Storing overlapping associative memories on latent manifolds in low-rank spiking networks | Paper Citation Record

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This paper cites A learning algorithm for boltzmann machines.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks A learning algorithm for boltzmann machines

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This paper cites Associative memory neural network with low temporal spiking rates.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Associative memory neural network with low temporal spiking rates

Reference 2

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This paper cites Shaping dynamics with multiple populations in low-rank recurrent networks.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Shaping dynamics with multiple populations in low-rank recurrent networks

Reference 3

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This paper cites Predictive coding of dynamical variables in balanced spiking networks.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Predictive coding of dynamical variables in balanced spiking networks

Reference 4

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Storing overlapping associative memories on latent manifolds in low-rank spiking networks Convex optimization

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This paper cites Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons

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This paper cites The geometry of robustness in spiking neural networks.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks The geometry of robustness in spiking neural networks

Reference 7

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This paper cites Computational principles of memory.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Computational principles of memory

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This paper cites Neural population geometry: An approach for understanding biological and artificial neural networks.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Neural population geometry: An approach for understanding biological and artificial neural networks

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This paper cites Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition

Reference 10

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This paper cites Benchmarks for progress in neuromorphic computing.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Benchmarks for progress in neuromorphic computing

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This paper cites The role of population structure in computations through neural dynamics.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks The role of population structure in computations through neural dynamics

Reference 12

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This paper cites A unified approach to building and controlling spiking attractor networks.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks A unified approach to building and controlling spiking attractor networks

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This paper cites Neural engineering: Computation, representation, and dynamics in neurobiological systems.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Neural engineering: Computation, representation, and dynamics in neurobiological systems

Reference 14

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This paper cites Robust computation with rhythmic spike patterns.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Robust computation with rhythmic spike patterns

Reference 15

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Storing overlapping associative memories on latent manifolds in low-rank spiking networks Neural manifolds for the control of movement

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This paper cites The space of interactions in neural network models.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks The space of interactions in neural network models

Reference 17

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This paper cites Associative memory in a network of ‘spiking’ neurons.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Associative memory in a network of ‘spiking’ neurons

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This paper cites Neuronal dynamics: From single neurons to networks and models of cognition.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Neuronal dynamics: From single neurons to networks and models of cognition

Reference 19

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Storing overlapping associative memories on latent manifolds in low-rank spiking networks Population adaptation in efficient balanced networks

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This paper cites Introduction to the theory of neural computation, 1991.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Introduction to the theory of neural computation, 1991

Reference 21

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Storing overlapping associative memories on latent manifolds in low-rank spiking networks Neural networks and physical systems with emergent collective computational abilities

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Storing overlapping associative memories on latent manifolds in low-rank spiking networks Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity

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Storing overlapping associative memories on latent manifolds in low-rank spiking networks Finding the engram

Reference 24

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This paper cites Associative recall of memory without errors.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Associative recall of memory without errors

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Storing overlapping associative memories on latent manifolds in low-rank spiking networks A new frontier for hopfield networks

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Storing overlapping associative memories on latent manifolds in low-rank spiking networks Dense associative memory for pattern recognition

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Storing overlapping associative memories on latent manifolds in low-rank spiking networks In search of the engram

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Storing overlapping associative memories on latent manifolds in low-rank spiking networks Gradient-based learning applied to document recognition

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Storing overlapping associative memories on latent manifolds in low-rank spiking networks Formation and maintenance of neuronal assemblies through synaptic plasticity

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Storing overlapping associative memories on latent manifolds in low-rank spiking networks Networks of spiking neurons can emulate arbitrary hopfield nets in temporal coding

Reference 31

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Storing overlapping associative memories on latent manifolds in low-rank spiking networks Understanding spiking networks through convex optimization

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This paper cites Three types of remapping with linear decoders: a population-geometric perspective.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Three types of remapping with linear decoders: a population-geometric perspective

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This paper cites Linking connectivity, dynamics, and computations in low-rank recurrent neural networks.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Linking connectivity, dynamics, and computations in low-rank recurrent neural networks

Reference 34

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This paper cites Content-addressable memory with spiking neurons.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Content-addressable memory with spiking neurons

Reference 35

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Storing overlapping associative memories on latent manifolds in low-rank spiking networks Information storage and retrieval in spin-glass like neural networks

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This paper cites Approximating nonlinear functions with latent boundaries in low-rank excitatory-inhibitory spiking networks.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Approximating nonlinear functions with latent boundaries in low-rank excitatory-inhibitory spiking networks

Reference 37

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Storing overlapping associative memories on latent manifolds in low-rank spiking networks High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating

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Storing overlapping associative memories on latent manifolds in low-rank spiking networks Engineering recurrent neural networks from task-relevant manifolds and dynamics

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This paper cites Hopfield networks is all you need.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Hopfield networks is all you need

Reference 40

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This paper cites Content addressable memory without catastrophic forgetting by heteroassociation with a fixed scaffold.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Content addressable memory without catastrophic forgetting by heteroassociation with a fixed scaffold

Reference 41

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This paper cites Associative memory in networks of spiking neurons.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Associative memory in networks of spiking neurons

Reference 42

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This paper cites Computational analysis of the role of the hippocampus in memory.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Computational analysis of the role of the hippocampus in memory

Reference 43

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This paper cites Computation through neural population dynamics.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Computation through neural population dynamics

Reference 44

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This paper cites The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks

Reference 45

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This paper cites Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks

Reference 46

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This paper cites Visualizing a joint future of neuroscience and neuromorphic engineering.

Storing overlapping associative memories on latent manifolds in low-rank spiking networks Visualizing a joint future of neuroscience and neuromorphic engineering

Reference 47

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