Efficient multi-sequence memory with controllable steady-state period and high sequence storage capacity

2010 ◽  
Vol 20 (1) ◽  
pp. 17-24 ◽  
Author(s):  
Min Xia ◽  
Yang Tang ◽  
Jian’an Fang ◽  
Feng Pan
Keyword(s):  
Steady State ◽  
Storage Capacity ◽  
Sequence Memory ◽  
Sequence Storage

Sequence Memory Based on Coherent Spin-Interaction Neural Networks

2014 ◽  
Vol 26 (12) ◽  
pp. 2944-2961 ◽  
Author(s):  
Min Xia ◽  
W. K. Wong ◽  
Zhijie Wang
Keyword(s):  
Neural Network ◽  
Steady State ◽  
Storage Capacity ◽  
Memory Models ◽  
Spin Interaction ◽  
Sequence Information ◽  
Exponential Relationship ◽  
Sequence Memory ◽  
Sequence Storage ◽  
Coherent Spin

Sequence information processing, for instance, the sequence memory, plays an important role on many functions of brain. In the workings of the human brain, the steady-state period is alterable. However, in the existing sequence memory models using heteroassociations, the steady-state period cannot be changed in the sequence recall. In this work, a novel neural network model for sequence memory with controllable steady-state period based on coherent spininteraction is proposed. In the proposed model, neurons fire collectively in a phase-coherent manner, which lets a neuron group respond differently to different patterns and also lets different neuron groups respond differently to one pattern. The simulation results demonstrating the performance of the sequence memory are presented. By introducing a new coherent spin-interaction sequence memory model, the steady-state period can be controlled by dimension parameters and the overlap between the input pattern and the stored patterns. The sequence storage capacity is enlarged by coherent spin interaction compared with the existing sequence memory models. Furthermore, the sequence storage capacity has an exponential relationship to the dimension of the neural network.

Selectively Grouping Neurons in Recurrent Networks of Lateral Inhibition

2002 ◽  
Vol 14 (11) ◽  
pp. 2627-2646 ◽  
Author(s):  
Xiaohui Xie ◽  
Richard H. R. Hahnloser ◽  
H. Sebastian Seung
Keyword(s):  
Steady State ◽  
Lateral Inhibition ◽  
Single Neuron ◽  
Storage Capacity ◽  
Activity Level ◽  
Stable Steady State ◽  
Digital Networks ◽  
Linear Threshold ◽  
Random Groups ◽  
Winner Take All

Winner-take-all networks have been proposed to underlie many of the brain's fundamental computational abilities. However, notmuchisknown about how to extend the grouping of potential winners in these networks beyond single neuron or uniformly arranged groups of neurons. We show that competition between arbitrary groups of neurons can be realized by organizing lateral inhibition in linear threshold networks. Given a collection of potentially overlapping groups (with the exception of some degenerate cases), the lateral inhibition results in network dynamics such that any permitted set of neurons that can be coactivated by some input at a stable steady state is contained in one of the groups. The information about the input is preserved in this operation. The activity level of a neuron in a permitted set corresponds to its stimulus strength, amplified by some constant. Sets of neurons that are not part of a group cannot be coactivated by any input at a stable steady state. We analyze the storage capacity of such a network for random groups—the number of random groups the network can store as permitted sets without creating too many spurious ones. In this framework, we calculate the optimal sparsity of the groups (maximizing group entropy). We find that for dense inputs, the optimal sparsity is unphysiologically small. However, when the inputs and the groups are equally sparse, we derive a more plausible optimal sparsity. We believe our results are the first steps toward attractor theories in hybrid analog-digital networks.

Robust sequence memory in sparsely-connected networks with controllable steady-state period

2009 ◽  
Vol 72 (13-15) ◽  
pp. 3123-3130 ◽  
Author(s):  
Min Xia ◽  
Jian’an Fang ◽  
Feng Pan ◽  
En’jian Bai
Keyword(s):  
Steady State ◽  
Sequence Memory

scholarly journals Training a perceptron by a bit sequence: storage capacity

1996 ◽  
Vol 29 (24) ◽  
pp. 7965-7972 ◽  
Author(s):  
M Schröder ◽  
W Kinzel ◽  
I Kanter
Keyword(s):  
Storage Capacity ◽  
Sequence Storage

scholarly journals A semi-analytical solution to accelerate spin-up of a coupled carbon and nitrogen land model to steady state

2012 ◽  
Vol 5 (5) ◽  
pp. 1259-1271 ◽  
Author(s):  
J. Y. Xia ◽  
Y. Q. Luo ◽  
Y.-P. Wang ◽  
E. S. Weng ◽  
O. Hararuk
Keyword(s):  
Steady State ◽  
Analytical Solution ◽  
Carbon Storage ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Storage Capacity ◽  
Computational Time ◽  
Ecosystem Carbon ◽  
Biogeochemical Models ◽  
Pool Sizes

Abstract. The spin-up of land models to steady state of coupled carbon–nitrogen processes is computationally so costly that it becomes a bottleneck issue for global analysis. In this study, we introduced a semi-analytical solution (SAS) for the spin-up issue. SAS is fundamentally based on the analytic solution to a set of equations that describe carbon transfers within ecosystems over time. SAS is implemented by three steps: (1) having an initial spin-up with prior pool-size values until net primary productivity (NPP) reaches stabilization, (2) calculating quasi-steady-state pool sizes by letting fluxes of the equations equal zero, and (3) having a final spin-up to meet the criterion of steady state. Step 2 is enabled by averaged time-varying variables over one period of repeated driving forcings. SAS was applied to both site-level and global scale spin-up of the Australian Community Atmosphere Biosphere Land Exchange (CABLE) model. For the carbon-cycle-only simulations, SAS saved 95.7% and 92.4% of computational time for site-level and global spin-up, respectively, in comparison with the traditional method (a long-term iterative simulation to achieve the steady states of variables). For the carbon–nitrogen coupled simulations, SAS reduced computational cost by 84.5% and 86.6% for site-level and global spin-up, respectively. The estimated steady-state pool sizes represent the ecosystem carbon storage capacity, which was 12.1 kg C m−2 with the coupled carbon–nitrogen global model, 14.6% lower than that with the carbon-only model. The nitrogen down-regulation in modeled carbon storage is partly due to the 4.6% decrease in carbon influx (i.e., net primary productivity) and partly due to the 10.5% reduction in residence times. This steady-state analysis accelerated by the SAS method can facilitate comparative studies of structural differences in determining the ecosystem carbon storage capacity among biogeochemical models. Overall, the computational efficiency of SAS potentially permits many global analyses that are impossible with the traditional spin-up methods, such as ensemble analysis of land models against parameter variations.

Age-dependent decline of steady state dopamine storage capacity of human brain: An FDOPA PET study

2010 ◽  
Vol 31 (3) ◽  
pp. 447-463 ◽  
Author(s):  
Yoshitaka Kumakura ◽  
Ingo Vernaleken ◽  
Hans-Georg Buchholz ◽  
Per Borghammer ◽  
Erik Danielsen ◽  
...  
Keyword(s):  
Steady State ◽  
Human Brain ◽  
Storage Capacity ◽  
Age Dependent

The Effects of Crushed Ice Ingestion Prior to Steady State Exercise in the Heat

2017 ◽  
Vol 27 (3) ◽  
pp. 220-227 ◽  
Author(s):  
Matthew Zimmermann ◽  
Grant Landers ◽  
Karen E. Wallman ◽  
Jacinta Saldaris
Keyword(s):  
Steady State ◽  
Perceived Exertion ◽  
Storage Capacity ◽  
Heat Storage ◽  
Thermal Sensation ◽  
Sweat Rate ◽  
Gross Efficiency ◽  
State Cycle ◽  
Study Participants ◽  
Crushed Ice

This study examined the physiological effects of crushed ice ingestion before steady state exercise in the heat. Ten healthy males with age (23 ± 3 y), height (176.9 ± 8.7 cm), body-mass (73.5 ± 8.0 kg), VO2peak (48.5 ± 3.6 mL∙kg∙min-1) participated in the study. Participants completed 60 min of cycling at 55% of their VO2peak preceded by 30 min of precooling whereby 7 g∙kg-1 of thermoneutral water (CON) or crushed ice (ICE) was ingested. The reduction in Tc at the conclusion of precooling was greater in ICE (-0.9 ± 0.3 °C) compared with CON (-0.2 ± 0.2 °C) (p ≤ .05). Heat storage capacity was greater in ICE compared with CON after precooling (ICE -29.3 ± 4.8 W∙m-2; CON -11.1 ± 7.3 W∙m-2, p < .05). Total heat storage was greater in ICE compared with CON at the end of the steady state cycle (ICE 62.0 ± 12.5 W∙m-2; CON 49.9 ± 13.4 W∙m-2, p < .05). Gross efficiency was higher in ICE compared with CON throughout the steady state cycle (ICE 21.4 ± 1.8%; CON 20.4 ± 1.9%, p < .05). Ice ingestion resulted in a lower thermal sensation at the end of precooling and a lower sweat rate during the initial stages of cycling (p < .05). Sweat loss, respiratory exchange ratio, heart rate and ratings of perceived exertion and thirst were similar between conditions (p > .05). Precooling with crushed ice led to improved gross efficiency while cycling due to an increased heat storage capacity, which was the result of a lower core temperature.

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