How to Knit Your Own Markov Blanket

Hohwy (Hohwy 2016, Hohwy 2017) argues there is a tension between the free energy principle and leading depictions of mind as embodied, enactive, and extended (so-called ‘EEE1 cognition’). The tension is traced to the importance, in free energy formulations, of a conception of mind and agency that depends upon the presence of a ‘Markov blanket’ demarcating the agent from the surrounding world. In what follows I show that the Markov blanket considerations do not, in fact, lead to the kinds of tension that Hohwy depicts. On the contrary, they actively favour the EEE story. This is because the Markov property, as exemplified in biological agents, picks out neither a unique nor a stationary boundary. It is this multiplicity and mutability– rather than the absence of agent-environment boundaries as such - that EEE cognition celebrates

Remote Sensing Signature Fields Reconstruction via Robust Regularization of Bayesian Minimum Risk Technique

The robust numerical technique for high-resolution reconstructive imaging and scene analysis is developed as required for enhanced remote sensing with large scale sensor array radar/synthetic aperture radar. The problem- oriented modification of the previously proposed fused Bayesian-regularization (FBR) enhanced radar imaging method is performed to enable it to reconstruct remote sensing signatures (RSS) of interest alleviating problem ill- poseness due to system-level and model-level uncertainties. We report some simulation results of hydrological RSS reconstruction from enhanced real-world environmental images indicative of the efficiency of the developed method.Cinvesta

Measuring category intuitiveness in unconstrained categorization tasks

What makes a category seem natural or intuitive? In this paper, an unsupervised categorization task was employed to examine observer agreement concerning the categorization of nine different stimulus sets. The stimulus sets were designed to capture different intuitions about classification structure. The main empirical index of category intuitiveness was the frequency of the preferred classification, for different stimulus sets. With 169 participants, and a within participants design, with some stimulus sets the most frequent classification was produced over 50 times and with others not more than two or three times. The main empirical finding was that cluster tightness was more important in determining category intuitiveness, than cluster separation. The results were considered in relation to the following models of unsupervised categorization: DIVA, the rational model, the simplicity model, SUSTAIN, an Unsupervised version of the Generalized Context Model (UGCM), and a simple geometric model based on similarity. DIVA, the geometric approach, SUSTAIN, and the UGCM provided good, though not perfect, fits. Overall, the present work highlights several theoretical and practical issues regarding unsupervised categorization and reveals weaknesses in some of the corresponding formal models

An exploratory study of imagining sounds and “hearing” music in autism

Individuals with autism spectrum disorder (ASD) reportedly possess preserved or superior music-processing skills compared to their typically developing counterparts. We examined auditory imagery and earworms (tunes that get “stuck” in the head) in adults with ASD and controls. Both groups completed a short earworm questionnaire together with the Bucknell Auditory Imagery Scale. Results showed poorer auditory imagery in the ASD group for all types of auditory imagery. However, the ASD group did not report fewer earworms than matched controls. These data suggest a possible basis in poor auditory imagery for poor prosody in ASD, but also highlight a separability between auditory imagery and control of musical memories. The separability is present in the ASD group but not in typically developing individuals

In search of dispersed memories: Generative diffusion models are associative memory networks

Uncovering the mechanisms behind long-term memory is one of the most fascinating open problems in neuroscience and artificial intelligence. Artificial associative memory networks have been used to formalize important aspects of biological memory. Generative diffusion models are a type of generative machine learning techniques that have shown great performance in many tasks. Like associative memory systems, these networks define a dynamical system that converges to a set of target states. In this work we show that generative diffusion models can be interpreted as energy-based models and that, when trained on discrete patterns, their energy function is (asymptotically) identical to that of modern Hopfield networks. This equivalence allows us to interpret the supervised training of diffusion models as a synaptic learning process that encodes the associative dynamics of a modern Hopfield network in the weight structure of a deep neural network. Leveraging this connection, we formulate a generalized framework for understanding the formation of long-term memory, where creative generation and memory recall can be seen as parts of a unified continuum

Aggregation of Descriptive Regularization Methods with Hardware/Software Co-Design for Remote Sensing Imaging

This study consider the problem of high-resolution imaging of the remote sensing (RS) environment formalized in terms of a nonlinear ill- posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the wavefield scattered from an extended remotely sensed scene (referred to as the scene image). However, the remote sensing techniques for reconstructive imaging in many RS application areas are relatively unacceptable for being implemented in a (near) real time implementation. In this work, we address a new aggregated descriptive-regularization (DR) method and the Hardware/Software (HW/SW) co-design for the SSP reconstruction from the uncertain speckle-corrupted measurement data in a computationally efficient parallel fashion that meets the (near) real time image processing requirements. The hardware design is performed via efficient systolic arrays (SAs). Finally, the efficiency both in resolution enhancement and in computational complexity reduction metrics of the aggregated descriptive-regularized and the HW/SW co-design method is presented via numerical simulations and by the performance analysis of the implementation based on a Xilinx Field Programmable Gate Array (FPGA) XC4VSX35-10ff668.Universidad de GuadalajaraUniversidad Autónoma de YucatánInstituto Tecnológico de Mérid

Form, Qualia and Time: The Hard Problem Reformed

The hard problem – focusing essentially on vision here – is in fact the problem of the origin of our image of the external world. This formulation in terms of the “image” is never seen stated, for the forms populating our image of the world are considered computable, and not considered qualia – the “redness” of the cube is the problem, not the cube as form. Form, however, cannot be divorced from motion and hence from time. Therefore we must examine the classical, spatial metaphysic of space and time, for practical purposes initiated by Galileo, wherein the real has been equated with the quantitative and wherein quality has been stripped from the material world. In this metaphysic, which sees form as quantitative or computable, the origin of qualia is problematic, with a problem of even greater primacy becoming the “memory” that supports the transforming events of perception, e.g., rotating cubes, buzzing flies, twisting leaves. It is this memory, supporting time-extended, flowing events, that necessarily supports all qualia. The concept of storage of “snapshots” of time-flowing events, a notion which the classic metaphysic engenders, is unworkable as a solution to the perception of these flows. Form, in fact being dynamic and defined over flowing fields, equally is a quality, equally requires this memory, and since forms populate the image, the origin of the entire image is indeed a problem. The counter-proposal becomes Bergson’s temporal metaphysic wherein motion is indivisible (or non-differentiable), the global motion of the universal field itself then carrying an intrinsic form of memory. In this framework, with this field viewed as holographic, Bergson provides a unique solution – one that leaves the problem of representation behind – as to how the brain specifies the qualitative image of the dynamically transforming external world