Synaptic compensation on Hopfield network: implications for memory rehabilitation

Menezes, Raimundo; Monteiro, Luiz Henrique Alves

doi:10.1007/s00521-010-0480-7

Cited by 4 publications

(2 citation statements)

References 31 publications

(33 reference statements)

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“…Due to its simplicity and popularity, we found the Hopfield network model to ideally suited for first investigations of established single cell models for memory impairment [Maia, Kutz(2014a), Maia, Kutz(2014b), Maia et al(2015)] at the level of neuronal dynamics. The rich body of knowledge that has been developed for the Hopfield model serves as an ideal basis for this study and allows for an embedding of our results in existing theories [Anafi, Bates(2010), Menezes, Monteiro(2011), Ruppin, Reggia(2011)].…”

Section: Introductionmentioning

confidence: 99%

Associative Memory Impairments arising from Neurodegenerative Diseases and Traumatic Brain Injuries in a Hopfield Network Model

Weber,

Maia,

Kutz

2016

Preprint

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Neurodegenerative diseases and traumatic brain injuries (TBI) are among the main causes of cognitive dysfunction in humans. Both manifestations exhibit the extensive presence of focal axonal swellings (FAS). FAS compromises the information encoded in spike trains, thus leading to potentially severe functional deficits. Complicating our understanding of the impact of FAS is our inability to access small scale injuries with noninvasive methods, the overall complexity of neuronal pathologies, and our limited knowledge of how networks process biological signals. Building on Hopfield's pioneering work, we extend a model for associative memory to account for FAS and its impact on memory encoding. We calibrate all FAS parameters from biophysical observations of their statistical distribution and size, providing a framework to simulate the effects of brain disorders on memory recall performance. A face recognition example is used to demonstrate and validate the functionality of the novel model. Our results link memory recall ability to observed FAS statistics, allowing for a description of different stages of brain disorders within neuronal networks. This provides a first theoretical model to bridge experimental observations of FAS in neurodegeneration and TBI with compromised memory recall, thus closing the large gap between theory and experiment on how biological signals are processed in damaged, high-dimensional functional networks. The work further lends new insight into positing diagnostic tools to measure cognitive deficits.

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Section: Introductionmentioning

confidence: 99%

Associative Memory Impairments arising from Neurodegenerative Diseases and Traumatic Brain Injuries in a Hopfield Network Model

Weber,

Maia,

Kutz

2016

Preprint

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“…Esta questão foi impulsionada por avanços tecnológicos que permitiram a obtenção experimental do sinal neuronal em variadas situações, estabelecendo uma base para diversos modelos quantitativos encontrados na literatura (KOCH; SEGEV, 1998;STERRATT et al, 2011). Tais modelos têm sido amplamente empregados na solução de problemas computacionais (GAO et al, 2012;MENEZES;MONTEIRO, 2011) e para investigar o funcionamento de sistemas neuronais (MONTEIRO; BUSSAB; CHAUI-…”

Section: Introductionunclassified

Modelagem de sinais neuronais utilizando filtros lineares de tempo discreto.

Palmieri¹

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A aquisição experimental de sinais neuronais é um dos principais avanços da neurociência. Por meio de observações da corrente e do potencial elétricos em uma região cerebral, é possível entender os processos fisiológicos envolvidos na geração do potencial de ação, e produzir modelos matemáticos capazes de simular o comportamento de uma célula neuronal. Uma prática comum nesse tipo de experimento é obter leituras a partir de um arranjo de eletrodos posicionado em um meio compartilhado por diversos neurônios, o que resulta em uma mistura de sinais neuronais em uma mesma série temporal. Este trabalho propõe um modelo linear de tempo discreto para o sinal produzido durante o disparo do neurônio. Os coeficientes desse modelo são calculados utilizando-se amostras reais dos sinais neuronais obtidas in vivo. O processo de modelagem concebido emprega técnicas de identificação de sistemas e processamento de sinais, e é dissociado de considerações sobre o funcionamento biofísico da célula, fornecendo uma alternativa de baixa complexidade para a modelagem do disparo neuronal. Além disso, a representação por meio de sistemas lineares permite idealizar um sistema inverso, cuja função é recuperar o sinal original de cada neurônio ativo em uma mistura extracelular. Nesse contexto, são discutidas algumas soluções baseadas em filtros adaptativos para a simulação do sistema inverso, introduzindo uma nova abordagem para o problema de separação de spikes neuronais. AbstractThe experimental acquisition of neuronal signals is a major advance in neuroscience.Through observations of electric current and potential in a brain region, it is possible to understand the physiological processes involved in the action potential generation, and create mathematical models capable of simulating the behavior of the neuronal cell.A common practice in this kind of experiment is to obtain readings from an array of electrodes positioned in a medium shared by several neurons, which results in a mixture of neuronal signals in the same time series. This work proposes a discrete-time linear model of the neuronal signal during the firing of the cell. The coefficients of this model are estimated using real samples of the neuronal signals obtained in vivo. The conceived modeling process employs system identification and signal processing concepts, and is dissociated from any considerations about the biophysical function of the neuronal cell, providing a low-complexity alternative to model the neuronal spike. In addition, the use of a linear representation allows the idealization of an inverse system, whose main purpose is to recover the original signal of each active neuron in a given extracellular mixture. In this context, some solutions based on adaptive filters are discussed for the inverse model simulation, introducing a new approach to the problem of neuronal spike separation.

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A biologically plausible network model for pattern storage and recall inspired by Dentate Gyrus

Janarthanam

Vishwanath

Shanthi

2020

Neural Comput & Applic

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Synaptic compensation on Hopfield network: implications for memory rehabilitation

Cited by 4 publications

References 31 publications

Associative Memory Impairments arising from Neurodegenerative Diseases and Traumatic Brain Injuries in a Hopfield Network Model

Associative Memory Impairments arising from Neurodegenerative Diseases and Traumatic Brain Injuries in a Hopfield Network Model

Modelagem de sinais neuronais utilizando filtros lineares de tempo discreto.

A biologically plausible network model for pattern storage and recall inspired by Dentate Gyrus

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