SYNAPTIC ENERGY DRIVES THE INFORMATION PROCESSING
... of input information among cortical neural ensembles . On the level of detailed networks, some studies have investigated the concept of energy minimization and the involved synaptic dynamics in relation to synchronized activity of networks acting as dynamic systems, see e.g., [25]. It has been shown ...
... of input information among cortical neural ensembles . On the level of detailed networks, some studies have investigated the concept of energy minimization and the involved synaptic dynamics in relation to synchronized activity of networks acting as dynamic systems, see e.g., [25]. It has been shown ...
Neural correlates of odor learning in the honeybee antennal lobe
... Fig. 2. Frequency analysis of the LFP and relationship of spikes to LFP. (a) Top: raw power spectra for the four consecutive 500-ms time windows: spontaneous baseline activity (gray), phasic ON-response (red), sustained response (magenta) and OFF-response (blue). Each box shows the trial-averaged po ...
... Fig. 2. Frequency analysis of the LFP and relationship of spikes to LFP. (a) Top: raw power spectra for the four consecutive 500-ms time windows: spontaneous baseline activity (gray), phasic ON-response (red), sustained response (magenta) and OFF-response (blue). Each box shows the trial-averaged po ...
Multiagent Learning: Basics, Challenges, and
... robot soccer, and coordination of large swarms of robots. Because of their complexity it becomes impossible to engineer optimal solutions by hand, that is, defining beforehand which behavior is optimal in which situation. Moreover, agents need to take into account not only changing circumstances but ...
... robot soccer, and coordination of large swarms of robots. Because of their complexity it becomes impossible to engineer optimal solutions by hand, that is, defining beforehand which behavior is optimal in which situation. Moreover, agents need to take into account not only changing circumstances but ...
FeUdal Networks for Hierarchical Reinforcement
... rewards. (Bellemare et al., 2016a) have significantly advanced the state-of-the-art on Montezuma’s Revenge by using pseudo-count based auxiliary rewards for exploration, which stimulate agents to explore new parts of the state space. The recently proposed UNREAL agent (Jaderberg et al., 2016) also d ...
... rewards. (Bellemare et al., 2016a) have significantly advanced the state-of-the-art on Montezuma’s Revenge by using pseudo-count based auxiliary rewards for exploration, which stimulate agents to explore new parts of the state space. The recently proposed UNREAL agent (Jaderberg et al., 2016) also d ...
Stimulus Dependence of Local Field Potential Spectra: Experiment
... As we will see later, it turns out that, despite its simplicity, the OU process provides a good fit of experimentally recorded LFPs. Importantly, the calculations performed in the next section do not depend on the process being an OU process and can be easily generalized to arbitrary stochastic proc ...
... As we will see later, it turns out that, despite its simplicity, the OU process provides a good fit of experimentally recorded LFPs. Importantly, the calculations performed in the next section do not depend on the process being an OU process and can be easily generalized to arbitrary stochastic proc ...
Thesis - CiteSeerX
... not possible to use a causal rule to perform diagnosis as rule inversion is not defined in the framework. This means that a diagnostic problem solving system must only have diagnostic rules. Mixing of predictive and diagnostic rules in the same rule set results in inconsistencies. Certainty factors ...
... not possible to use a causal rule to perform diagnosis as rule inversion is not defined in the framework. This means that a diagnostic problem solving system must only have diagnostic rules. Mixing of predictive and diagnostic rules in the same rule set results in inconsistencies. Certainty factors ...
Synaptic Pruning in Development: A Novel Account in Neural Terms
... possible value of = 1. In particular, no deletion strategy can yield better performance than the intact network. A similar result was previously shown by [Sompolinsky, 1988] in the Hop eld model. The use here of signal-to-noise analysis enables us to proceed and derive optimal functions under di e ...
... possible value of = 1. In particular, no deletion strategy can yield better performance than the intact network. A similar result was previously shown by [Sompolinsky, 1988] in the Hop eld model. The use here of signal-to-noise analysis enables us to proceed and derive optimal functions under di e ...
Learning of Sequences of Finger Movements and Timing: Frontal
... learning conditions with a visuo-motor control condition. In two learning conditions, the subjects learned either a sequence of finger movements with random timing or a sequence of timing with random use of fingers. In the third condition the subjects learned to execute a sequence of specific finger ...
... learning conditions with a visuo-motor control condition. In two learning conditions, the subjects learned either a sequence of finger movements with random timing or a sequence of timing with random use of fingers. In the third condition the subjects learned to execute a sequence of specific finger ...
A survey of fault localization techniques in computer networks
... of a relationship among system components. The model is usually defined using an objectoriented paradigm [16,22,38,45,97] and frequently has the form of a graph of dependencies among system components. A different model is proposed in SINERGIA [8], which represents structural knowledge as a set of n ...
... of a relationship among system components. The model is usually defined using an objectoriented paradigm [16,22,38,45,97] and frequently has the form of a graph of dependencies among system components. A different model is proposed in SINERGIA [8], which represents structural knowledge as a set of n ...
CCNBook/Neuron
... we know these models aren't just completely made up fantasies? The answer seems simple: the models must be constrained by data at as many levels as possible, and they must generate predictions that can then be tested empirically. In what follows, we discuss different approaches that people might tak ...
... we know these models aren't just completely made up fantasies? The answer seems simple: the models must be constrained by data at as many levels as possible, and they must generate predictions that can then be tested empirically. In what follows, we discuss different approaches that people might tak ...
The Effect of Slow Electrical Stimuli to Achieve Learning in Cultured
... network level, showing that a change in a simple input-output relationship between two neurons required network wide connectivity changes. It is not completely understood how and why slow electrical stimulation (fstim ,1 Hz) may alter network connectivity. A recent study suggested that low frequency ...
... network level, showing that a change in a simple input-output relationship between two neurons required network wide connectivity changes. It is not completely understood how and why slow electrical stimulation (fstim ,1 Hz) may alter network connectivity. A recent study suggested that low frequency ...
The Neural Foundations of Reaction and Action in Aversive Motivation
... multiple physical forms. For example, in the description of the aftermath of the Boston bombings above, the general goal would be to escape the danger; thus, the appropriate response would be to run away from the area. However, if the explosion had taken place on a boat and survivors were in the wat ...
... multiple physical forms. For example, in the description of the aftermath of the Boston bombings above, the general goal would be to escape the danger; thus, the appropriate response would be to run away from the area. However, if the explosion had taken place on a boat and survivors were in the wat ...
A scientific theory of ars memoriae: spatial view cells in a continuous
... The architecture of a continuous attractor neural network (CANN). The architecture is the same as that of a discrete attractor neural network. During learning, external inputs ei with Gaussian spatial fields force the output neurons to fire with rates ri, the recurrent collaterals produce the same r ...
... The architecture of a continuous attractor neural network (CANN). The architecture is the same as that of a discrete attractor neural network. During learning, external inputs ei with Gaussian spatial fields force the output neurons to fire with rates ri, the recurrent collaterals produce the same r ...
Aggregate Input-Output Models of Neuronal Populations
... order statistics such as cross-correlation and coherence measures are often used to gauge the relationship between the spiking activity of pairs of neurons [4], [5]. However, these are limited measures that provide snapshots of interactions between individual neurons from different regions. They she ...
... order statistics such as cross-correlation and coherence measures are often used to gauge the relationship between the spiking activity of pairs of neurons [4], [5]. However, these are limited measures that provide snapshots of interactions between individual neurons from different regions. They she ...
Deep learning in neural networks: An overview
... own DL research group in the past quarter-century. For these reasons, this work should be viewed as merely a snapshot of an ongoing credit assignment process. To help improve it, please do not hesitate to send corrections and suggestions to [email protected]. 1. Introduction to Deep Learning (DL) in ...
... own DL research group in the past quarter-century. For these reasons, this work should be viewed as merely a snapshot of an ongoing credit assignment process. To help improve it, please do not hesitate to send corrections and suggestions to [email protected]. 1. Introduction to Deep Learning (DL) in ...
Insect Bio-inspired Neural Network Provides New Evidence on How
... The ability to discriminate between visual patterns is essential for honeybees allowing them to identify familiar flowers and landmarks while navigating on foraging trips and locating the correct hive entrance upon their return. Nonetheless even for these types of precisely defined visual stimuli, s ...
... The ability to discriminate between visual patterns is essential for honeybees allowing them to identify familiar flowers and landmarks while navigating on foraging trips and locating the correct hive entrance upon their return. Nonetheless even for these types of precisely defined visual stimuli, s ...
On the realization of asymmetric high radix signed digital
... synaptic strengths of biological neurons. In both cases, some inputs are made more important than others so that they have a greater effect on the processing element as they combine to produce a neural response. Component 2.Summation Function: The first step in a processing element's operation is to ...
... synaptic strengths of biological neurons. In both cases, some inputs are made more important than others so that they have a greater effect on the processing element as they combine to produce a neural response. Component 2.Summation Function: The first step in a processing element's operation is to ...
Predictive Coding: A Possible Explanation of Filling
... (HPC)of natural images, which has, recently, gained growing support as the general coding principle of visual cortex [14–24] (For an excellent review see [25]). The root of Hierarchical predictive coding lies in the probabilistic hierarchical generative model and the efficient coding of natural imag ...
... (HPC)of natural images, which has, recently, gained growing support as the general coding principle of visual cortex [14–24] (For an excellent review see [25]). The root of Hierarchical predictive coding lies in the probabilistic hierarchical generative model and the efficient coding of natural imag ...
Down - 서울대 Biointelligence lab
... which is often recorded as the effect of inhibitory synapses on the cell body. (B) The effect of simultaneously activated voltage-gated excitatory synapses that are in close physical proximity to each other (synaptic clusters) can be larger than the sum of the effect of each individual synapse. Exam ...
... which is often recorded as the effect of inhibitory synapses on the cell body. (B) The effect of simultaneously activated voltage-gated excitatory synapses that are in close physical proximity to each other (synaptic clusters) can be larger than the sum of the effect of each individual synapse. Exam ...
Impact of correlated inputs to neurons
... network simulations (Kremkow et al. 2010). Modulation of the activity level of a neuron by background synaptic noise statistics has been demonstrated in in vitro experiments (Sceniak and Sabo 2010). Here, we studied the interplay of multiple potential rate modulating factors observed in experiments, ...
... network simulations (Kremkow et al. 2010). Modulation of the activity level of a neuron by background synaptic noise statistics has been demonstrated in in vitro experiments (Sceniak and Sabo 2010). Here, we studied the interplay of multiple potential rate modulating factors observed in experiments, ...
Biologically Inspired Modular Neural Networks
... Artificial intelligence is the study of intelligent behavior and how computer programs can be made to exhibit such behavior. There are two categories of artificial intelligence from the computational point of view. One is based on symbolism, and the other is based on connectionism. In the former app ...
... Artificial intelligence is the study of intelligent behavior and how computer programs can be made to exhibit such behavior. There are two categories of artificial intelligence from the computational point of view. One is based on symbolism, and the other is based on connectionism. In the former app ...
Feedforward and feedback frequency
... level of description, we simulate each laminar subcircuit with a nonlinear firing rate model, of the Wilson-Cowan type (see Materials and Methods), which represents the mean activities of a population of excitatory neurons and a population of inhibitory neurons. The local circuits of the supra- and ...
... level of description, we simulate each laminar subcircuit with a nonlinear firing rate model, of the Wilson-Cowan type (see Materials and Methods), which represents the mean activities of a population of excitatory neurons and a population of inhibitory neurons. The local circuits of the supra- and ...
Catastrophic interference
Catastrophic Interference, also known as catastrophic forgetting, is the tendency of a artificial neural network to completely and abruptly forget previously learned information upon learning new information. Neural networks are an important part of the network approach and connectionist approach to cognitive science. These networks use computer simulations to try and model human behaviours, such as memory and learning. Catastrophic interference is an important issue to consider when creating connectionist models of memory. It was originally brought to the attention of the scientific community by research from McCloskey and Cohen (1989), and Ractcliff (1990). It is a radical manifestation of the ‘sensitivity-stability’ dilemma or the ‘stability-plasticity’ dilemma. Specifically, these problems refer to the issue of being able to make an artificial neural network that is sensitive to, but not disrupted by, new information. Lookup tables and connectionist networks lie on the opposite sides of the stability plasticity spectrum. The former remains completely stable in the presence of new information but lacks the ability to generalize, i.e. infer general principles, from new inputs. On the other hand, connectionst networks like the standard backpropagation network are very sensitive to new information and can generalize on new inputs. Backpropagation models can be considered good models of human memory insofar as they mirror the human ability to generalize but these networks often exhibit less stability than human memory. Notably, these backpropagation networks are susceptible to catastrophic interference. This is considered an issue when attempting to model human memory because, unlike these networks, humans typically do not show catastrophic forgetting. Thus, the issue of catastrophic interference must be eradicated from these backpropagation models in order to enhance the plausibility as models of human memory.