scholarly journals Disynaptic Effect of Hilar Cells on Pattern Separation in A Spiking Neural Network of Hippocampal Dentate Gyrus

2021 ◽  
Author(s):  
Sang-Yoon Kim ◽  
Woochang Lim
Keyword(s):  
Neural Network ◽  
Dentate Gyrus ◽  
Phase Locking ◽  
Random Phase ◽  
The Other ◽  
Pattern Separation ◽  
Spiking Neural Network ◽  
Hippocampal Dentate Gyrus ◽  
Other Hand ◽  
Separation Degree

We investigate the disynaptic effect of the hilar cells on pattern separation in a spiking neural network of the hippocampal dentate gyrus (DG). The principal granule cells (GCs) in the DG perform pattern separation, transforming similar input patterns into less-similar output patterns. In our DG network, the hilus consists of excitatory mossy cells (MCs) and inhibitory HIPP (hilar perforant path-associated) cells. Here, we consider the disynaptic effects of the MCs and the HIPP cells on the GCs, mediated by the inhibitory basket cells (BCs) in the granular layer; MC → BC → GC and HIPP → BC → GC. Disynaptic inhibition from the MCs tends to decrease the firing activity of the GCs. On the other hand, the HIPP cells disinhibit the intermediate BCs, which leads to increasing the activity of the GCs. By changing the synaptic strength K(BC, X) [from the presynaptic X (= MC or HIPP) to the postsynaptic BC] from the default value K(BC, X)*, we study the change in the pattern separation degree Sd. When decreasing K(BC, MC) or independently increasing K(BC, HIPP) from their default values, Sd is found to decrease (i.e., pattern separation is reduced). On the other hand, as K(BC, MC) is increased or independently K(BC, HIPP) is decreased from their default values, pattern separation becomes enhanced (i.e., Sd increases). In this way, the disynaptic effects of the MCs and the HIPP cells on the pattern separation are opposite ones. Thus, when simultaneously varying both K(BC, MC) and K(BC, HIPP), as a result of balance between the two competing disynaptic effects of the MCs and the HIPP cells, Sd forms a bell-shaped curve with an optimal maximum at their default values. Moreover, we also investigate population and individual behaviors of the sparsely synchronized rhythm of the GCs, and find that the amplitude measure Ma (representing population synchronization degree) and the random-phase-locking degree Ld (denoting individual activity degree) are strongly correlated with the pattern separation degree Sd. Consequently, the larger the synchronization and the random phase-locking degrees of the sparsely synchronized rhythm is, the more the pattern separation becomes enhanced.

scholarly journals Population and Individual Firing Behaviors in Sparsely Synchronized Rhythms in The Hippocampal Dentate Gyrus

2021 ◽  
Author(s):  
Sang-Yoon Kim ◽  
Woochang Lim
Keyword(s):  
Dentate Gyrus ◽  
Granule Cells ◽  
Phase Locking ◽  
Random Phase ◽  
Firing Activity ◽  
Hippocampal Dentate Gyrus ◽  
Population Frequency ◽  
Mossy Cells ◽  
Winner Take All ◽  
Take All

We investigate population and individual firing behaviors in sparsely synchronized rhythms (SSRs) in a spiking neural network of the hippocampal dentate gyrus (DG). The main encoding granule cells (GCs) are grouped into lamellar clusters. In each GC cluster, there is one inhibitory (I) basket cell (BC) along with excitatory (E) GCs, and they form the E-I loop. Winner-take-all competition, leading to sparse activation of the GCs, occurs in each GC cluster. Such sparsity has been thought to enhance pattern separation performed in the DG. During the winner-take-all competition, SSRs are found to appear in each population of the GCs and the BCs through interaction of excitation of the GCs with inhibition of the BCs. Sparsely synchronized spiking stripes appear successively with the population frequency fp (= 13 Hz) in the raster plots of spikes. We also note that excitatory hilar mossy cells (MCs) control the firing activity of the GC-BC loop by providing excitation to both the GCs and the BCs. SSR also appears in the population of MCs via interaction with the GCs (i.e., GC-MC loop). Population behaviors in the SSRs are quantitatively characterized in terms of the synchronization measures. In addition, we investigate individual firing activity of GCs, BCs, and MCs in the SSRs. Individual GCs exhibit random spike skipping, leading to a multi-peaked inter-spike-interval histogram, which is well characterized in terms of the random phase-locking degree. In this case, population-averaged mean-firing-rate (MFR) <fi(GC)> is less than the population frequency fp. On the other hand, both BCs and MCs show "intrastripe" burstings within stripes, together with "interstripe" random spike skipping. Thus, the population-averaged MFR <fi(X)> (X= MC and BC) is larger than fp, in contrast to the case of the GCs. MC loss may occur during epileptogenesis. With decreasing the fraction of the MCs, changes in the population and individual firings in the SSRs are also studied. Finally, quantitative association between the population/individual firing behaviors in the SSRs and the winner-take-all competition is discussed.

scholarly journals Adult Neurogenesis in the Hippocampus From a Perspective of Discrimination and Generalization: A Hypothesis

2017 ◽  
pp. 441-448 ◽  
Author(s):  
A. PISTIKOVA ◽  
H. BROZKA ◽  
A. STUCHLIK
Keyword(s):  
Dentate Gyrus ◽  
Continuous Spectrum ◽  
Adult Neurogenesis ◽  
The Other ◽  
Testable Hypothesis ◽  
Behavioral Tests ◽  
Hippocampal Dentate Gyrus ◽  
Other Hand ◽  
Behavioral Tasks

The function of adult neurogenesis in the dentate gyrus is not yet completely understood, though many competing theories have attempted to explain the function of these newly-generated neurons. Most theories give adult neurogenesis a role in aiding known hippocampal/dentate gyrus functions. Other theories offer a novel role for these new cells based on their unique physiological qualities, such as their low excitability threshold. Many behavioral tests have been used to test these theories, but results have been inconsistent and often contradictory. Substantial variability in tests and protocols may be at least partially responsible for the mixed results. On the other hand, conflicting results arising from the same tests can serve as aids in elucidating the function of adult neurogenesis. Here, we offer a hypothesis that considers the cognitive nature of tasks commonly used to assess the function of adult neurogenesis, and introduce a dichotomy between tasks focused on discrimination vs. generalization. We view these two aspects as opposite ends of the continuous spectrum onto which traditional tests can be mapped. We propose that high neurogenesis favors behavioral discrimination while low adult neurogenesis favors behavioral generalization of a knowledge or rule. Since many tasks require both, the effects of neurogenesis could be cancelled out in many cases. Although speculative, we hope that our view presents an interesting and testable hypothesis of the effect of adult neurogenesis in traditional behavioral tasks. We conclude that new, carefully designed behavioral tests may be necessary to reach a final consensus on the role of adult neurogenesis in behavior.

scholarly journals Exploiting Manipulated Region in an Image using Integrated Convolution Neural Network and LRW Segmentation Features

2019 ◽  
Vol 8 (3) ◽  
pp. 5488-5495
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Random Walk ◽  
Digital Images ◽  
Convolution Neural Network ◽  
The Other ◽  
Final Decision ◽  
Convolution Neural Networks ◽  
Other Hand

To locate the manipulated region in digital images, we suggest to use Convolution Neural Networks and the segmentation based analysis. A unified CNN architecture is designed with set of training procedures for sampled training patches. Tampering map can be generated for the above said Convolution Neural Networks with the help of tampering detectors. In the other hand, a segmentation using lazy random walk based method is second-hand to generate the tampering chance map, finally integrate the maps and generate the final decision map. This can help to locate the manipulated region accurately. Experiments are conducted using the various datasets to prove the efficiency of the suggest method.

scholarly journals A Unified FPGA Virtualization Framework for General-Purpose Deep Neural Networks in the Cloud

2022 ◽  
Vol 15 (3) ◽  
pp. 1-31
Author(s):  
Shulin Zeng ◽  
Guohao Dai ◽  
Hanbo Sun ◽  
Jun Liu ◽  
Shiyao Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Performance ◽  
Deep Neural Networks ◽  
Cost Effective ◽  
General Purpose ◽  
The Other ◽  
Private Cloud ◽  
Single Task ◽  
Other Hand

INFerence-as-a-Service (INFaaS) has become a primary workload in the cloud. However, existing FPGA-based Deep Neural Network (DNN) accelerators are mainly optimized for the fastest speed of a single task, while the multi-tenancy of INFaaS has not been explored yet. As the demand for INFaaS keeps growing, simply increasing the number of FPGA-based DNN accelerators is not cost-effective, while merely sharing these single-task optimized DNN accelerators in a time-division multiplexing way could lead to poor isolation and high-performance loss for INFaaS. On the other hand, current cloud-based DNN accelerators have excessive compilation overhead, especially when scaling out to multi-FPGA systems for multi-tenant sharing, leading to unacceptable compilation costs for both offline deployment and online reconfiguration. Therefore, it is far from providing efficient and flexible FPGA virtualization for public and private cloud scenarios. Aiming to solve these problems, we propose a unified virtualization framework for general-purpose deep neural networks in the cloud, enabling multi-tenant sharing for both the Convolution Neural Network (CNN), and the Recurrent Neural Network (RNN) accelerators on a single FPGA. The isolation is enabled by introducing a two-level instruction dispatch module and a multi-core based hardware resources pool. Such designs provide isolated and runtime-programmable hardware resources, which further leads to performance isolation for multi-tenant sharing. On the other hand, to overcome the heavy re-compilation overheads, a tiling-based instruction frame package design and a two-stage static-dynamic compilation, are proposed. Only the lightweight runtime information is re-compiled with ∼1 ms overhead, thus guaranteeing the private cloud’s performance. Finally, the extensive experimental results show that the proposed virtualized solutions achieve up to 3.12× and 6.18× higher throughput in the private cloud compared with the static CNN and RNN baseline designs, respectively.

scholarly journals Why do hippocampal mossy cells matter? It depends on the frequency and context

2021 ◽  
Author(s):  
Katarina Kolaric ◽  
Christina Strauch ◽  
Yingxin Li ◽  
Sasha Woods ◽  
Marinho A. Lopes ◽  
...  
Keyword(s):  
Spatial Memory ◽  
Dentate Gyrus ◽  
Learning And Memory ◽  
Network Dynamics ◽  
Pattern Separation ◽  
Hippocampal Dentate Gyrus ◽  
Mossy Cells

AbstractThe discrimination of similar episodes and places, and their representation as distinct memories, depend on a process called pattern separation that relies on the circuitry of the hippocampal dentate gyrus (DG). Mossy cells (MCs) are key neurons in the circuitry, but how they influence DG network dynamics, function, and seizure risk has not been fully elucidated. We found the net impact of MCs was inhibitory at physiological frequencies connected with learning and behaviour, and their absence associated with deficits in pattern separation and spatial memory; at higher frequencies, their net impact was excitatory, and their absence protected against seizures. Thus, MCs influence DG outputs in a highly dynamic manner that varies with frequency and context.One-Sentence SummaryHippocampal mossy cells are required for learning and memory; but their absence protects against seizures.

Role Comparison between Deep Belief Neural Network and NeuroEvolution of Augmenting Topologies to Detect Diabetes

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
A.B.M. Wijaya ◽  
D.S. Ikawahyuni ◽  
Rospita Gea ◽  
Febe Maedjaja
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
The Other ◽  
Multi Layer Perceptron ◽  
Initial Weight ◽  
Restricted Boltzmann Machines ◽  
Regression Problem ◽  
Perceptron Algorithm ◽  
Other Hand ◽  
The Neural Network

Diabetes in Indonesia has been perceived as a grave health problem and has been a concern since the early 1980’s [2]. The prevalence of diabetes in adults in Indonesia, as stated by IDF, was 6.2% with the total case amounting to 10.681.400. Moreover, Indonesia is also in the top ten global countries with the highest diabetes case in 2013. This research will investigate the role of Deep Belief Network (DBN) and NeuroEvolution of Augmenting Topology (NEAT) in solving regression problems in detecting diabetes. DBN works by processing the data in unsupervised network architectures. The algorithm puts Restricted Boltzmann Machines (RBM) into a stacked process. The output of the first RBM will be the input for the next RBM. On the other hand, the NEAT algorithm works by investigating the neural network architecture and evaluating the architecture using a multi-layer perceptron algorithm. Collaboration with a Genetic Algorithm in NEAT is the key process in architecture development. The research results showed that DBN could be utilized as the initial weight for Backpropagation Neural Network at 22.61% on average. On the other hand, the NEAT algorithm could be used by collaborating with a multi-layer perceptron to solve this regression problem by providing 74.5% confidence. This work also reveals potential works in the future by combining the Backpropagation algorithm with NEAT as an evaluation function and by combining it with DBN algorithms to process the produced initial weight.

Hippocampal Dentate Gyrus Atrophy Predicts Pattern Separation Impairment in Patients with LGI1 Encephalitis

Neuroscience ◽  
2019 ◽  
Vol 400 ◽  
pp. 120-131 ◽  
Author(s):  
Annika Hanert ◽  
Julius Rave ◽  
Oliver Granert ◽  
Martin Ziegler ◽  
Anya Pedersen ◽  
...  
Keyword(s):  
Dentate Gyrus ◽  
Pattern Separation ◽  
Hippocampal Dentate Gyrus

Temporal Coding: Assembly Formation Through Constructive Interference

2008 ◽  
Vol 20 (7) ◽  
pp. 1796-1820 ◽  
Author(s):  
Thomas Burwick
Keyword(s):  
Neural Network ◽  
Frequency Spectrum ◽  
Phase Locking ◽  
Temporal Coding ◽  
The Other ◽  
Constructive Interference ◽  
Remarkable Feature ◽  
Self Organized ◽  
Oscillatory Neural Network ◽  
Pattern Retrieval

Temporal coding is studied for an oscillatory neural network model with synchronization and acceleration. The latter mechanism refers to increasing (decreasing) the phase velocity of each unit for stronger (weaker) or more coherent (decoherent) input from the other units. It has been demonstrated that acceleration generates the desynchronization that is needed for self-organized segmentation of two overlapping patterns. In this letter, we continue the discussion of this remarkable feature, giving also an example with several overlapping patterns. Due to acceleration, Hebbian memory implies a frequency spectrum for pure pattern states, defined as coherent patterns with decoherent overlapping patterns. With reference to this frequency spectrum and related frequency bands, the process of pattern retrieval, corresponding to the formation of temporal coding assemblies, is described as resulting from constructive interference (with frequency differences due to acceleration) and phase locking (due to synchronization).

scholarly journals A neural network model of hippocampal contributions to category learning

2022 ◽  
Author(s):  
Jelena Sucevic ◽  
Anna C. Schapiro
Keyword(s):  
Neural Network ◽  
Dentate Gyrus ◽  
Entorhinal Cortex ◽  
Network Model ◽  
Neural Network Model ◽  
Category Learning ◽  
Critical Role ◽  
Pattern Separation ◽  
Rapid Binding ◽  
Separation Functions

In addition to its critical role in encoding individual episodes, the hippocampus is capable of extracting regularities across experiences. This ability is central to category learning, and a growing literature indicates that the hippocampus indeed makes important contributions to this kind of learning. Using a neural network model that mirrors the anatomy of the hippocampus, we investigated the mechanisms by which the hippocampus may support novel category learning. We simulated three category learning paradigms and evaluated the network's ability to categorize and to recognize specific exemplars in each. We found that the trisynaptic pathway within the hippocampus-connecting entorhinal cortex to dentate gyrus, CA3, and CA1-was critical for remembering individual exemplars, reflecting the rapid binding and pattern separation functions of this circuit. The monosynaptic pathway from entorhinal cortex to CA1, in contrast, was responsible for detecting the regularities that define category structure, made possible by the use of distributed representations and a slower learning rate. Together, the simulations provide an account of how the hippocampus and its constituent pathways support novel category learning.

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