arXiv subjects

Francisco B. Rodriguez

Publications and source records attributed to Francisco B. Rodriguez.

7 recordsLinked to original sources

arXiv2026

Real-time closed-loop protocol to assess neural variability in temporal coding

Understanding temporal coding in neural systems is essential for decoding brain communication and advancing knowledge of neural information processing. Neural activity often conveys information through spike sequences with stereotypical temporal structures linked to specific functions. However, these sequences are subject to variability introduced by neural dynamics. Real-time closed-loop stimulation is a powerful approach to study temporal coding through adaptive control. In this work, we evaluate how a closed-loop protocol adapts to this variability to drive neural dynamics toward a desired state. It computes the Victor-Purpura distance to quantify similarity between spike sequences generated by the neural system and a triggering pattern. If the protocol determines that a neural sequence is similar to the trigger pattern, it applies stimulation to the system. This allows for an analysis of whether the system's responses are consistent and facilitates the identification of varying spike sequences that can be considered instances of the same functional temporal code. We designed two validation experiments using the Hindmarsh-Rose model: (i) detection of a temporal code and delivery of stimulation to produce brief interspersed bursts, and (ii) detection of burst onset in chaotic activity followed by inhibitory stimulation to regularize it. Gaussian noise was progressively injected to increase variability. The protocol exhibited high degree of adaptability to variability and was effective in achieving the target dynamics. The results reported in this paper suggest that adaptive closed-loop stimulation can enhance experimental methodologies for studying neural coding under realistic variability conditions.

q-bio.NC↗

arXiv2014

Fair anonymity for the Tor network

Current anonymizing networks have become an important tool for guaranteeing users' privacy. However, these platforms can be used to perform illegitimate actions, which sometimes makes service providers see traffic coming from these networks as a probable threat. In order to solve this problem, we propose to add support for fairness mechanisms to the Tor network. Specifically, by introducing a slight modification to the key negotiation process with the entry and exit nodes, in the shape of group signatures. By means of these signatures, we set up an access control method to prevent misbehaving users to make use of the Tor network. Additionally, we establish a predefined method for denouncing illegitimate actions, which impedes the application of the proposed fairness mechanisms as a threat eroding users' privacy. As a direct consequence, traffic coming from Tor would be considered less suspicious by service providers.

cs.CR↗

arXiv2012

Formal security analysis of registration protocols for interactive systems: a methodology and a case of study

In this work we present and formally analyze CHAT-SRP (CHAos based Tickets-Secure Registration Protocol), a protocol to provide interactive and collaborative platforms with a cryptographically robust solution to classical security issues. Namely, we focus on the secrecy and authenticity properties while keeping a high usability. In this sense, users are forced to blindly trust the system administrators and developers. Moreover, as far as we know, the use of formal methodologies for the verification of security properties of communication protocols isn't yet a common practice. We propose here a methodology to fill this gap, i.e., to analyse both the security of the proposed protocol and the pertinence of the underlying premises. In this concern, we propose the definition and formal evaluation of a protocol for the distribution of digital identities. Once distributed, these identities can be used to verify integrity and source of information. We base our security analysis on tools for automatic verification of security protocols widely accepted by the scientific community, and on the principles they are based upon. In addition, it is assumed perfect cryptographic primitives in order to focus the analysis on the exchange of protocol messages. The main property of our protocol is the incorporation of tickets, created using digests of chaos based nonces (numbers used only once) and users' personal data. Combined with a multichannel authentication scheme with some previous knowledge, these tickets provide security during the whole protocol by univocally linking each registering user with a single request. [..]

cs.CR↗

arXiv2012

A formal methodology for integral security design and verification of network protocols

We propose a methodology for verifying security properties of network protocols at design level. It can be separated in two main parts: context and requirements analysis and informal verification; and formal representation and procedural verification. It is an iterative process where the early steps are simpler than the last ones. Therefore, the effort required for detecting flaws is proportional to the complexity of the associated attack. Thus, we avoid wasting valuable resources for simple flaws that can be detected early in the verification process. In order to illustrate the advantages provided by our methodology, we also analyze three real protocols.

cs.CR↗

arXiv2005

Accurate and robust image superresolution by neural processing of local image representations

Image superresolution involves the processing of an image sequence to generate a still image with higher resolution. Classical approaches, such as bayesian MAP methods, require iterative minimization procedures, with high computational costs. Recently, the authors proposed a method to tackle this problem, based on the use of a hybrid MLP-PNN architecture. In this paper, we present a novel superresolution method, based on an evolution of this concept, to incorporate the use of local image models. A neural processing stage receives as input the value of model coefficients on local windows. The data dimensionality is firstly reduced by application of PCA. An MLP, trained on synthetic sequences with various amounts of noise, estimates the high-resolution image data. The effect of varying the dimension of the network input space is examined, showing a complex, structured behavior. Quantitative results are presented showing the accuracy and robustness of the proposed method.

cs.CV↗

arXiv2005

Dynamical Neural Network: Information and Topology

A neural network works as an associative memory device if it has large storage capacity and the quality of the retrieval is good enough. The learning and attractor abilities of the network both can be measured by the mutual information (MI), between patterns and retrieval states. This paper deals with a search for an optimal topology, of a Hebb network, in the sense of the maximal MI. We use small-world topology. The connectivity $γ$ ranges from an extremely diluted to the fully connected network; the randomness $ω$ ranges from purely local to completely random neighbors. It is found that, while stability implies an optimal $MI(γ,ω)$ at $γ_{opt}(ω)\to 0$, for the dynamics, the optimal topology holds at certain $γ_{opt}>0$ whenever $0\leqω<0.3$.

cs.IR↗

arXiv2005

A hybrid MLP-PNN architecture for fast image superresolution

Image superresolution methods process an input image sequence of a scene to obtain a still image with increased resolution. Classical approaches to this problem involve complex iterative minimization procedures, typically with high computational costs. In this paper is proposed a novel algorithm for super-resolution that enables a substantial decrease in computer load. First, a probabilistic neural network architecture is used to perform a scattered-point interpolation of the image sequence data. The network kernel function is optimally determined for this problem by a multi-layer perceptron trained on synthetic data. Network parameters dependence on sequence noise level is quantitatively analyzed. This super-sampled image is spatially filtered to correct finite pixel size effects, to yield the final high-resolution estimate. Results on a real outdoor sequence are presented, showing the quality of the proposed method.

cs.CV↗