Complete lattice projection autoassociative memories

Orientador: Marcos Eduardo Ribeiro do Valle MesquitaTese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação CientíficaResumo: A capacidade do cérebro humano de armazenar e recordar informações por associação tem inspirado o desenvolvimento de modelos matemáticos referidos na literatura como memórias associativas. Em primeiro lugar, esta tese apresenta um conjunto de memórias autoassociativas (AMs) que pertecem à ampla classe das memórias morfológicas autoassociativas (AMMs). Especificamente, as memórias morfológicas autoassociativas de projeção max-plus e min-plus (max-plus e min-plus PAMMs), bem como suas composições, são introduzidas nesta tese. Tais modelos podem ser vistos como versões não distribuídas das AMMs propostas por Ritter e Sussner. Em suma, a max-plus PAMM produz a maior combinação max-plus das memórias fundamentais que é menor ou igual ao padrão de entrada. Dualmente, a min-plus PAMM projeta o padrão de entrada no conjunto de todas combinações min-plus. Em segundo, no contexto da teoria dos conjuntos fuzzy, esta tese propõe novas memórias autoassociativas fuzzy, referidas como classe das max-C e min-D FPAMMs. Uma FPAMM representa uma rede neural morfológica fuzzy com uma camada oculta de neurônios que é concebida para o armazenamento e recordação de conjuntos fuzzy ou vetores num hipercubo. Experimentos computacionais relacionados à classificação de padrões e reconhecimento de faces indicam possíveis aplicações dos novos modelos acima mencionadosAbstract: The human brain¿s ability to store and recall information by association has inspired the development various mathematical models referred to in the literature as associative memories. Firstly, this thesis presents a set of autoassociative memories (AMs) that belong to the broad class of autoassociative morphological memories (AMMs). Specifically, the max-plus and min-plus projection autoassociative morphological memories (max-plus and min-plus PAMMs), as well as their compositions, are introduced in this thesis. These models are non-distributed versions of the AMM models developed by Ritter and Sussner. Briefly, the max-plus PAMM yields the largest max-plus combination of the stored vectors which is less than or equal to the input pattern. Dually, the min-plus PAMM projects the input pattern into the set of all min-plus combinations. In second, in the context of fuzzy set theory, this thesis proposes new fuzzy autoassociative memories mentioned as class of the max-C and min-D FPAMMs. A FPAMM represents a fuzzy morphological neural network with a hidden layer of neurons that is designed for the storage and retrieval of fuzzy sets or vectors on a hypercube. Computational experiments concerning pattern classification and face recognition indicate possible applications of the aforementioned new AM modelsDoutoradoMatematica AplicadaDoutor em Matemática AplicadaCAPE

Connectivity, plasticity, and function of neuronal circuits in the zebrafish olfactory forebrain

For most living animals such as worms, insects, fishes, rodents and humans, chemical cues from the environment (odorants) play critical roles in guiding behaviors important for survival, including preying, mating, breeding, and escaping. How those odorants are detected, identified, learned, remembered, and used by the nervous system is a longstanding interest for neuroscientists. An animal that is well-suited to study the processing of odor information at the level of neuronal circuits is the zebrafish (Danio rerio) because its small brain size allows for exhaustive quantitative measurements of neuronal activity patterns. In vertebrates, odorants are detected by olfactory sensory neurons in the nose and transmitted to the first olfactory processing center in the brain, the olfactory bulb (OB), as patterns of neuronal activities. In the OB, neuronal activity patterns from the nose are transformed into odor-specific spatiotemporal activity patterns across second order neurons, the mitral cells. These discrete neuronal activity patterns are broadcast to various target areas. The largest of these higher brain areas is piriform cortex or its teleost homolog, the posterior zone of dorsal telencephalon (Dp). In this higher brain region, an odor-encoding neuronal activity pattern from the OB is thought to be encoded as a "gestalt", or "odor object", and possibly stored in memory by specific modifications of functional connections between distributed neuronal ensembles. Such neuronal ensembles are also thought to be connected with other brain regions that involved in the control of different behaviors. Therefore, by inducing a specific activity pattern in the OB, which then retrieves related neuronal ensemble activities in a higher brain region, an odor cue (or even partial cue) recalls an odor object memory that may further trigger a specific set of behavioral responses in the animal. The mechanisms by which odor object memory is synthesized, stored, and recalled is of major interest in neuroscience because it may provide fundamental insights into associative memory functions. However, dissecting higher brain functions such as associative memory will first require basic understanding of connectivity, plasticity, and related modulating factors for the underlying neuronal circuits. In this inaugural dissertation, I present an approach to study the connectivity, plasticity, and cholinergic modulation of the neural circuits in Dp and present new insights into the synaptic organizations of this neuronal network. In results part one, I show that transgenes can be introduced directly into the adult zebrafish brain by herpes simplex type I viruses (HSV-1) or electroporation. I developed a new procedure to target electroporation to defined brain areas, e.g. Dp, and identified promoters that produced strong long-term expression. These new methods fill an important gap in the spectrum of molecular tools for zebrafish and are likely to have a wide range of applications. In results part two, I used a combination of electroporation, optogenetics, electrophysiology, and pharmacology to study the intrinsic connectivity and plasticity in neural circuits of Dp. I found that connectivity between any pair of excitatory neurons in Dp is extremely sparse (connection probability < 1.5 %). The connection probability of inhibitory synapses is also sparse but slightly higher (< 2.5 %). Furthermore, I found that connectivity can be functionally modified by activity-dependent synaptic plasticity including spike timing-dependent long-term potentiation. Moreover, I show that cholinergic agonists differentially modulate excitatory and inhibitory synaptic transmissions in Dp, consistent with the notion that cholinergic neuromodulation controls experience-dependent changes in functional connectivity. These findings show that the synaptic organization of Dp is similar to mammalian piriform cortex and provide quantitative insights into the functional organization of a brain area that is likely to be involved in associative memory

Toward a further understanding of object feature binding: a cognitive neuroscience perspective.

The aim of this thesis is to lead to a further understanding of the neural mechanisms underlying object feature binding in the human brain. The focus is on information processing and integration in the visual system and visual shortterm memory. From a review of the literature it is clear that there are three major competing binding theories, however, none of these individually solves the binding problem satisfactorily. Thus the aim of this research is to conduct behavioural experimentation into object feature binding, paying particular attention to visual short-term memory. The behavioural experiment was designed and conducted using a within-subjects delayed responset ask comprising a battery of sixty-four composite objects each with three features and four dimensions in each of three conditions (spatial, temporal and spatio-temporal).Findings from the experiment,which focus on spatial and temporal aspects of object feature binding and feature proximity on binding errors, support the spatial theories on object feature binding, in addition we propose that temporal theories and convergence, through hierarchical feature analysis, are also involved. Because spatial properties have a dedicated processing neural stream, and temporal properties rely on limited capacity memory systems, memories for sequential information would likely be more difficult to accuratelyr ecall. Our study supports other studies which suggest that both spatial and temporal coherence to differing degrees,may be involved in object feature binding. Traditionally, these theories have purported to provide individual solutions, but this thesis proposes a novel unified theory of object feature binding in which hierarchical feature analysis, spatial attention and temporal synchrony each plays a role. It is further proposed that binding takes place in visual short-term memory through concerted and integrated information processing in distributed cortical areas. A cognitive model detailing this integrated proposal is given. Next, the cognitive model is used to inform the design and suggested implementation of a computational model which would be able to test the theory put forward in this thesis. In order to verify the model, future work is needed to implement the computational model.Thus it is argued that this doctoral thesis provides valuable experimental evidence concerning spatio-temporal aspects of the binding problem and as such is an additional building block in the quest for a solution to the object feature binding problem

해마 하위 영역 CA1과 CA3의 장면 자극에 기반한 장소 표상 형성 연구

학위논문 (박사) -- 서울대학교 대학원 : 자연과학대학 뇌인지과학과, 2021. 2. 이인아.When we recall the past experiences, we usually think of a scene which is a combination of what we saw, the sounds we hear, and the feeling we felt at that moment. Since the scene is an essential component of episodic memory, studying how scene stimuli are represented and stored in the brain is important in understanding the processes of formation, storage, and retrieval of our memories. One of the brain regions important for episodic memory is the hippocampus. It has been reported that patients or animals with damage to the hippocampus have trouble with retrieving past experiences or forming new memories. The hippocampus is involved not only in episodic memory but also in the formation of a cognitive map. In particular, the place cells observed in the rodent hippocampus play a key role in these functions. However, research on place cells has mainly focused on the firing patterns of cells during foraging in a space, and it has not been clear how hippocampal cells represent and make use of visual scenes for behavior. To find how scene stimuli are represented in place cells, I measured spiking activities of single neurons in the CA1, one of the subregions of hippocampus, and the subiculum, a major output of the hippocampus. Neuronal spiking activity was monitored when the rat performed a task of selecting right or left associated to the scene stimulus presented on monitors. As a result, I found that the place cells in the CA1 and subiculum showed rate modulation according to the scene stimulus. In addition, I also conducted an experiment using a virtual reality system to investigate the neural mechanisms of the formation of a place field based on visual scenes. In this experiment, the rat ran on a virtual linear track as visual cues were added one by one to make a scene-like environment. Neuronal activities of place cells were recorded in the CA1 and CA3 simultaneously to study the neural mechanisms of the development of a place field on the basis of external visual stimuli. Place fields appeared in the CA1 even with a single visual cue, whereas in the CA3, place fields only emerged when a sufficient number of visual cues were collectively arranged in a scene-like fashion. The results suggest that that scene is one of the key stimulus that effectively recruits the hippocampus.우리는 과거의 경험을 떠올릴 때 그 때를 묘사하는 문장을 떠올리는 것이 아니라 경험 한 순간에 보았던 것, 들렸던 소리, 느꼈던 감정 등이 복합적으로 어우러진 장면을 떠올리게 된다. 이렇게 장면은 일화 기억을 구성하는 중요한 요소라 할 수 있기에 장면 자극이 뇌에서 어떻게 표상되며 저장되는지를 연구하는 것은 우리 기억의 형성과 저장, 재인 과정을 이해하는데 있어 매우 중요하다고 볼 수 있다. 뇌에서 일화 기억을 담당한다고 알려진 영역은 해마로써, 해마에 손상을 입은 환자들 또는 동물들이 과거의 기억을 인출하거나 새로운 기억을 형성하는데 있어 어려움을 겪는다는 것이 여러 실험을 통해 보고 된 바 있다. 해마는 일화 기억뿐만 아니라 공간에 대한 지도를 형성하는 데에도 관여하는데, 특히, 설치류 해마에서 관찰 되는 장소 세포가 이러한 해마의 기능들을 수행하는데 핵심적인 역할을 하는 것으로 알려져 있다. 하지만 장소 세포는 주로 쥐가 공간을 탐색하는 과정에서의 발화 패턴을 관측한 연구가 주를 이루었으며 장면 자극이 개별 장소 세포의 발화 패턴을 통해 어떻게 표상이 되는지에 대한 연구는 미미한 수준이다. 이 논문에서 나는 장면 자극이 해마의 장소 세포에서 어떻게 표상되는지를 알아보고자 쥐가 모니터에 제시 된 장면 자극을 보고 오른쪽이나 왼쪽을 선택해야 하는 과제를 수행 할 때 해마의 하위 영역인 CA1과 해마의 정보를 전달 받아 뇌의 다른 영역으로 정보를 전달하는 해마이행부의 단일 세포 활동을 측정하였다. 그 결과 CA1과 해마이행부에서 관찰 된 장소 세포들이 장면 자극에 따른 발화율 변화를 보인다는 것을 확인 할 수 있었다. 이에 더하여 나는 해마의 장소 세포들이 장소장을 형성하기 위해서 필요한 시각 자극이 무엇이며, 이에 장면 자극이 어떤 역할을 하는지 확인하기 위해 가상 환경을 이용한 실험을 수행하였다. 이 실험에서는 쥐가 선형 트랙을 달릴 때, 빈 공간에서 시작하여 장면 자극을 형성 할 때까지 시각 자극을 하나씩 추가하면서 해마의 하위 영역인 CA1과 CA3의 장소 세포 활동을 측정 하는 과정을 통해 어떤 시각 자극이 장소 세포의 장소장 형성에 가장 큰 영향을 미치는 것인지 알아보았다. 그 결과 CA1의 장소 세포는 간단한 시각 자극의 추가에도 장소장을 잘 형성하는 모습을 보인 반면 CA3의 장소 세포들은 충분한 시각 자극이 모여서 장면 자극을 형성 한 경우에 장소장을 형성하는 것이 관찰되었다. 이러한 일련의 실험을 통하여 나는 장면 자극이 해마의 장소 세포 발화를 통해 표상되며, 해마의 하위 영역이 모두 장면 자극 처리에 관여하지만 그 중에서도 특히 CA3가 장면 자극을 처리 할 때에 한하여 큰 활성을 보인다는 것을 밝혔다.Abstract i Table of Contents iii List of Figures iv Background 1 Scene processing in the hippocampus 2 Anatomical connections of CA1 and CA3 4 Properties of place cell activity 7 Chapter 1. Visual scene representation of CA1 and subiculum in the visual scene memory task 10 Introduction 11 Materials and methods 14 Results 31 Discussion 60 Chapter 2. Role of the visual scene stimulus for place field formation in CA1 and CA3 65 Introduction 66 Materials and methods 68 Results 80 Discussion 107 General Discussion 118 Bibliography 124 국문초록 140Docto

Toward a further understanding of object feature binding : a cognitive neuroscience perspective

The aim of this thesis is to lead to a further understanding of the neural mechanisms underlying object feature binding in the human brain. The focus is on information processing and integration in the visual system and visual shortterm memory. From a review of the literature it is clear that there are three major competing binding theories, however, none of these individually solves the binding problem satisfactorily. Thus the aim of this research is to conduct behavioural experimentation into object feature binding, paying particular attention to visual short-term memory. The behavioural experiment was designed and conducted using a within-subjects delayed responset ask comprising a battery of sixty-four composite objects each with three features and four dimensions in each of three conditions (spatial, temporal and spatio-temporal).Findings from the experiment,which focus on spatial and temporal aspects of object feature binding and feature proximity on binding errors, support the spatial theories on object feature binding, in addition we propose that temporal theories and convergence, through hierarchical feature analysis, are also involved. Because spatial properties have a dedicated processing neural stream, and temporal properties rely on limited capacity memory systems, memories for sequential information would likely be more difficult to accuratelyr ecall. Our study supports other studies which suggest that both spatial and temporal coherence to differing degrees,may be involved in object feature binding. Traditionally, these theories have purported to provide individual solutions, but this thesis proposes a novel unified theory of object feature binding in which hierarchical feature analysis, spatial attention and temporal synchrony each plays a role. It is further proposed that binding takes place in visual short-term memory through concerted and integrated information processing in distributed cortical areas. A cognitive model detailing this integrated proposal is given. Next, the cognitive model is used to inform the design and suggested implementation of a computational model which would be able to test the theory put forward in this thesis. In order to verify the model, future work is needed to implement the computational model.Thus it is argued that this doctoral thesis provides valuable experimental evidence concerning spatio-temporal aspects of the binding problem and as such is an additional building block in the quest for a solution to the object feature binding problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Brain Computations and Connectivity [2nd edition]

This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

Of memories and ripples

The hippocampus is one of the regions in the mammalian brain that is associated with memory of events in their spatiotemporal context. Sequences of neuronal activity in the hippocampus are the chief candidate for a neurophysiological correlate of such contextual, or episodic memory. Simultaneously to replaying these behaviorally-related activity sequences, the hippocampus engages in a powerful and fast oscillation known as sharp-wave ripples (SWR). Ripples in turn participate in a brain-wide pattern of activity and may orchestrate the local strengthening of memories and their broadcasting to the cortex. In this Thesis, both memory sequences and ripple oscillations are studied in the light of the unifying hypothesis that the coordinated activation of a neuronal assembly represents an individual memory item in the sequences, and is at the same time responsible for the individual cycles in the oscillations. To test the hypothesis, we investigated SWR in vitro and in vivo in the mouse, using intracellular recordings of currents in CA1 pyramidal cells referenced to the local field potential. Expanding current hypotheses on SWR generation, we found powerful, well ripple-locked and spatially pervasive but CA1-local excitatory inputs, indicative of presynaptic assemblies of CA1 principal neurons. Combining a novel peeling reconstruction algorithm for synaptic currents with recordings at different holding potentials, we could for the first time unravel individual synaptic contributions during ripples. Analysis of the strikingly precise timing of currents demonstrated that inhibition aligns its phase to excitation over the course of a ripple. We carried on the dissection of ripples to the theoretical domain by incorporating the effect of inhibition into a mean field model of sequence replay. Using this model, we inquired what are the neuronal assembly size and inhibitory feedback strength that maximize the capacity of a hippocampal network to store memories, so that those memories can be successfully retrieved during ripple episodes. We found that a linearly coupled inhibitory population indeed helps increase storage capacity by dynamically stabilizing replay in an oscillatory manner for lower assembly sizes than in absence of inhibition. The findings about the temporal structure of neuronal activation during ripples complement our experimental observations. Collectively, they offer new insights on the physiology and function of sharp-wave ripples, paving the way for an integrated, continuous-time model of large networks of sparsely connected neurons that replay activity sequences concomitant to transient ensemble oscillations

Geometry and Topology in Memory and Navigation

Okinawa Institute of Science and Technology Graduate UniversityDoctor of PhilosophyGeometry and topology offer rich mathematical worlds and perspectives with which to study and improve our understanding of cognitive function. Here I present the following examples: (1) a functional role for inhibitory diversity in associative memories with graph- ical relationships; (2) improved memory capacity in an associative memory model with setwise connectivity, with implications for glial and dendritic function; (3) safe and effi- cient group navigation among conspecifics using purely local geometric information; and　(4) enhancing geometric and topological methods to probe the relations between neural activity and behaviour. In each work, tools and insights from geometry and topology are used in essential ways to gain improved insights or performance. This thesis contributes to our knowledge of the potential computational affordances of biological mechanisms (such as inhibition and setwise connectivity), while also demonstrating new geometric and topological methods and perspectives with which to deepen our understanding of cognitive tasks and their neural representations.doctoral thesi

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience