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Learning Intrinsic Sparse Structures within Long Short-Term Memory
[article]
2018
arXiv
pre-print
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This work aims to learn structurally-sparse Long Short-Term Memory (LSTM) by reducing the sizes of basic structures within LSTM units, including input updates, gates, hidden states, cell states and outputs ...
To overcome the problem, we propose Intrinsic Sparse Structures (ISS) in LSTMs. ...
RELATED WORK
LEARNING INTRINSIC SPARSE STRUCTURES
INTRINSIC SPARSE STRUCTURES The computation within LSTMs is (Hochreiter & Schmidhuber (1997) ) i t = σ (x t · W xi + h t−1 · W hi + b i ) f t = σ ...
arXiv:1709.05027v7
fatcat:klmh7fcrvzhgfcfuv3jn2yszme
Central processing in the mushroom bodies
2014
Current Opinion in Insect Science
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and memory. ...
Here, focusing mainly on olfaction, we discuss functionally related roles the mushroom bodies play in signal gain control, response sparsening, the separation of similar signals (decorrelation), and learning ...
Right: Short-term memory (stm), medium-term memory (mtm) and long-term memory (ltm) are processed in the γ, α'/β', and α/β lobes, respectively. ...
doi:10.1016/j.cois.2014.10.009
pmid:25621203
pmcid:PMC4303581
fatcat:433zmwy5szdfvfm2jciljpq5xu
Functional and comparative assessements of the octopus learning and memory system
2010
Frontiers in bioscience (Scholar edition)
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Long term potentiation (LTP) of the MSF-VL synaptic input 6. How the VL and its LTP are involved in behavioral learning and memory? 7. Summary 7.1. ...
The learning and memory system of Octopus vulgaris 5.1. Morphological organization of the octopus VL-MSF syste 5.2. Electrophysiological characterization of the MSF and VL neurons 5.3. ...
(39) showed that, in the octopus, short-term memory is stored outside the brain area that uses LTP to acquire long-term memory (the VL), since short-term learning during training for the avoidance task ...
doi:10.2741/s99
pmid:20036982
fatcat:6yozxoy4xne5pjgigzcmbqlfzi
Autonomous dynamics in neural networks: the dHAN concept and associative thought processes
2007
AIP Conference Proceedings
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2007 pre-print arXiv:q-bio/0703002v1 fatcat:bho7xrl52nhhhiyqmmyzp72mke
The autonomous dynamics needs a long-term working-point optimization which acquires within the dHAN concept a dual functionality: It stabilizes the time development of the associative thought process and ...
An associative thought-process is characterized, within this approach, by a time-series of transient attractors. Each transient state corresponds to a stored information, a memory. ...
A short-term memory can speed-up the learning process substantially as it stabilizes external patterns and hence gives the system time to consolidate long-term synaptic plasticity. • Systems using sparse ...
doi:10.1063/1.2709594
fatcat:kkyw63csbvgdlfboudeqjziz54
Dynamic Adaptive Network Intelligence
[article]
2015
arXiv
pre-print
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Accurate representational learning of both the explicit and implicit relationships within data is critical to the ability of machines to perform more complex and abstract reasoning tasks. ...
We describe the efficient weakly supervised learning of such inferences by our Dynamic Adaptive Network Intelligence (DANI) model. ...
Although we report the application of DANI as an independent framework for learning representation, we recognize that our system could be employed to condition the input and intermediate layers of neural ...
arXiv:1511.06379v1
fatcat:2obpgubyjnfqhl7hwpw2tfh6qy
Intrinsically Sparse Long Short-Term Memory Networks
[article]
2019
arXiv
pre-print
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Long Short-Term Memory (LSTM) has achieved state-of-the-art performances on a wide range of tasks. ...
Its outstanding performance is guaranteed by the long-term memory ability which matches the sequential data perfectly and the gating structure controlling the information flow. ...
Introduction In recent years, Long Short-Term Memory (LSTM) has returned to people's attention with its outstanding performance in speech recognition (Graves et al., 2013) , neural machine translation ...
arXiv:1901.09208v1
fatcat:auj2bmoajfdjxfsvrptrt4banq
Properties and mechanisms of olfactory learning and memory
2014
Frontiers in Behavioral Neuroscience
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We describe the properties of odor learning intrinsic to the olfactory bulb and review the utility of the olfactory system of adult rodents as a memory system in which to study the cellular mechanisms ...
2014) Properties and mechanisms of olfactory learning and memory. ...
STRUCTURAL MECHANISMS IN THE OB
Long-term potentiation Long-term potentiation has been clearly if sparsely observed in the early olfactory system, notably within piriform cortex and its ascending synapses ...
doi:10.3389/fnbeh.2014.00238
pmid:25071492
pmcid:PMC4083347
fatcat:oeht2fvpzfbmbfvs3rtwv6smoi
A Vision Architecture
[article]
2014
arXiv
pre-print
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In this interpretation the permanent connectivity of cortex is an overlay of well-structured networks, nets, which are formed on the slow time-scale of learning by self-interaction of the network under ...
We are offering a particular interpretation (well within the range of experimentally and theoretically accepted notions) of neural connectivity and dynamics and discuss it as the data-and-process architecture ...
Visual input selects a sparse subset of units (vertical arrows). Neurons within units have WTA dynamics. ...
arXiv:1407.1642v1
fatcat:dcwkwdvg25hrdk2hancvupbov4
Learning precise spatiotemporal sequences via biophysically realistic circuits with modular structure
[article]
2020
bioRxiv
pre-print
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The ability to express and learn temporal sequences is an essential part of learning and memory. ...
This model provides a possible framework for biologically realistic sequence learning and memory, and is in agreement with recent experimental results, which have shown sequence dependent plasticity in ...
Owing to these inputs and due to its strong recurrent connectivity, this network possesses a long-term memory of the state of the columnar network. ...
doi:10.1101/2020.04.17.046862
fatcat:uceqxeg43vduzmbzqjubx3tpki
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
[article]
2016
arXiv
pre-print
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Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. ...
A top-level value function learns a policy over intrinsic goals, and a lower-level function learns a policy over atomic actions to satisfy the given goals. h-DQN allows for flexible goal specifications ...
Hernandez-Gardiol and Mahadevan [19] combined hierarchical RL with a variable length short-term memory of high-level decisions. ...
arXiv:1604.06057v2
fatcat:p33suojusrcpfpg4ybc4hrfj6y
Brain Decoding from Functional MRI Using Long Short-Term Memory Recurrent Neural Networks
[chapter]
2018
Lecture Notes in Computer Science
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In this study, we develop a deep learning based framework for brain decoding by leveraging recent advances in sequence modeling using long short-term memory (LSTM) recurrent neural networks (RNNs). ...
, and LSTM RNNs are adopted to learn decoding mappings between functional profiles and brain states. ...
On the other hand, recurrent neural networks (RNNs) with long short-term memory (LSTM) [10] have achieved remarkable advances in sequence modeling [11] , and these techniques might be powerful alternatives ...
doi:10.1007/978-3-030-00931-1_37
pmid:30320311
pmcid:PMC6180332
fatcat:w2f675q4jbd5tjwfxak2kihv3q
Off-line memory reprocessing following on-line unsupervised learning strongly improves recognition performance in a hierarchical visual memory
2010
The 2010 International Joint Conference on Neural Networks (IJCNN)
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Surprisingly, the positive effect turns out to be independent of synapse-specific plasticity, relying entirely on a homeostatic mechanism equalizing the intrinsic excitability levels of the units within ...
Recently, experience-driven unsupervised learning was shown to create combinatorial parts-based representations in a model of hierarchical visual memory. ...
During on-line learning, the bottomup and lateral synaptic structure within a lower memory layer is developed to represent local facial features and their relations. ...
doi:10.1109/ijcnn.2010.5596765
dblp:conf/ijcnn/JitsevM10
fatcat:ujacyp5v4zgzvaifoksixezudy
Maze Learning using a Hyperdimensional Predictive Processing Cognitive Architecture
[article]
2022
arXiv
pre-print
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We test CogNGen on four maze-learning tasks, including those that test memory and planning, and find that CogNGen matches performance of deep reinforcement learning models and exceeds on a task designed ...
to test memory. ...
Memory For CogNGen, we model both short and long-term memory using the MINERVA 2 model of human memory [9] . ...
arXiv:2204.00619v2
fatcat:l5zoat2inzcqhczr2qtmtmea6i
The less things change, the more they are different: contributions of long-term synaptic plasticity and homeostasis to memory
2014
Learning & memory (Cold Spring Harbor, N.Y.)
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as a consequence of the accommodative interactions between long-term synaptic plasticity and homeostasis. ...
An important cellular mechanism contributing to the strength and duration of memories is activity-dependent alterations in the strength of synaptic connections within the neural circuit encoding the memory ...
Coexpression of homeostatic and plasticity mechanisms regulate basal synaptic transmission Reversal of short-and long-term plasticity or short-and long-term memory typically results in the reversal of ...
doi:10.1101/lm.027326.112
pmid:24532836
pmcid:PMC3929853
fatcat:cgpb322lcvbwbmgiisi75qa3pe
Multi-View Structural Local Subspace Tracking
2017
Sensors
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The proposed model not only exploits the intrinsic relationship among target candidates and their local patches, but also takes advantages of both sparse representation and incremental subspace learning ...
Zhang et al. [33] proposed a structural sparse tracking algorithm which combines global and partial models together, then used the multi-task framework to exploit the intrinsic relationship among different ...
We use it to account for the long-term memory of the target. ...
doi:10.3390/s17040666
pmid:28333088
pmcid:PMC5419779
fatcat:txgmsggatbfrla5jboby4ri4im
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