Towards a common theory of explanation for artificial and biological intelligence

Much of the confusion that occurs when working at the intersection of cognitive science, artificial intelligence, and neuroscience stems from disagreement about what it means to explain intelligence. I claim that to integrate these fields, we must reconcile their different theories of explanation. I briefly review theories of scientific explanation in neuroscience and recontextualize the stated views of several prominent cognitive computational neuroscientists in terms of the theories of explanation they espouse. Finally, I describe some of the challenges of forging a new theory of explanation that would apply equally to artificial and biological intelligence. As a first step towards an integration of research on biological and artificial intelligence, my goal in writing this paper is to equip scientists of intelligence to interrogate and justify the theories of explanation that underlie their definitions of scientific progress

Towards a common theory of explanation for artificial and biological intelligence

Much of the confusion that occurs when working at the intersection of cognitive science, artificial intelligence, and neuroscience stems from disagreement about what it means to explain intelligence. I claim that to integrate these fields, we must reconcile their different theories of explanation. I briefly review theories of scientific explanation in neuroscience and recontextualize the stated views of several prominent cognitive computational neuroscientists in terms of the theories of explanation they espouse. Finally, I describe some of the challenges of forging a new theory of explanation that would apply equally to artificial and biological intelligence. As a first step towards an integration of research on biological and artificial intelligence, my goal in writing this paper is to equip scientists of intelligence to interrogate and justify the theories of explanation that underlie their definitions of scientific progress

Towards a common theory of explanation for artificial and biological intelligence

Much of the confusion that occurs when working at the intersection of cognitive science, artificial intelligence, and neuroscience stems from disagreement about what it means to explain intelligence. I claim that to integrate these fields, we must reconcile their different theories of explanation. I briefly review theories of scientific explanation in neuroscience and recontextualize the stated views of several prominent cognitive computational neuroscientists in terms of the theories of explanation they espouse. Finally, I describe some of the challenges of forging a new theory of explanation that would apply equally to artificial and biological intelligence. As a first step towards an integration of research on biological and artificial intelligence, my goal in writing this paper is to equip scientists of intelligence to interrogate and justify the theories of explanation that underlie their definitions of scientific progress

Towards a common theory of explanation for artificial and biological intelligence

Much of the confusion that occurs when working at the intersection of cognitive science, artificial intelligence, and neuroscience stems from disagreement about what it means to explain intelligence. I claim that to integrate these fields, we must reconcile their different theories of explanation. I briefly review theories of scientific explanation in neuroscience and recontextualize the stated views of several prominent cognitive computational neuroscientists in terms of the theories of explanation they espouse. Finally, I describe some of the challenges of forging a new theory of explanation that would apply equally to artificial and biological intelligence. As a first step towards an integration of research on biological and artificial intelligence, my goal in writing this paper is to equip scientists of intelligence to interrogate and justify the theories of explanation that underlie their definitions of scientific progress

Characterizing and comparing acoustic representations in convolutional neural networks and the human auditory system

Le traitement auditif dans le cerveau humain et dans les systèmes informatiques consiste en une cascade de transformations représentationnelles qui extraient et réorganisent les informations pertinentes pour permettre l'exécution des tâches. Cette thèse s'intéresse à la nature des représentations acoustiques et aux principes de conception et d'apprentissage qui soutiennent leur développement. Les objectifs scientifiques sont de caractériser et de comparer les représentations auditives dans les réseaux de neurones convolutionnels profonds (CNN) et la voie auditive humaine. Ce travail soulève plusieurs questions méta-scientifiques sur la nature du progrès scientifique, qui sont également considérées. L'introduction passe en revue les connaissances actuelles sur la voie auditive des mammifères et présente les concepts pertinents de l'apprentissage profond. Le premier article soutient que les questions philosophiques les plus pressantes à l'intersection de l'intelligence artificielle et biologique concernent finalement la définition des phénomènes à expliquer et ce qui constitue des explications valables de tels phénomènes. Je surligne les théories pertinentes de l'explication scientifique que j’espére fourniront un échafaudage pour de futures discussions. L'article 2 teste un modèle populaire de cortex auditif basé sur des modulations spectro-temporelles. Nous constatons qu'un modèle linéaire entraîné uniquement sur les réponses BOLD aux ondulations dynamiques simples (contenant seulement une fréquence fondamentale, un taux de modulation temporelle et une échelle spectrale) peut se généraliser pour prédire les réponses aux mélanges de deux ondulations dynamiques. Le troisième article caractérise la spécificité linguistique des couches CNN et explore l'effet de l'entraînement figé et des poids aléatoires. Nous avons observé trois régions distinctes de transférabilité: (1) les deux premières couches étaient entièrement transférables, (2) les couches 2 à 8 étaient également hautement transférables, mais nous avons trouvé évidence de spécificité de la langue, (3) les couches suivantes entièrement connectées étaient plus spécifiques à la langue mais pouvaient être adaptées sur la langue cible. Dans l'article 4, nous utilisons l'analyse de similarité pour constater que la performance supérieure de l'entraînement figé obtenues à l'article 3 peuvent être attribuées aux différences de représentation dans l'avant-dernière couche: la deuxième couche entièrement connectée. Nous analysons également les réseaux aléatoires de l'article 3, dont nous concluons que la forme représentationnelle est doublement contrainte par l'architecture et la forme de l'entrée et de la cible. Pour tester si les CNN acoustiques apprennent une hiérarchie de représentation similaire à celle du système auditif humain, le cinquième article compare l'activité des réseaux «freeze trained» de l'article 3 à l'activité IRMf 7T dans l'ensemble du système auditif humain. Nous ne trouvons aucune évidence d'une hiérarchie de représentation partagée et constatons plutôt que tous nos régions auditifs étaient les plus similaires à la première couche entièrement connectée. Enfin, le chapitre de discussion passe en revue les mérites et les limites d'une approche d'apprentissage profond aux neurosciences dans un cadre de comparaison de modèles. Ensemble, ces travaux contribuent à l'entreprise naissante de modélisation du système auditif avec des réseaux de neurones et constituent un petit pas vers une science unifiée de l'intelligence qui étudie les phénomènes qui se manifestent dans l'intelligence biologique et artificielle.Auditory processing in the human brain and in contemporary machine hearing systems consists of a cascade of representational transformations that extract and reorganize relevant information to enable task performance. This thesis is concerned with the nature of acoustic representations and the network design and learning principles that support their development. The primary scientific goals are to characterize and compare auditory representations in deep convolutional neural networks (CNNs) and the human auditory pathway. This work prompts several meta-scientific questions about the nature of scientific progress, which are also considered. The introduction reviews what is currently known about the mammalian auditory pathway and introduces the relevant concepts in deep learning.The first article argues that the most pressing philosophical questions at the intersection of artificial and biological intelligence are ultimately concerned with defining the phenomena to be explained and with what constitute valid explanations of such phenomena. I highlight relevant theories of scientific explanation which we hope will provide scaffolding for future discussion. Article 2 tests a popular model of auditory cortex based on frequency-specific spectrotemporal modulations. We find that a linear model trained only on BOLD responses to simple dynamic ripples (containing only one fundamental frequency, temporal modulation rate, and spectral scale) can generalize to predict responses to mixtures of two dynamic ripples. Both the third and fourth article investigate how CNN representations are affected by various aspects of training. The third article characterizes the language specificity of CNN layers and explores the effect of freeze training and random weights. We observed three distinct regions of transferability: (1) the first two layers were entirely transferable between languages, (2) layers 2--8 were also highly transferable but we found some evidence of language specificity, (3) the subsequent fully connected layers were more language specific but could be successfully finetuned to the target language. In Article 4, we use similarity analysis to find that the superior performance of freeze training achieved in Article 3 can be largely attributed to representational differences in the penultimate layer: the second fully connected layer. We also analyze the random networks from Article 3, from which we conclude that representational form is doubly constrained by architecture and the form of the input and target. To test whether acoustic CNNs learn a similar representational hierarchy as that of the human auditory system, the fifth article presents a similarity analysis to compare the activity of the freeze trained networks from Article 3 to 7T fMRI activity throughout the human auditory system. We find no evidence of a shared representational hierarchy and instead find that all of our auditory regions were most similar to the first fully connected layer. Finally, the discussion chapter reviews the merits and limitations of a deep learning approach to neuroscience in a model comparison framework. Together, these works contribute to the nascent enterprise of modeling the auditory system with neural networks and constitute a small step towards a unified science of intelligence that studies the phenomena that are exhibited in both biological and artificial intelligence

The effect of task and training on intermediate representations in convolutional neural networks revealed with modified RV similarity analysis

Centered Kernel Alignment (CKA) was recently proposed as a similarity metric for comparing activation patterns in deep networks. Here we experiment with the modified RV-coefficient (RV2), which has very similar properties as CKA while being less sensitive to dataset size. We compare the representations of networks that received varying amounts of training on different layers: a standard trained network (all parameters updated at every step), a freeze trained network (layers gradually frozen during training), random networks (only some layers trained), and a completely untrained network. We found that RV2 was able to recover expected similarity patterns and provide interpretable similarity matrices that suggested hypotheses about how representations are affected by different training recipes. We propose that the superior performance achieved by freeze training can be attributed to representational differences in the penultimate layer. Our comparisons of random networks suggest that the inputs and targets serve as anchors on the representations in the lowest and highest layers.Comment: 4 pages, 4 figures, Conference on Cognitive Computational Neuroscience 201

Convective and Wave Signatures in Ozone Profiles Over the Equatorial Americas: Views from TC4 (2007) and SHADOZ

During the months of July-August 2007 NASA conducted a research campaign called the Tropical Composition, Clouds and Climate Coupling (TC4) experiment. Vertical profiles of ozone were measured daily using an instrument known as an ozonesonde, which is attached to a weather balloon and launch to altitudes in excess of 30 km. These ozone profiles were measured over coastal Las Tablas, Panama (7.8N, 80W) and several times per week at Alajuela, Costa Rica (ION, 84W). Meteorological systems in the form of waves, detected most prominently in 100- 300 in thick ozone layer in the tropical tropopause layer, occurred in 50% (Las Tablas) and 40% (Alajuela) of the soundings. These layers, associated with vertical displacements and classified as gravity waves ("GW," possibly Kelvin waves), occur with similar stricture and frequency over the Paramaribo (5.8N, 55W) and San Cristobal (0.925, 90W) sites of the Southern Hemisphere Additional Ozonesondes (SHADOZ) network. The gravity wave labeled layers in individual soundings correspond to cloud outflow as indicated by the tracers measured from the NASA DC-8 and other aircraft data, confirming convective initiation of equatorial waves. Layers representing quasi-horizontal displacements, referred to as Rossby waves, are robust features in soundings from 23 July to 5 August. The features associated with Rossby waves correspond to extra-tropical influence, possibly stratospheric, and sometimes to pollution transport. Comparison of Las Tablas and Alajuela ozone budgets with 1999-2007 Paramaribo and San Cristobal soundings shows that TC4 is typical of climatology for the equatorial Americas. Overall during TC4, convection and associated meteorological waves appear to dominate ozone transport in the tropical tropopause layer

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke

The goal of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well‐powered meta‐ and mega‐analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large‐scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided

Using human genetics to understand the disease impacts of testosterone in men and women.

Testosterone supplementation is commonly used for its effects on sexual function, bone health and body composition, yet its effects on disease outcomes are unknown. To better understand this, we identified genetic determinants of testosterone levels and related sex hormone traits in 425,097 UK Biobank study participants. Using 2,571 genome-wide significant associations, we demonstrate that the genetic determinants of testosterone levels are substantially different between sexes and that genetically higher testosterone is harmful for metabolic diseases in women but beneficial in men. For example, a genetically determined 1 s.d. higher testosterone increases the risks of type 2 diabetes (odds ratio (OR) = 1.37 (95% confidence interval (95% CI): 1.22-1.53)) and polycystic ovary syndrome (OR = 1.51 (95% CI: 1.33-1.72)) in women, but reduces type 2 diabetes risk in men (OR = 0.86 (95% CI: 0.76-0.98)). We also show adverse effects of higher testosterone on breast and endometrial cancers in women and prostate cancer in men. Our findings provide insights into the disease impacts of testosterone and highlight the importance of sex-specific genetic analyses.A.R.W. and T.M.F. are supported by the European Research Council grant: SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC. R.B. is funded by the Wellcome Trust and Royal Society grant 104150/Z/14/Z. J.T. is supported by the Academy of Medical Sciences Springboard award which is supported by the Wellcome Trust and GCRF [SBF004\1079]. This work was supported by the Medical Research Council [Unit Programme numbers MC_UU_12015/1 and MC_UU_12015/2]