Neural dynamics based on the recognition of neural fingerprints

Carrillo-Medina, José Luis; Latorre, Roberto

doi:10.3389/fncom.2015.00033

Cited by 6 publications

(5 citation statements)

References 40 publications

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“…To achieve this, we impose two constraints (Wiedemann and Lüthi, 2003; Tabak et al, 2010): (i) predominance of excitatory synapses and (ii) a refractory period in each neuron following hyperexcitation. Information processing in the second dimension is based on the emission and recognition of specific neural signatures (Tristán et al, 2004; Carrillo-Medina and Latorre, 2015), i.e., information in this dimension propagates encoded in a spike-timing modality. An intra-unit contextualization mechanism drives the signature emission and recognition processes.…”

Section: Models and Methodsmentioning

confidence: 99%

“…The spike-timing encoding is related to the spreading of specific intraburst spike patterns through the network and the synchronization mechanisms that allow a group of neurons to recognize and emit the same signature at a given moment (Tristán et al, 2004; Carrillo-Medina and Latorre, 2015). To graphically illustrate the dynamic spatial organization of the spike patterns within the network, we generate activity movies representing the fingerprint-based evolving dynamics (e.g., see Figure 5 ).…”

Section: Models and Methodsmentioning

confidence: 99%

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Implementing Signature Neural Networks with Spiking Neurons

Carrillo-Medina

Latorre

2016

Front. Comput. Neurosci.

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Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm—i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data—to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence of inhibitory connections. These parameters also modulate the memory capabilities of the network. The dynamical modes observed in the different informational dimensions in a given moment are independent and they only depend on the parameters shaping the information processing in this dimension. In view of these results, we argue that plasticity mechanisms inside individual cells and multicoding strategies can provide additional computational properties to spiking neural networks, which could enhance their capacity and performance in a wide variety of real-world tasks.

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Section: Models and Methodsmentioning

confidence: 99%

Section: Models and Methodsmentioning

confidence: 99%

Implementing Signature Neural Networks with Spiking Neurons

Carrillo-Medina

Latorre

2016

Front. Comput. Neurosci.

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“…La aplicación de diferentes tecnologías ha permitido el cuidado de la piel, podemos mencionar la tele dermatología, se ha convertido en un servicio de salud, el cual realiza un diagnóstico dermatológico en zonas rurales para personas que sufren diferentes patologías en la piel y su posterior cuidado [17]. Sin embargo, el desconocimiento de este tipo de tecnologías, no ha permitido su adaptación e implementación en los centros de salud u hogares de las personas con problemas dermatológicos.…”

Section: Conclusionesunclassified

“…Existen estudios sobre el melanoma y la detección de lesiones cutáneas utilizando una dermatoscopia digital de teléfono inteligente de aplicaciones donde se puede analizar imágenes de alta resolución de las lesiones de la piel y proporciona al usuario información sobre la apariencia de malignidad, también se estudia las aplicaciones móviles (teléfonos inteligentes), imágenes multiespectrales (MoleMate y MelaFi) [14,15]. Adicional se ha considerado un sistema de soporte de gestión del cuidado de la piel basado en la computación en la nube, se propone un sistema de apoyo para la gestión del cuidado de la piel con un método de interacción, aprovechando un mecanismo de sincronización de datos permitiendo que los pacientes y el personal médico se visualicen simultáneamente [16,17].…”

Section: Introductionunclassified

Analysis of mobile applications that help skin care through facial recognition: A systematic analysis

Ruben

Luis

Espinosa

et al. 2020

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The knowledge and integration of new technologies has shown that it is possible to improve access to health services, such as skin care and diagnosis of pathologies in the area of dermatology, applying mobile technologies and ap- plications. In this systematic review it seeks to investigate mobile applications that use intelligent systems that help skin care through facial recognition. The investigation was carried out between 2010 and 2018, finding 168 articles that resemble the subject; that were reduced to 28 articles with available text, after a dissertion of articles that did not contribute to the research as well as repeated. Finally, three of them were chosen to begin the topic of study. The present review allows us to conclude that there is a guideline for using mobile applications and technologies for the care and diagnosis of pathologies on the skin by means of algorithms, models, techniques and methods applying intelligent systems. How- ever, the integration of mobile applications directly for skin care is just beginning the research field, but offers promising processes and results. Resumen. El conocimiento e integración de nuevas tecnologías, ha demostrado que se puede mejorar el acceso a servicios de la salud, como es al cuidado de la piel y diagnóstico de patologías en el área de la dermatología, aplicando tecnologías y aplicaciones móviles. En esta revisión sistemática busca indagar aplicaciones móviles que usan sistemas inteligentes que ayuden al cuidado de la piel mediante reconocimiento facial. La indagación fue realizada entre 2010 y 2018, encontrando 168 artículos que se asemejan al tema; que se redujeron a 28 artículos con texto disponible, luego de una dicersion de artículos que no aportaban a la investigación como también repetidos. Finalmente, tres de ellos se eligió para empezar el tema de estudio. La presente revisión permite concluir que hay una directriz para usar aplicaciones y tecnologías móviles para el cuidado y el diagnostico de patologías sobre la piel mediante algoritmos, modelos, técnicas y métodos aplicando sistemas inteligentes. Sin embargo, la integración de aplicaciones móviles directamente para elcuidado de la piel, apenas inicia el campo de investigación, pero ofrece procesos y resultados prometedores.

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“…The proposed simple network is able to generate complex collective dynamics [ 6 ]. Without stimuli, the network evolves to a stationary state, related to the emission of the spontaneous activity.…”

mentioning

confidence: 99%