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On stability and associative recall of memories in attractor neural networks.

ABSTRACT: Attractor neural networks such as the Hopfield model can be used to model associative memory. An efficient associative memory should be able to store a large number of patterns which must all be stable. We study in detail the meaning and definition of stability of network states. We reexamine the meanings of retrieval, recognition and recall and assign precise mathematical meanings to each of these terms. We also examine the relation between them and how they relate to memory capacity of the network. We have shown earlier in this journal that orthogonalization scheme provides an effective way of overcoming catastrophic interference that limits the memory capacity of the Hopfield model. It is not immediately apparent whether the improvement made by orthgonalization affects the processes of retrieval, recognition and recall equally. We show that this influence occurs to different degrees and hence affects the relations between them. We then show that the conditions for pattern stability can be split into a necessary condition (recognition) and a sufficient one (recall). We interpret in cognitive terms the information being stored in the Hopfield model and also after it is orthogonalized. We also study the alterations in the network dynamics of the Hopfield network upon the introduction of orthogonalization, and their effects on the efficiency of the network as an associative memory.

SUBMITTER: Sampath S

PROVIDER: S-EPMC7498056 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

On stability and associative recall of memories in attractor neural networks.

Sampath Suchitra S Srivastava Vipin V

PloS one 20200917 9

Attractor neural networks such as the Hopfield model can be used to model associative memory. An efficient associative memory should be able to store a large number of patterns which must all be stable. We study in detail the meaning and definition of stability of network states. We reexamine the meanings of retrieval, recognition and recall and assign precise mathematical meanings to each of these terms. We also examine the relation between them and how they relate to memory capacity of the net ...[more]

PMID: 32941475

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Project description:BackgroundThe experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a Systemic Metabolic Structure in the cell, characterized by a set of different enzymatic reactions always locked into active states (metabolic core) while the rest of the catalytic processes are only intermittently active. This global metabolic structure was verified for Escherichia coli, Helicobacter pylori and Saccharomyces cerevisiae, and it seems to be a common key feature to all cellular organisms. In concordance with these observations, the cell can be considered a complex metabolic network which mainly integrates a large ensemble of self-organized multienzymatic complexes interconnected by substrate fluxes and regulatory signals, where multiple autonomous oscillatory and quasi-stationary catalytic patterns simultaneously emerge. The network adjusts the internal metabolic activities to the external change by means of flux plasticity and structural plasticity.Methodology/principal findingsIn order to research the systemic mechanisms involved in the regulation of the cellular enzymatic activity we have studied different catalytic activities of a dissipative metabolic network under different external stimuli. The emergent biochemical data have been analysed using statistical mechanic tools, studying some macroscopic properties such as the global information and the energy of the system. We have also obtained an equivalent Hopfield network using a Boltzmann machine. Our main result shows that the dissipative metabolic network can behave as an attractor metabolic network.Conclusions/significanceWe have found that the systemic enzymatic activities are governed by attractors with capacity to store functional metabolic patterns which can be correctly recovered from specific input stimuli. The network attractors regulate the catalytic patterns, modify the efficiency in the connection between the multienzymatic complexes, and stably retain these modifications. Here for the first time, we have introduced the general concept of attractor metabolic network, in which this dynamic behavior is observed.

Project description:Ensembles of principal neurons in the basolateral amygdala (BLA) generate the initial engrams for fear memories, while projections from the BLA to the medial prefrontal cortex (mPFC) are essential for the encoding, transfer and storage of remote fear memories. We tested the effects of chronic pain on remote fear memories in mice. Male mice underwent classic fear conditioning by pairing a single tone (conditional stimulus, CS) with a single electric foot shock (unconditional stimulus, US). Sciatic nerve constriction was used to induce neuropathic pain at various time points before or after the fear conditioning. The mice with sciatic nerve cuffs implanted 48 h after the fear conditioning showed an increased freezing response to CS when compared to mice without cuffs or when compared to mice in which the nerve cuffing was performed 48 h before the fear conditioning. The enhancing effect of pain on consolidated fear memory was further tested and mice in which the nerve cuffing was performed 14 days after the fear conditioning also showed an increased fear response when tested 56 days later. We used immunostaining to detect morphological changes in the BLA that could suggest a mechanism for the observed increase in fear response. We found an increased number of calbindin/parvalbumin positive neurons in the BLA and increased perisomatic density of GAD65 on projection neurons that connect BLA to mPFC in mice with nerve cuffs. Despite the strong increase of c-Fos expression in BLA and mPFC that was induced by fear recall, neither the BLA to mPFC nor the mPFC to BLA projection neurons were activated in mice with nerve cuffs. Furthermore, non-injured mice had an increased fear response when BLA to mPFC projections were inhibited by a chemogenetic method. In conclusion, this study provides evidence that persistent pain has a significant impact on consolidated fear memories. Very likely the underlying mechanism for this phenomenon is increased inhibitory input onto the BLA to mPFC projection neurons, possibly from neurons with induced parvalbumin expression. Conceivably, the increased fear response to consolidated fear memory is a harbinger for the later development of anxiety and depression symptoms associated with chronic pain.

Dataset Information

On stability and associative recall of memories in attractor neural networks.

Publications

On stability and associative recall of memories in attractor neural networks.

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