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target function
target function

... experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E ...
A Computational Model of the Amygdala Nuclei`s Role in - laral
A Computational Model of the Amygdala Nuclei`s Role in - laral

Integrator or coincidence detector? The role of the cortical neuron
Integrator or coincidence detector? The role of the cortical neuron

... detection, by contrast, implies that most PSPs do not actually contribute to the generation of output signals, and that the number concept, no information can be carried by the precise of relevant PSPs is small compared to the total number of timing of action potentials. Correlations between the PSP ...
A Model of Distributed Sensorimotor Control in the Cockroach
A Model of Distributed Sensorimotor Control in the Cockroach

... Any sudden puff of wind directed toward the American cockroach (Periplaneta americana), such as from an attacking predator, evokes a rapid directional turn away from the wind source followed by a run (Ritzmann, 1984). The initial turn is generally completed in approximately 60 msec after the onset o ...
Article  - Dynamic Connectome Lab
Article - Dynamic Connectome Lab

... of individual neurons, and extracellular recordings using single or multiple electrodes (Brette and Destexhe 2012). While each modality provides some information about the system’s dynamics, it is not always clear how this information is related to the underlying neuronal activity. Intracellular rec ...
Visual Categorization: How the Monkey Brain Does It
Visual Categorization: How the Monkey Brain Does It

NeuroCube Help
NeuroCube Help

... also the minimum and maximum frequency for the uniform distribution. The number of spikes generated by each neuron will be given by both the firing rates and the duration of the ...
Visual Categorization: How the Monkey Brain Does It
Visual Categorization: How the Monkey Brain Does It

... In particular, we analyzed a total of 116 stimulus-selective IT neurons during the “sample” period (100ms to 900ms after stimulus onset). Only a small number of IT neurons responded selectively during the delay period. For the PFC data, there were 67 stimulus-selective neurons during the sample peri ...
Knowledge, Performance, and Task: Décalage and Dynamics in Young Children’s
Knowledge, Performance, and Task: Décalage and Dynamics in Young Children’s

... inputs into responses in novel noun generalization tasks. Insights into these processes come from work by Spencer and colleagues modeling the dynamics of responses in discrimination (same/different) and forced choice tasks (Simmering, Spencer, & Schöner, in press; Spencer, Simmering, & Schutte, in p ...
Special Track on Uncertain Reasoning
Special Track on Uncertain Reasoning

A Taxonomy of the Evolution of Artificial Neural Systems Helmut A
A Taxonomy of the Evolution of Artificial Neural Systems Helmut A

DATA MINING OF INPUTS: ANALYSING MAGNITUDE AND
DATA MINING OF INPUTS: ANALYSING MAGNITUDE AND

A visual processing task: Retina and V1
A visual processing task: Retina and V1

Patient Machine Interface for the Control of Mechanical Ventilation
Patient Machine Interface for the Control of Mechanical Ventilation

... maintain essential functions of the organs of the body on a moment by moment basis [1]. Breathing is subject to both voluntary and automatic control. Voluntary control adjusts breathing during daily activities such as speaking or eating and is thought to be regulated by cortical and subcortical cent ...
Training a Cognitive Agent to Acquire and Represent Knowledge
Training a Cognitive Agent to Acquire and Represent Knowledge

... bridging CA and RL, for acquiring knowledge (KA), as part of an imitation process. Lieberman set the foundations for PBE [6] on which a great deal of our research relies on. However, most of the research by Lieberman (or his associates) does not deal with agents (much less CA), nor does it deal with ...
Document
Document

... The weighted outputs of the last hidden layer are input to units making up the output layer, which emits the network's prediction The network is feed-forward: None of the weights cycles back to an input unit or to an output unit of a previous layer From a statistical point of view, networks perform ...
deep variational bayes filters: unsupervised learning of state space
deep variational bayes filters: unsupervised learning of state space

Toward General Analysis of Recursive Probability Models
Toward General Analysis of Recursive Probability Models

... defined recursively, and is calculated directly from the sub-expressions from which the newly created expression is derived. For a variable, its level is the number denoting the variable. For a A.-abstraction, the level is derived from the level of the expression in the body of the abstraction, but ...
Generative Adversarial Structured Networks
Generative Adversarial Structured Networks

... The generative adversarial learning paradigm has significantly advanced the field of unsupervised learning. The adversarial framework pits a generator against a discriminator in a non-cooperative two-player game: the generator’s goal is to generate artificial samples that are convincing enough to be ...
probabilistic methods for location estimation in wireless
probabilistic methods for location estimation in wireless

BRAIN SIMULATION PLATFORM
BRAIN SIMULATION PLATFORM

Bayesian Retrieval In Associative Memories With Storage Errors
Bayesian Retrieval In Associative Memories With Storage Errors

... efficient associative memory models in terms of information stored per bit of memory. However, it has not been so widely used since its performance degrades significantly if there are errors in the initial patterns presented or if there are errors in the synaptic weight matrix [12]. Both sorts of er ...
Heart Rate and Inter-beat Interval Computation to Diagnose Stress
Heart Rate and Inter-beat Interval Computation to Diagnose Stress

... electrodes works together as the sensors. Two electrodes place in left arm with some distance and one in right arm. The electrodes transform a physical signal from the body into an electrical signal and the signal is then transmitted to the amplifier. ECG amplifier: the amplifier that takes analog s ...
PDF
PDF

Small Networks
Small Networks

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Neural modeling fields

Neural modeling field (NMF) is a mathematical framework for machine learning which combines ideas from neural networks, fuzzy logic, and model based recognition. It has also been referred to as modeling fields, modeling fields theory (MFT), Maximum likelihood artificial neural networks (MLANS).This framework has been developed by Leonid Perlovsky at the AFRL. NMF is interpreted as a mathematical description of mind’s mechanisms, including concepts, emotions, instincts, imagination, thinking, and understanding. NMF is a multi-level, hetero-hierarchical system. At each level in NMF there are concept-models encapsulating the knowledge; they generate so-called top-down signals, interacting with input, bottom-up signals. These interactions are governed by dynamic equations, which drive concept-model learning, adaptation, and formation of new concept-models for better correspondence to the input, bottom-up signals.
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