We define a Potts version of neural networks with q states. We give upper and lower bounds for the storage capacity of this model of associative memory in the sense of exact retrieval of the stored information. The critical capacity is of the order where N is the number of neurons and the constant c increases quadratically with q.Potts network Neural networks Storage capacity Hopfield model
We analyze the storage capacity of the Hopfield model with spatially correlated patterns ¸ i (i.e....
The Bidirectional Associative Memory (B.A.M.) is a neural network which can store and associate pair...
Recurrent neural networks have been shown to be able to store memory patterns as fixed point attract...
We consider the properties of “Potts” neural networks where each neuron can be in $Q$ different stat...
We introduce and analyze a minimal network model of semantic memory in the human brain. The model is...
The storage capacity of a Q-state Hopfield network is determined via finite size scaling for paralle...
A twin-multistate quaternion Hopfield neural network (TMQHNN) is a multistate Hopfield model and can...
Quantum neural networks form one pillar of the emergent field of quantum machine learning. Here quan...
An autoassociative network of Potts units, coupled via tensor connections, has been proposed and ana...
The Hopfield model is a pioneering neural network model with associative memory retrieval. The analy...
The information capacity of general forms of memory is formalized. The number of bits of information...
International audienceThe optimal storage properties of three different neural network models are st...
The Hopfield model of a neural network is studied for p = αN, where p is the number of memorized pat...
AbstractThe focus of the paper is the estimation of the maximum number of states that can be made st...
We analyze the storage capacity of a variant of the Hopfield model with semantically correlated patt...
We analyze the storage capacity of the Hopfield model with spatially correlated patterns ¸ i (i.e....
The Bidirectional Associative Memory (B.A.M.) is a neural network which can store and associate pair...
Recurrent neural networks have been shown to be able to store memory patterns as fixed point attract...
We consider the properties of “Potts” neural networks where each neuron can be in $Q$ different stat...
We introduce and analyze a minimal network model of semantic memory in the human brain. The model is...
The storage capacity of a Q-state Hopfield network is determined via finite size scaling for paralle...
A twin-multistate quaternion Hopfield neural network (TMQHNN) is a multistate Hopfield model and can...
Quantum neural networks form one pillar of the emergent field of quantum machine learning. Here quan...
An autoassociative network of Potts units, coupled via tensor connections, has been proposed and ana...
The Hopfield model is a pioneering neural network model with associative memory retrieval. The analy...
The information capacity of general forms of memory is formalized. The number of bits of information...
International audienceThe optimal storage properties of three different neural network models are st...
The Hopfield model of a neural network is studied for p = αN, where p is the number of memorized pat...
AbstractThe focus of the paper is the estimation of the maximum number of states that can be made st...
We analyze the storage capacity of a variant of the Hopfield model with semantically correlated patt...
We analyze the storage capacity of the Hopfield model with spatially correlated patterns ¸ i (i.e....
The Bidirectional Associative Memory (B.A.M.) is a neural network which can store and associate pair...
Recurrent neural networks have been shown to be able to store memory patterns as fixed point attract...