps can be constructed with two hidden ...
... continuous function. Intuition of proof: decompose function to be approximated into a sum of localized “bumps.” The bumps can be constructed with two hidden ...
Probabilistic Sense Sentiment Similarity through Hidden Emotions
... the algorithm for this purpose. SS(.,.) indicates sentiment similarity computed by Equation (18). A positive SS means the words are sentimentally similar and thus the answer is yes. However, negative SS leads to a no response. In SO-prediction task, we aim to compute more accurate SO using our senti ...
... the algorithm for this purpose. SS(.,.) indicates sentiment similarity computed by Equation (18). A positive SS means the words are sentimentally similar and thus the answer is yes. However, negative SS leads to a no response. In SO-prediction task, we aim to compute more accurate SO using our senti ...
What is optimal about perception?
... Optimality principles and Bayesian decision theory What is optimal about perception? Motor control and the corollary discharge Neural code efficiency and predictive coding ...
... Optimality principles and Bayesian decision theory What is optimal about perception? Motor control and the corollary discharge Neural code efficiency and predictive coding ...
Mechanism for propagation of rate signals through a 10
... 3.3. Network activity modulated by the synaptic time constant It is worth noting that the synaptic time constant, τsyn , remarkably affects the network dynamics. Figure 4(a) shows f10 versus τsyn for different noise intensities. The curves are unimodal with a peak at τsyn = 3 ms. This is related to th ...
... 3.3. Network activity modulated by the synaptic time constant It is worth noting that the synaptic time constant, τsyn , remarkably affects the network dynamics. Figure 4(a) shows f10 versus τsyn for different noise intensities. The curves are unimodal with a peak at τsyn = 3 ms. This is related to th ...