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Exact neural mass model for synaptic-based working memory
[article]
2020
arXiv
pre-print
Preserved Fulltext
In this context, we have developed a neural mass model able to reproduce exactly the dynamics of heterogeneous spiking neural networks encompassing realistic cellular mechanisms for short-term synaptic ...
A synaptic theory of Working Memory (WM) has been developed in the last decade as a possible alternative to the persistent spiking paradigm. ...
Exact neural mass model with STP in Methods). ...
arXiv:2010.07071v1
fatcat:6jfrm2mdujaobioc4rvdqutdu4
Exact neural mass model for synaptic-based working memory
[article]
2020
bioRxiv
pre-print
Preserved Fulltext
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We have developed a neural mass model able to reproduce exactly the dynamics of heterogeneous spiking neural networks with short-term synaptic plasticity. ...
This neural mass model gives access not only to the population firing rate, but also to the mean membrane potential. ...
Exact neural mass model with STP in Methods). ...
doi:10.1101/2020.06.24.168880
fatcat:lmhulx5cczgzrgljbqe6mx2toq
Exact neural mass model for synaptic-based working memory
2020
PLoS Computational Biology
Preserved Fulltext
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In this context, we have developed a neural mass model able to reproduce exactly the dynamics of heterogeneous spiking neural networks encompassing realistic cellular mechanisms for short-term synaptic ...
A synaptic theory of Working Memory (WM) has been developed in the last decade as a possible alternative to the persistent spiking paradigm. ...
to obtain an exact neural mass model for the QIF spiking network with STP. ...
doi:10.1371/journal.pcbi.1008533
pmid:33320855
pmcid:PMC7771880
fatcat:fu3yesghn5h7bokekmmjgp7oju
Optimal Recall from Bounded Metaplastic Synapses: Predicting Functional Adaptations in Hippocampal Area CA3
2014
PLoS Computational Biology
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The Internet Archive has a preservation copy of this work in our general collections.
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Recent treatments based on the notion of metaplasticity provide a powerful model for individual bounded synapses; however, their implications for the ability of the hippocampus to retrieve memories well ...
A venerable history of classical work on autoassociative memory has significantly shaped our understanding of several features of the hippocampus, and most prominently of its CA3 area, in relation to memory ...
We proposed that this is underpinned by a sampling-based neural code for uncertainty in the hippocampus [45, 91] . ...
doi:10.1371/journal.pcbi.1003489
pmid:24586137
pmcid:PMC3937414
fatcat:vkhbsc7ixndahdys6zc24otx4u
A Computational Model of Working Memory Based on Spike-Timing-Dependent Plasticity
2021
Frontiers in Computational Neuroscience
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We propose a working memory model based on spike-timing-dependent plasticity (STDP). ...
Working memory is closely involved in various cognitive activities, but its neural mechanism is still under exploration. ...
Following this idea, we propose a working memory model based on STDP. ...
doi:10.3389/fncom.2021.630999
pmid:33967727
pmcid:PMC8096998
fatcat:4cjhhfgkwbesjehtdp66zzld3a
Simulating the Cortical Microcircuit Significantly Faster Than Real Time on the IBM INC-3000 Neural Supercomputer
2022
Frontiers in Neuroscience
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neural modeling. ...
This article employs the new IBM INC-3000 prototype FPGA-based neural supercomputer to implement a widely used model of the cortical microcircuit. ...
While for purely linear models [LIF and MAT2 (Yamauchi et al., 2011) , both with CUBA-based synaptic coupling], the method of exact integration (Rotter and Diesmann, 1999 ) can be applied, but non-linear ...
doi:10.3389/fnins.2021.728460
pmid:35126034
pmcid:PMC8811464
fatcat:dfw4zurq6ndxhppl3gf7qyjnkq
Emergence of cognitive priming and structure building from the hierarchical interaction of canonical microcircuit models
2019
Biological cybernetics
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In this study, we establish minimal canonical microcircuit models as elements of hierarchical processing networks. ...
Further, we derive and examine two prototypical meta-circuits of cooperating minimal canonical microcircuits for the neurocognitive problems of priming and structure building. ...
In their neural model of working memory, an item is explicitly bound to a specific position and can be recalled by rerouting this neural activity. ...
doi:10.1007/s00422-019-00792-y
pmid:30767085
pmcid:PMC6510829
fatcat:fqzyuwojkfd2hoafsea4fb23aq
Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype
2019
IEEE Transactions on Biomedical Circuits and Systems
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2019 pre-print arXiv:1903.08500v1 fatcat:ckmvto6xazbqzkqd7tkc4ftahi
We implement the recently introduced reward-based synaptic sampling model that employs structural plasticity to learn a function or task. ...
The model implementation is integrated into the SpiNNaker software framework allowing for scalability onto larger systems. ...
In addition, this work was supported by the Center for Advancing Electronics Dresden (cfaed) and the H2020- ...
doi:10.1109/tbcas.2019.2906401
pmid:30932847
fatcat:wagyzgfit5cqjfojmd44g6hmue
PyRates - A Python Framework for Rate-Based Neural Simulations
[article]
2019
bioRxiv
pre-print
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In this work, we present PyRates, a Python framework that provides the means to build a large variety of neural models as a graph. ...
For computational efficiency and parallelization, the model graph is translated into a tensorflow-based compute graph. ...
As an alternative, neural population models (also called neural mass models) have widely been used [21] . ...
doi:10.1101/608067
fatcat:ljbzhig5xve5hfnuoxwzljblam
Spike-Based Synaptic Plasticity in Silicon: Design, Implementation, Application, and Challenges
2014
Proceedings of the IEEE
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plasticity (STDP); spikebased plasticity; spiking neural networks; synaptic plasticity; triplet STDP; very large-scale integration (VLSI); voltage-based STDP ...
These learning rules can be based on abstract computational neuroscience models or on detailed biophysical ones. ...
Boahen, as well as the anonymous reviewers for their fruitful and constructive comments that improved the quality of this paper. ...
doi:10.1109/jproc.2014.2314454
fatcat:mvgzmtj7xja3znyquie4o35ybi
Distinct excitatory and inhibitory bump wandering in a stochastic neural field
[article]
2022
arXiv
pre-print
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Localized persistent cortical neural activity is a validated neural substrate of parametric working memory. Such activity 'bumps' represent the continuous location of a cue over several seconds. ...
Pyramidal (excitatory) and interneuronal (inhibitory) subpopulations exhibit tuned bumps of activity, linking neural dynamics to behavioral inaccuracies observed in memory recall. ...
We thank Sage Shaw for assistance with python code development. ...
arXiv:2203.02438v1
fatcat:q2bfzuztobhzpdbh75jlo47ipq
Pathological Neural Attractor Dynamics in Slowly Growing Gliomas Supports an Optimal Time Frame for White Matter Plasticity
2013
PLoS ONE
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The model predicts an optimal level of synaptic conductance boost that compensates for tumor-induced connectivity loss. ...
Tumors of different configurations show differences in memory recall performance with slightly lower plasticity values for dense tumors compared to more diffuse tumors. ...
Synaptic channels were modeled for AMPA/Kainate, NMDA and GABA A , and included synaptic depression. ...
doi:10.1371/journal.pone.0069798
pmid:23922804
pmcid:PMC3724895
fatcat:vzl67gpog5f5rjyjub3qeyl2xe
The quest for a Quantum Neural Network
2014
Quantum Information Processing
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2014 pre-print arXiv:1408.7005v1 fatcat:czhmdiqrnjf35h5v2cdnlisbh4
It is found that none of the proposals for a potential QNN model fully exploits both the advantages of quantum physics and computing in neural networks. ...
With the overwhelming success in the field of quantum information in the last decades, the "quest" for a Quantum Neural Network (QNN) model began in order to combine quantum computing with the striking ...
In order to access their scope, we want to introduce three minimum requirements for a meaningful QNN that is based on the Hopfield Neural Network model and contains the feature of associative memory. ...
doi:10.1007/s11128-014-0809-8
fatcat:qdujedkxzfhi3mgrsjvs43seeq
Brain-Specific Deletion of GIT1 Impairs Cognition and Alters Phosphorylation of Synaptic Protein Networks Implicated in Schizophrenia Susceptibility
[article]
2018
bioRxiv
pre-print
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We generated conditional neural-selective GIT1 knockout mice and find that these mice have deficits in fear conditioning learning and spatial memory. ...
Using global quantitative phospho-proteomics, we revealed that GIT1 deletion in brain perturbs specific networks of GIT1-interacting synaptic proteins. ...
In addition, we found that neural-specific GIT1 knockout mice had a severe working memory deficit in the spontaneous alternation test (Figure 1) . ...
doi:10.1101/290312
fatcat:nmszeqzewzbhvgfiqqv4fyhvgu
Synaptic mechanisms of interference in working memory
[article]
2017
arXiv
pre-print
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Furthermore, we demonstrate task protocols for which the plastic model performs better than a model with static connectivity: repetitively presented targets are better retained in working memory than targets ...
The model has response statistics whose mean is centered at the true target location across many trials, typical of such visual working memory tasks. ...
We thank Krešimir Josić and Brent Doiron for helpful conversations and comments on the manuscript. ...
arXiv:1706.05395v2
fatcat:heabsqfmznbpjoonnhei7fbkzq
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