Neural-Symbolic Learning and Reasoning: Contributions and
... examples of the logical expressions arrive with values for only part of the input space. This suggests that a Lifelong Machine Learning (LML) approach is needed that can consolidate the knowledge of individual examples over many learning episodes (Silver, 2013a; Fowler, 2011). The consolidation of l ...
... examples of the logical expressions arrive with values for only part of the input space. This suggests that a Lifelong Machine Learning (LML) approach is needed that can consolidate the knowledge of individual examples over many learning episodes (Silver, 2013a; Fowler, 2011). The consolidation of l ...
Edge of chaos and prediction of computational performance for
... (Isyn (t) + Ibackground + Inoise ), where τm = 30 ms is the membrane time constant, Isyn models synaptic inputs from other neurons in the circuits, Ibackground models a constant unspecific background input and Inoise models noise in the input. The membrane resistance Rm was chosen as 1 M in all sec ...
... (Isyn (t) + Ibackground + Inoise ), where τm = 30 ms is the membrane time constant, Isyn models synaptic inputs from other neurons in the circuits, Ibackground models a constant unspecific background input and Inoise models noise in the input. The membrane resistance Rm was chosen as 1 M in all sec ...
Associative memory with spatiotemporal chaos control
... values for which chaos is observed in the network. In the simulations, we put these parameters values into 0.9, 0.6, and 0.2, respectively. The corresponding maximum Lyapunov exponent is evaluated as 0.3911~.0!. Figure 3 shows an example of the associative processes for the target pattern ‘‘yacht.’’ ...
... values for which chaos is observed in the network. In the simulations, we put these parameters values into 0.9, 0.6, and 0.2, respectively. The corresponding maximum Lyapunov exponent is evaluated as 0.3911~.0!. Figure 3 shows an example of the associative processes for the target pattern ‘‘yacht.’’ ...
Signals and circuits in the Purkinje neuron NEURAL CIRCUITS Ze’ev R. Abrams
... Purkinje neurons (PN) in the cerebellum have over 100,000 inputs organized in an orthogonal geometry, and a single output channel. As the sole output of the cerebellar cortex layer, their complex firing pattern has been associated with motor control and learning. As such they have been extensively mo ...
... Purkinje neurons (PN) in the cerebellum have over 100,000 inputs organized in an orthogonal geometry, and a single output channel. As the sole output of the cerebellar cortex layer, their complex firing pattern has been associated with motor control and learning. As such they have been extensively mo ...
Realizing Biological Spiking Network Models in a Configurable
... NE of the challenges in simulating large neural networks in a parallel, distributed manner is to ensure sufficient communication bandwidth between the computation nodes. Depending on the neural connection densites and the actual spike rates, communication can in fact constitute the major bottleneck l ...
... NE of the challenges in simulating large neural networks in a parallel, distributed manner is to ensure sufficient communication bandwidth between the computation nodes. Depending on the neural connection densites and the actual spike rates, communication can in fact constitute the major bottleneck l ...
Neural Coding 2016
... bridging disciplines and introducing theoretical ideas and methods to neuroscience research. This concept of combining theoretical and experimental approaches has proven highly successful and nowadays plays a pivotal role in the modern neurosciences. Research in neural coding covers neural represent ...
... bridging disciplines and introducing theoretical ideas and methods to neuroscience research. This concept of combining theoretical and experimental approaches has proven highly successful and nowadays plays a pivotal role in the modern neurosciences. Research in neural coding covers neural represent ...
Nonmonotonic inferences in neural networks
... Thirdly, the next sections will be devoted to showing that schemata support default assumptions about the environ-ment. The neural network is thus capable of filling in missing information. There are some elementary operations on schemata that will be of interest when we consider nonmonotonic infere ...
... Thirdly, the next sections will be devoted to showing that schemata support default assumptions about the environ-ment. The neural network is thus capable of filling in missing information. There are some elementary operations on schemata that will be of interest when we consider nonmonotonic infere ...
Monitoring Piecewise Continuous Behaviors by Refining Semi
... A discontinuous change happens in an instant. Unfortunately, with imprecise models and noisy and finitely sampled observations, we may never be able to determine the precise instant when the change takes place. The best we can do is determine time bounds on the instant when the change occurred. For ...
... A discontinuous change happens in an instant. Unfortunately, with imprecise models and noisy and finitely sampled observations, we may never be able to determine the precise instant when the change takes place. The best we can do is determine time bounds on the instant when the change occurred. For ...
Coupled Noisy Spiking Neurons as Velocity-Controlled
... Hasselmo, 2008; Hasselmo, 2008; Welinder et al., 2008; Burak and Fiete, 2009; Zilli et al., 2009) is that they are presented in terms of abstract, perfect oscillators, whereas oscillators in the brain are noisy and have more complicated dynamics. Zilli et al. (2009) showed that experimentally measur ...
... Hasselmo, 2008; Hasselmo, 2008; Welinder et al., 2008; Burak and Fiete, 2009; Zilli et al., 2009) is that they are presented in terms of abstract, perfect oscillators, whereas oscillators in the brain are noisy and have more complicated dynamics. Zilli et al. (2009) showed that experimentally measur ...
Effects of Correlated Input on Development of Structure in an
... Algorithms to generate Poisson processes are well documented. We use an algorithm described by Pasupathy (2011). In order to show the effect of such a process on the model, as described, we generated a Poisson process with a mean rate of 0.07 events per second, corresponding to the figure of 251.7±5 ...
... Algorithms to generate Poisson processes are well documented. We use an algorithm described by Pasupathy (2011). In order to show the effect of such a process on the model, as described, we generated a Poisson process with a mean rate of 0.07 events per second, corresponding to the figure of 251.7±5 ...
Hypothetical Pattern Recognition Design Using Multi
... is p-dimensional data vector for input feature of an object xi=(x1,x2,…xp)T (T denotes vector transpose). Thus the features are the variables specified by the investigator and thought to be important for classification [16]. We can illustrate of a pattern is as an image data, optical character data, ...
... is p-dimensional data vector for input feature of an object xi=(x1,x2,…xp)T (T denotes vector transpose). Thus the features are the variables specified by the investigator and thought to be important for classification [16]. We can illustrate of a pattern is as an image data, optical character data, ...
5. hierarchical multimodal language modeling
... Figure 6. System state of the model of cortical language areas after simulation step 13. The ‘_blank’ representing the word border between words “bot” and “put” is recognized in area A2 which activates the ‘OFF’ representation in af-A4 which deactivates area A4 for one simulation step. Immediately a ...
... Figure 6. System state of the model of cortical language areas after simulation step 13. The ‘_blank’ representing the word border between words “bot” and “put” is recognized in area A2 which activates the ‘OFF’ representation in af-A4 which deactivates area A4 for one simulation step. Immediately a ...
Scaling self-organizing maps to model large cortical networks
... debug small simulations and then scale them up to larger simulations only when needed. The scaling equations also facilitate the comparison of biological maps and parameters between individuals and species with different brain region sizes. Second, the equations are combined into a new growing map m ...
... debug small simulations and then scale them up to larger simulations only when needed. The scaling equations also facilitate the comparison of biological maps and parameters between individuals and species with different brain region sizes. Second, the equations are combined into a new growing map m ...
Cortex-inspired Developmental Learning for Vision-based Navigation, Attention and Recognition
... behaviors in the challenging task of vision-based navigation, using reinforcement learning and supervised learning jointly. Locally Balanced Incremental Hierarchical Discriminant Regression (LBIHDR) Tree was developed as a cognitive mapping engine to automatically generate internal representations, ...
... behaviors in the challenging task of vision-based navigation, using reinforcement learning and supervised learning jointly. Locally Balanced Incremental Hierarchical Discriminant Regression (LBIHDR) Tree was developed as a cognitive mapping engine to automatically generate internal representations, ...
lect3_classicsystems..
... Reason about domain without actually coding knowledge e.g. mathematical models Generate the correct input-output behaviour over range of tasks; can use as a judge of correctness. BUT means by which provides behaviour not available for inspection or use. ...
... Reason about domain without actually coding knowledge e.g. mathematical models Generate the correct input-output behaviour over range of tasks; can use as a judge of correctness. BUT means by which provides behaviour not available for inspection or use. ...
Dynamic shaping of dopamine signals during probabilistic
... Fig. 3. (a–c) Example dopamine traces on individual trials recorded at individual electrodes in the 0, 0.5 and 1.0 groups are shown. For the 0.5 group, reward trials and reward omission trials are shown separately, though they were randomly interleaved during training. Traces were smoothed with a 5- ...
... Fig. 3. (a–c) Example dopamine traces on individual trials recorded at individual electrodes in the 0, 0.5 and 1.0 groups are shown. For the 0.5 group, reward trials and reward omission trials are shown separately, though they were randomly interleaved during training. Traces were smoothed with a 5- ...
a14b NeuroPhysII
... • Specialized for the release and reception of neurotransmitters • Typically composed of two parts o Axon terminal of the presynaptic neuron, which contains synaptic vesicles o Receptor region on the postsynaptic neuron ...
... • Specialized for the release and reception of neurotransmitters • Typically composed of two parts o Axon terminal of the presynaptic neuron, which contains synaptic vesicles o Receptor region on the postsynaptic neuron ...
