Lifelong Learning, Abstraction and Generalization
Hava Siegelmann
University of Massachusetts Amherst, U.S.A.

Lifelong Learning is the cutting edge of artificial intelligence - encompassing computational
methods that allow systems to learn in runtime and incorporate learning for application in new,unanticipated situations. Until recently, this sort of computation has been found exclusively innature; thus, Lifelong Learning looks to nature for its underlying principles and mechanisms andthen transfer them to this new technology.
In this talk we will focus on the capability to abstract and generalize. We identified a property of
the human connectome that leads to the capability of abstraction. The analytic results identified apreviously unrecognized brain connectome hierarchy, shedding light on the role of brain structure
and processing pathways in creating varying levels of cognition and leading ultimately to abstract
thought [Scientific Reports 2015]. We will then translate the biological findings to mathematics
and use this formulation as a measure of generalization in deep networks, proposing it as a toolto grade networks by their capability to generalize [ICML 2020].
We will also mention a recent result [Nature Communication 2020] that characterize brain’s“rehearsal,” a mechanism which reorganizes memories with new learning, and apply this newmechanism, “generative replay,” to neural networks to enable generalization of information
attained via continual learning.

The new science of thought imaging:
Using machine learning to break the brain’s code for representing concepts
Marcel Adam Just
Carnegie Mellon University, U.S.A.

Recent computational techniques, particularly machine learning, are being applied to fMRI brain
imaging data, making it possible for the first time to relate patterns of brain activity to specificthoughts. Our early work focused on the identification of the neural signatures of individual
concrete concepts, like the thought of an apple or a hammer. It progressed to identifying manyother types of concepts, such as emotions, abstract concepts, and sentences.
One application of this approach is to instruction in science courses, and another is toneuropsychiatry, where it has been possible to identify suicidal ideation in terms of alterations ofa normative pattern of concept representation.

Linking Dimensions of Psychopathology and Connectivity in Functional Brain
Networks
Cedric Huchuan Xia
University of Pennsylvania, U.S.A.

Neurobiological abnormalities associated with psychiatric disorders do not map well to existing
diagnostic categories. High co-morbidity suggests dimensional circuit-level abnormalities thatcross diagnoses. Here we seek to identify brain-based dimensions of psychopathology using
sparse canonical correlation analysis in a large sample of youths. This analysis reveals correlated
patterns of functional connectivity and psychiatric symptoms. We find that four dimensions ofpsychopathology – mood, psychosis, fear, and externalizing behavior – are associated with
distinct patterns of connectivity. Loss of network segregation between the default mode networkand executive networks emerges as a common feature across all dimensions. These resultsdelineate connectivity-guided dimensions of psychopathology that cross clinical diagnostic
categories, which could serve as a foundation for developing network-based biomarkers inpsychiatry.

Neural representations of visually evoked emotional experience
感情体験の脳情報表現
Tomoyasu Horikawa
堀川 友慈
Advanced Telecommunications Research Institute International (ATR), Japan
国際電気通信基礎技術研究所

Our experiences are colored by a wide variety of emotions. Conventional emotion studies havecharacterized emotional experiences and neural responses based on a limited set of emotion
categories (e.g., basic emotions) or affective dimensions (e.g., valence and arousal). Recent
behavioral work by Cowen and Keltner (2017) analyzed reported emotional states (34 emotion
categories and 14 affective dimensions) elicited by 2,185 short videos and identified 27 distinct
varieties of experiences described in a high dimensional space. They also found that emotion
categories rather than affective dimensions organize self-reports of subjective experience. In thistalk, I will present the results of several analyses seeking to identify the neural substrates of thehigh-dimensional space of emotional experience using fMRI responses to all 2,185 videos. Our
analyses demonstrated that (1) dozens of video-evoked emotions were accurately predicted fromfMRI patterns in multiple brain regions with different regional configurations for individualemotions; (2) emotion categories better predicted cortical and subcortical responses than affective
dimensions, outperforming visual and semantic covariates in transmodal regions; and (3)emotion-related fMRI responses had a cluster-like organization efficiently characterized by
distinct categories. These results support an emerging theory of the high-dimensional emotion
space, illustrating the feasibility and importance to characterize neural responses associated with
our emotional experiences using richer and nuanced emotion categories.

Visualization of brain representation of diverse cognitive functions using encoding
modeling
エンコーディングモデルによる多様な認知機能の脳内表現の可視化Tomoya Nakai
中井 智也
Center for Information and Neural Networks (CiNet), Japan
脳情報通信融合研究センター

One of the central issues in cognitive neuroscience is to reveal how our daily cognitive processes
are represented in the brain. Previous studies have revealed the cortical representation ofperceptual experiences using encoding and decoding model approaches, while few attempts havebeen made to build a quantitative model for the cortical representation of active cognitiveprocesses. We performed fMRI experiments with over 100 naturalistic tasks and examined
cortical representations with two voxel-wise encoding models. A sparse task-type model revealeda hierarchical organization and cortical mapping of over 100 tasks. A metadata-based cognitive
factor model predicts brain activity and decodes tasks, even under novel conditions. Our
framework provides a powerful step toward the comprehensive modeling of the representations
underlying human cognition.

Neuroscience revealing latent relationships among cognitive functions
神経科学的方法によって認知機能同士の隠れた関係性を明らかにする
Hiroki Kurashige
倉重 宏樹
Tokai University, Japan東海大学

To look closer at each cognitive function and to consider the brain as a network of these functions,
it is vital to uncover relationships among dozens of these functions. In this talk, I show several
approaches to reveal latent relationships among cognitive functions and characteristics of eachfunction using methods in functional brain mapping and network science. First, I provide a mapof diverse cognitive functions (e.g. “emotion”, “attention”, “episodic memory”) where thefunctions whose neural substrates are closely related are located closely. Then, I show the resultsfrom a neuroscience-based conceptual analysis where a concept of each cognitive function isdivided into several sub-concepts. Moreover, I propose a novel way to construct the brain
parcellation where each parcel is originally associated with some cognitive functions. Using thoseparcels as network nodes in the brain network analysis, I show several relationships between thecognitive functions and features of the subnetwork in the brain that plays a role in the function.
Those results are informative not only for basic neuroscientific research but also for clinicalapplications and developments of brain-inspired artificial intelligence.