Neural fields, a cognitive approach
- Neural fields model complex cognitive and behavioral functions as emerging from adaptive sensorimotor loops involving the external world, body, and brain (1 sentence)
- The model studies visual attention through mechanisms like saliency maps, inhibition of return, and the premotor theory of attention using neural fields (1 sentence)
- Decision making in the model is studied through the dynamics of neural fields modeling structures like the superior colliculus that encode saccade plans and make decisions about eye movement targets (1 sentence)
The document discusses modeling cognition from an embodied perspective. It argues that cognition emerges from adaptive sensorimotor loops involving the external world, body, and brain. To truly understand complex cognitive systems, one must take a systemic approach and study the whole, integrated system in natural behavioral situations. The critical elements for cognition may not be a certain number of neurons, brain size, or specific structures, but rather the embodied interactions and experience of an entity with its environment.
" ...we managed to explicitly dissociate reinforcement learning from Hebbian learning and demonstrated covert learning inside the basal ganglia. These results suggest that a behavioral decision results from both the cooperation (acquisition) and competition (expression) of two distinct but entangled memory systems, the goal-directed system and the habit system that may represent the two ends of the same graded phenomenon."
This document provides an overview of computational neuroscience from modeling single neurons to neural circuits and behavior. It discusses:
- Models of single neurons from the Hodgkin-Huxley model to reduced models like FitzHugh-Nagumo and Izhikevich neurons.
- How neurons are organized into neural circuits using different connection types and how properties like synchronization emerge from circuit properties.
- Approaches to modeling larger brain areas as neural populations using techniques like neural fields to model mean firing rates over continuous space.
- Phenomena like neural coding, plasticity, learning and their role in computational models of behaviors and cognition. It provides examples of modeling visual attention, decision making and more
Brain research has made tremendous progress over the last few decades in nearly all areas of investigation and yet, the comprehension of cognition still eludes us because most high-level functions, such as decision making, results from the complex and dynamic interaction between several structures. On the one hand we have a fragmented collection of computational models whose unification is out of reach, on the other hand we have holistic approaches whose complexity obliterates any hope of comprehension. Informed by neuroscience and guided by philosophy, I propose to build the foundations of a research program in computational neuroscience in order to explain how cognition develop in most vertebrates, while guaranteeing its intelligibility.
The presentation by Cerenaut at the orientation (2021-06-05) for the 5th WBA Hackathon, an online AI competition to implement working memory
https://wba-initiative.org/en/18687/
- Neuroscientific issues
- Architecture details
- Instruction on the CodaLab competition
This document discusses neural coding and how neurons encode information during cognitive tasks. It summarizes research using single-unit neural recordings in rats performing a directional control task. Three key findings are presented:
1) Neural firing rates in areas like PM and M1 encoded information about trial outcomes and the rat's learning progress over multiple sessions.
2) Precise spike timing provided information about the rat's cognitive state, with more synchronized activity observed early in learning.
3) Network modeling revealed changes in functional connectivity between neurons associated with synaptic plasticity, with more optimized patterns emerging as the rat became proficient at the task.
Week 8 : The neural basis of consciousness : consciousness vs. attention Nao (Naotsugu) Tsuchiya
12-week lecture series on "the neural basis of consciousness" by Prof Nao Tsuchiya.
Given to 3rd year undergraduate level. No prerequisites.
Contents:
1) How can we define “attention”?
2) What are the paradigms to manipulate attention?
3) What are the neuronal mechanisms of attention?
4) How can we explain the relationship between attention and consciousness?
The document discusses modeling cognition from an embodied perspective. It argues that cognition emerges from adaptive sensorimotor loops involving the external world, body, and brain. To truly understand complex cognitive systems, one must take a systemic approach and study the whole, integrated system in natural behavioral situations. The critical elements for cognition may not be a certain number of neurons, brain size, or specific structures, but rather the embodied interactions and experience of an entity with its environment.
" ...we managed to explicitly dissociate reinforcement learning from Hebbian learning and demonstrated covert learning inside the basal ganglia. These results suggest that a behavioral decision results from both the cooperation (acquisition) and competition (expression) of two distinct but entangled memory systems, the goal-directed system and the habit system that may represent the two ends of the same graded phenomenon."
This document provides an overview of computational neuroscience from modeling single neurons to neural circuits and behavior. It discusses:
- Models of single neurons from the Hodgkin-Huxley model to reduced models like FitzHugh-Nagumo and Izhikevich neurons.
- How neurons are organized into neural circuits using different connection types and how properties like synchronization emerge from circuit properties.
- Approaches to modeling larger brain areas as neural populations using techniques like neural fields to model mean firing rates over continuous space.
- Phenomena like neural coding, plasticity, learning and their role in computational models of behaviors and cognition. It provides examples of modeling visual attention, decision making and more
Brain research has made tremendous progress over the last few decades in nearly all areas of investigation and yet, the comprehension of cognition still eludes us because most high-level functions, such as decision making, results from the complex and dynamic interaction between several structures. On the one hand we have a fragmented collection of computational models whose unification is out of reach, on the other hand we have holistic approaches whose complexity obliterates any hope of comprehension. Informed by neuroscience and guided by philosophy, I propose to build the foundations of a research program in computational neuroscience in order to explain how cognition develop in most vertebrates, while guaranteeing its intelligibility.
The presentation by Cerenaut at the orientation (2021-06-05) for the 5th WBA Hackathon, an online AI competition to implement working memory
https://wba-initiative.org/en/18687/
- Neuroscientific issues
- Architecture details
- Instruction on the CodaLab competition
This document discusses neural coding and how neurons encode information during cognitive tasks. It summarizes research using single-unit neural recordings in rats performing a directional control task. Three key findings are presented:
1) Neural firing rates in areas like PM and M1 encoded information about trial outcomes and the rat's learning progress over multiple sessions.
2) Precise spike timing provided information about the rat's cognitive state, with more synchronized activity observed early in learning.
3) Network modeling revealed changes in functional connectivity between neurons associated with synaptic plasticity, with more optimized patterns emerging as the rat became proficient at the task.
Week 8 : The neural basis of consciousness : consciousness vs. attention Nao (Naotsugu) Tsuchiya
12-week lecture series on "the neural basis of consciousness" by Prof Nao Tsuchiya.
Given to 3rd year undergraduate level. No prerequisites.
Contents:
1) How can we define “attention”?
2) What are the paradigms to manipulate attention?
3) What are the neuronal mechanisms of attention?
4) How can we explain the relationship between attention and consciousness?
Week 4 the neural basis of consciousness introduction to the visual systemNao (Naotsugu) Tsuchiya
12-week lecture series on "the neural basis of consciousness" by Prof Nao Tsuchiya.
Given to 3rd year undergraduate level. No prerequisites.
Contents:
1) What are behavioral and neural signatures of nonconscious processing?
2) Can blindsight-like behavior induced in monkeys? What are the evidence?
3) How can we discriminate nonconscious from conscious behaviors using a concept of metacognition?
4) What is the structure of eye and how does it shape our conscious vision?
Mirror neurons help make meaning in communication in 3 key ways:
1) Mirror neurons fire both when we perform an action and when we observe another performing the same action, allowing us to understand others' intentions and have empathy.
2) Through mirroring others' gestures, words and actions in conversation, mirror neurons enable understanding without explicit meaning.
3) Mirror neurons are thought to have played a role in the evolution of language and culture by facilitating learning through imitation.
This document discusses using network and semantic analysis to map disciplinary structures in cognitive neuroscience. It provides examples of contemporary meta-analyses tools like Neurosynth and the Cognitive Atlas that synthesize knowledge in the field using semantic terminology and brain locations. The document outlines applying network analysis techniques like text network analysis to represent relations between anatomy and concept terms found in cognitive neuroscience literature. It describes generating networks from a corpus of cognitive neuroscience articles and analyzing the conceptual, anatomical, and functional network structures that emerge. Limitations and future directions are also discussed.
This document summarizes a student's thesis project investigating the role of high frequency oscillations (HFOs) in spatial memory tasks. The student presents evidence that replay of rodent grid cells during sharp-wave ripple complexes is low compared to replay in place cells. The student also reports on a pilot study with an epilepsy patient, one of the first to attempt detecting HFOs during cognitive processes. Future research controlling task design may help identify behavioral correlates of HFOs and their role in cognitive function and memory processes.
Week 11 neural basis of consciousness : consciousness and integration (1)Nao (Naotsugu) Tsuchiya
12-week lecture series on "the neural basis of consciousness" by Prof Nao Tsuchiya.
Given to 3rd year undergraduate level. No prerequisites.
Contents:
1) How can we compute integrated information?
2) How we can estimate the proposed boundary of consciousness?
3) What are the reported phenomenology / behaviors of split brain patients?
4) How does IIT explain various known facts about consciousness, such as split brain patients?
The document discusses neural computing and artificial neural networks. It provides an overview of several key topics, including the aims of investigating biological nervous systems and designing artificial systems that emulate biological principles. The document outlines several planned lectures on topics like the differences between natural and artificial intelligence, neurobiology, neural processing and signaling, stochasticity in neural codes, neural operators for vision, cognition and evolution, and artificial neural networks. It also lists some reference books and provides examples for exercises on neural computing concepts.
This document provides an overview of the learning objectives and topics to be covered in Week 1 of a course on the neural basis of consciousness. The key questions to be answered are: a) Why are we interested in consciousness? b) What do we mean by consciousness? c) How can we study consciousness? d) What are the challenges of understanding consciousness in animals, plants and robots? The document discusses phenomenology, experimental approaches, theories of consciousness and challenges in the field. It also provides an introduction to relevant neuroanatomy and encourages students to learn basic terminology to prepare for future assessments.
Neuroaesthetics: science embraces art (UX Bristol)Nomensa
This document is a presentation on neuroaesthetics given by Simon from Nomensa. It discusses how insights from neuroscience can be applied to design. It provides examples of artworks and explores concepts like inherited and acquired brain concepts, perceptual constancy, abstraction, and ambiguity. The presentation suggests designers can benefit from understanding how the brain perceives patterns, processes visual information, and experiences pleasure to create more engaging and intuitive designs.
Week 9 the neural basis of consciousness : dissociation of consciousness &...Nao (Naotsugu) Tsuchiya
12-week lecture series on "the neural basis of consciousness" by Prof Nao Tsuchiya.
Given to 3rd year undergraduate level. No prerequisites.
Contents:
1) What are the logic and evidence of experiments which demonstrate dissociation between attention and consciousness?
2) How do they manipulate & assess consciousness?
3) How do they manipulate & assess attention?
The document discusses the structure and function of the brain as a complex network. It notes that the brain exhibits both segregation and integration at multiple scales from neurons to regions. The structural connectivity of the brain forms a small-world network that allows for both specialized processing within clusters and integrated processing between regions via short path lengths. Computational models can capture large-scale brain activity and dynamics based on the underlying structural connectivity.
2013-1 Machine Learning Lecture 07 - Michael Negnevitsky - Hybrid Intellig…Dongseo University
This document discusses hybrid intelligent systems, specifically evolutionary neural networks and fuzzy evolutionary systems. It describes how genetic algorithms can be used to optimize neural network weights and topology by encoding them in chromosomes that are evolved over generations using crossover and mutation operators. Genetic algorithms guide the selection of an optimal neural network structure and weight set for a given problem. The document also explains how genetic algorithms can be applied to generate fuzzy rules and tune membership functions for fuzzy systems used in classification problems.
This document provides an overview of computational social neuroscience and the use of dynamical models to study social behavior. It discusses how dynamical models have been expanded over time to capture increasingly complex social phenomena by incorporating elements like broken symmetry, discreteness, adaptation, directedness, and multi-agent interactions. Models have been used to study behavioral coordination and related neural oscillations. The goal is to develop a "nesting doll" modeling strategy to describe social behavior across multiple scales from molecules to culture.
2013-1 Machine Learning Lecture 04 - Michael Negnevitsky - Artificial neur…Dongseo University
This document discusses artificial neural networks and supervised learning. It begins with an introduction to how biological neural networks in the brain work, then describes the basic components and functions of artificial neurons. The perceptron, an early single-layer neural network, is introduced as well as multi-layer neural networks. Backpropagation is described as a method for training multi-layer networks by propagating error backwards from the output layer to adjust weights. Key topics covered include the neuron model, perceptron learning rule, perceptron training algorithm, limitations of single-layer networks, architecture of multi-layer networks, and error backpropagation for training multi-layer networks.
Commonsense reasoning as a key feature for dynamic knowledge invention and co...Antonio Lieto
This document discusses commonsense reasoning and its importance for computational creativity and knowledge invention. It provides an overview of past AI and cognitive science approaches to commonsense reasoning such as semantic networks, frames, and default logic. It then presents the TCL (Typicality Description Logic) framework, which extends description logics with typicality, probabilities, and cognitive heuristics to model commonsense conceptual combination. The framework is applied to generate novel concepts to achieve goals and to dynamically classify multimedia content. Evaluations show it effectively reclassifies content and generates recommendations that users and experts find high quality.
Info-Computationalism and Philosophical Aspects of Research in Information Sc...Gordana Dodig-Crnkovic
This document discusses several topics related to info-computationalism and philosophical aspects of scientific research, including:
1) The major paradigm shifts in our view of the universe from mytho-poetic to mechanical to computational universes.
2) Natural philosophy and how computation and information are two interrelated phenomena that are studied in info-computationalism.
3) Limitations of the Turing machine model and how natural computation goes beyond Turing machines to model complex biological systems.
The sophisticated signal processing techniques developed during last years for structural and functional imaging methods allow us to detect abnormalities of brain connectivity in brain disorders with unprecedented detail. Interestingly, recent works shed light on both functional and structural underpinnings of musical anhedonia (i.e., the individual's incapacity to enjoy listening to music). On the other hand, computational models based on brain simulation tools are being used more and more for mapping the functional consequences of structural abnormalities. The latter could help to better understand the mechanism that is impaired in people unable to derive pleasure from music, and formulate hypotheses on how music acquired reward value. The presentation gives an overview of today's studies and proposes a possible simulation pipeline to reproduce such scenario.
Week 7 the neural basis of consciousness: higher visual areas and the nccNao (Naotsugu) Tsuchiya
12-week lecture series on "the neural basis of consciousness" by Prof Nao Tsuchiya.
Given to 3rd year undergraduate level. No prerequisites.
Contents:
1) What are the evidence supporting the claim that higher visual areas are the NCC?
2) What are the phenomenological and behavioral characteristics of binocular rivalry?
3) How did the researchers establish the binocular rivalry paradigm with monkeys as participants?
4) What are the implications of the NCC studies using binocular rivalry?
A General Principle of Learning and its Application for Reconciling Einstein’...Jeffrey Huang
This document proposes a general principle of learning based on discovering intrinsic constraint models. It defines intelligence as the ability to understand the world by discovering intrinsic variables and constraints that have minimum entropy. The key goals of learning are to map observations to intrinsic variables and detect constraints to minimize a model entropy objective function. Discovering intrinsic variables is critical for maximizing prediction accuracy, learning efficiency, and generalization power. The principle provides a theoretical foundation for explaining and developing artificial general intelligence.
Heterogeneous Proxytypes as a Unifying Cognitive Framework for Conceptual Rep...Antonio Lieto
This document summarizes Antonio Lieto's work on developing a cognitive framework called heterogeneous proxytypes for conceptual representation and reasoning in artificial systems. The framework incorporates multiple knowledge representations, including prototypes, exemplars, and theories. It allows different representations and reasoning mechanisms to be activated based on context. Lieto describes cognitive models that integrate heterogeneous proxytypes, like the DUAL-PECCS system, and evaluates them on commonsense reasoning tasks.
This document provides an overview of cognitive psychology. It discusses the topics covered in cognitive psychology such as memory, attention, problem solving, and more. It defines cognition and cognitive psychology. Experimental cognitive psychology, computational cognitive science, cognitive neuropsychology, and cognitive neuroscience are described as approaches to studying cognitive psychology. Limitations of each approach are also outlined. Brain imaging techniques used in cognitive neuroscience like EEG, PET, fMRI are explained. Basic brain anatomy terms are defined. Eyetracking is discussed as an experimental technique.
This document provides an overview of key concepts in sensation and perception. It begins by defining sensation as the process by which sensory receptors receive stimulation from the environment and transmit that information to the brain. Perception is defined as the process of selecting, organizing, and interpreting sensory information to recognize meaningful objects and events. The document then discusses several principles of perception, including that perception is an active constructive process, not a passive recording of external stimuli. It presents examples of perceptual illusions and organization to illustrate this. Subsequent sections discuss theories of perception, the distinction between active and passive touch, and Gibson's theory of affordances. The document emphasizes that perception involves an engagement with the world through action and exploration, not just internal representations in the
Week 4 the neural basis of consciousness introduction to the visual systemNao (Naotsugu) Tsuchiya
12-week lecture series on "the neural basis of consciousness" by Prof Nao Tsuchiya.
Given to 3rd year undergraduate level. No prerequisites.
Contents:
1) What are behavioral and neural signatures of nonconscious processing?
2) Can blindsight-like behavior induced in monkeys? What are the evidence?
3) How can we discriminate nonconscious from conscious behaviors using a concept of metacognition?
4) What is the structure of eye and how does it shape our conscious vision?
Mirror neurons help make meaning in communication in 3 key ways:
1) Mirror neurons fire both when we perform an action and when we observe another performing the same action, allowing us to understand others' intentions and have empathy.
2) Through mirroring others' gestures, words and actions in conversation, mirror neurons enable understanding without explicit meaning.
3) Mirror neurons are thought to have played a role in the evolution of language and culture by facilitating learning through imitation.
This document discusses using network and semantic analysis to map disciplinary structures in cognitive neuroscience. It provides examples of contemporary meta-analyses tools like Neurosynth and the Cognitive Atlas that synthesize knowledge in the field using semantic terminology and brain locations. The document outlines applying network analysis techniques like text network analysis to represent relations between anatomy and concept terms found in cognitive neuroscience literature. It describes generating networks from a corpus of cognitive neuroscience articles and analyzing the conceptual, anatomical, and functional network structures that emerge. Limitations and future directions are also discussed.
This document summarizes a student's thesis project investigating the role of high frequency oscillations (HFOs) in spatial memory tasks. The student presents evidence that replay of rodent grid cells during sharp-wave ripple complexes is low compared to replay in place cells. The student also reports on a pilot study with an epilepsy patient, one of the first to attempt detecting HFOs during cognitive processes. Future research controlling task design may help identify behavioral correlates of HFOs and their role in cognitive function and memory processes.
Week 11 neural basis of consciousness : consciousness and integration (1)Nao (Naotsugu) Tsuchiya
12-week lecture series on "the neural basis of consciousness" by Prof Nao Tsuchiya.
Given to 3rd year undergraduate level. No prerequisites.
Contents:
1) How can we compute integrated information?
2) How we can estimate the proposed boundary of consciousness?
3) What are the reported phenomenology / behaviors of split brain patients?
4) How does IIT explain various known facts about consciousness, such as split brain patients?
The document discusses neural computing and artificial neural networks. It provides an overview of several key topics, including the aims of investigating biological nervous systems and designing artificial systems that emulate biological principles. The document outlines several planned lectures on topics like the differences between natural and artificial intelligence, neurobiology, neural processing and signaling, stochasticity in neural codes, neural operators for vision, cognition and evolution, and artificial neural networks. It also lists some reference books and provides examples for exercises on neural computing concepts.
This document provides an overview of the learning objectives and topics to be covered in Week 1 of a course on the neural basis of consciousness. The key questions to be answered are: a) Why are we interested in consciousness? b) What do we mean by consciousness? c) How can we study consciousness? d) What are the challenges of understanding consciousness in animals, plants and robots? The document discusses phenomenology, experimental approaches, theories of consciousness and challenges in the field. It also provides an introduction to relevant neuroanatomy and encourages students to learn basic terminology to prepare for future assessments.
Neuroaesthetics: science embraces art (UX Bristol)Nomensa
This document is a presentation on neuroaesthetics given by Simon from Nomensa. It discusses how insights from neuroscience can be applied to design. It provides examples of artworks and explores concepts like inherited and acquired brain concepts, perceptual constancy, abstraction, and ambiguity. The presentation suggests designers can benefit from understanding how the brain perceives patterns, processes visual information, and experiences pleasure to create more engaging and intuitive designs.
Week 9 the neural basis of consciousness : dissociation of consciousness &...Nao (Naotsugu) Tsuchiya
12-week lecture series on "the neural basis of consciousness" by Prof Nao Tsuchiya.
Given to 3rd year undergraduate level. No prerequisites.
Contents:
1) What are the logic and evidence of experiments which demonstrate dissociation between attention and consciousness?
2) How do they manipulate & assess consciousness?
3) How do they manipulate & assess attention?
The document discusses the structure and function of the brain as a complex network. It notes that the brain exhibits both segregation and integration at multiple scales from neurons to regions. The structural connectivity of the brain forms a small-world network that allows for both specialized processing within clusters and integrated processing between regions via short path lengths. Computational models can capture large-scale brain activity and dynamics based on the underlying structural connectivity.
2013-1 Machine Learning Lecture 07 - Michael Negnevitsky - Hybrid Intellig…Dongseo University
This document discusses hybrid intelligent systems, specifically evolutionary neural networks and fuzzy evolutionary systems. It describes how genetic algorithms can be used to optimize neural network weights and topology by encoding them in chromosomes that are evolved over generations using crossover and mutation operators. Genetic algorithms guide the selection of an optimal neural network structure and weight set for a given problem. The document also explains how genetic algorithms can be applied to generate fuzzy rules and tune membership functions for fuzzy systems used in classification problems.
This document provides an overview of computational social neuroscience and the use of dynamical models to study social behavior. It discusses how dynamical models have been expanded over time to capture increasingly complex social phenomena by incorporating elements like broken symmetry, discreteness, adaptation, directedness, and multi-agent interactions. Models have been used to study behavioral coordination and related neural oscillations. The goal is to develop a "nesting doll" modeling strategy to describe social behavior across multiple scales from molecules to culture.
2013-1 Machine Learning Lecture 04 - Michael Negnevitsky - Artificial neur…Dongseo University
This document discusses artificial neural networks and supervised learning. It begins with an introduction to how biological neural networks in the brain work, then describes the basic components and functions of artificial neurons. The perceptron, an early single-layer neural network, is introduced as well as multi-layer neural networks. Backpropagation is described as a method for training multi-layer networks by propagating error backwards from the output layer to adjust weights. Key topics covered include the neuron model, perceptron learning rule, perceptron training algorithm, limitations of single-layer networks, architecture of multi-layer networks, and error backpropagation for training multi-layer networks.
Commonsense reasoning as a key feature for dynamic knowledge invention and co...Antonio Lieto
This document discusses commonsense reasoning and its importance for computational creativity and knowledge invention. It provides an overview of past AI and cognitive science approaches to commonsense reasoning such as semantic networks, frames, and default logic. It then presents the TCL (Typicality Description Logic) framework, which extends description logics with typicality, probabilities, and cognitive heuristics to model commonsense conceptual combination. The framework is applied to generate novel concepts to achieve goals and to dynamically classify multimedia content. Evaluations show it effectively reclassifies content and generates recommendations that users and experts find high quality.
Info-Computationalism and Philosophical Aspects of Research in Information Sc...Gordana Dodig-Crnkovic
This document discusses several topics related to info-computationalism and philosophical aspects of scientific research, including:
1) The major paradigm shifts in our view of the universe from mytho-poetic to mechanical to computational universes.
2) Natural philosophy and how computation and information are two interrelated phenomena that are studied in info-computationalism.
3) Limitations of the Turing machine model and how natural computation goes beyond Turing machines to model complex biological systems.
The sophisticated signal processing techniques developed during last years for structural and functional imaging methods allow us to detect abnormalities of brain connectivity in brain disorders with unprecedented detail. Interestingly, recent works shed light on both functional and structural underpinnings of musical anhedonia (i.e., the individual's incapacity to enjoy listening to music). On the other hand, computational models based on brain simulation tools are being used more and more for mapping the functional consequences of structural abnormalities. The latter could help to better understand the mechanism that is impaired in people unable to derive pleasure from music, and formulate hypotheses on how music acquired reward value. The presentation gives an overview of today's studies and proposes a possible simulation pipeline to reproduce such scenario.
Week 7 the neural basis of consciousness: higher visual areas and the nccNao (Naotsugu) Tsuchiya
12-week lecture series on "the neural basis of consciousness" by Prof Nao Tsuchiya.
Given to 3rd year undergraduate level. No prerequisites.
Contents:
1) What are the evidence supporting the claim that higher visual areas are the NCC?
2) What are the phenomenological and behavioral characteristics of binocular rivalry?
3) How did the researchers establish the binocular rivalry paradigm with monkeys as participants?
4) What are the implications of the NCC studies using binocular rivalry?
A General Principle of Learning and its Application for Reconciling Einstein’...Jeffrey Huang
This document proposes a general principle of learning based on discovering intrinsic constraint models. It defines intelligence as the ability to understand the world by discovering intrinsic variables and constraints that have minimum entropy. The key goals of learning are to map observations to intrinsic variables and detect constraints to minimize a model entropy objective function. Discovering intrinsic variables is critical for maximizing prediction accuracy, learning efficiency, and generalization power. The principle provides a theoretical foundation for explaining and developing artificial general intelligence.
Heterogeneous Proxytypes as a Unifying Cognitive Framework for Conceptual Rep...Antonio Lieto
This document summarizes Antonio Lieto's work on developing a cognitive framework called heterogeneous proxytypes for conceptual representation and reasoning in artificial systems. The framework incorporates multiple knowledge representations, including prototypes, exemplars, and theories. It allows different representations and reasoning mechanisms to be activated based on context. Lieto describes cognitive models that integrate heterogeneous proxytypes, like the DUAL-PECCS system, and evaluates them on commonsense reasoning tasks.
This document provides an overview of cognitive psychology. It discusses the topics covered in cognitive psychology such as memory, attention, problem solving, and more. It defines cognition and cognitive psychology. Experimental cognitive psychology, computational cognitive science, cognitive neuropsychology, and cognitive neuroscience are described as approaches to studying cognitive psychology. Limitations of each approach are also outlined. Brain imaging techniques used in cognitive neuroscience like EEG, PET, fMRI are explained. Basic brain anatomy terms are defined. Eyetracking is discussed as an experimental technique.
This document provides an overview of key concepts in sensation and perception. It begins by defining sensation as the process by which sensory receptors receive stimulation from the environment and transmit that information to the brain. Perception is defined as the process of selecting, organizing, and interpreting sensory information to recognize meaningful objects and events. The document then discusses several principles of perception, including that perception is an active constructive process, not a passive recording of external stimuli. It presents examples of perceptual illusions and organization to illustrate this. Subsequent sections discuss theories of perception, the distinction between active and passive touch, and Gibson's theory of affordances. The document emphasizes that perception involves an engagement with the world through action and exploration, not just internal representations in the
1) The document discusses Araya Inc.'s research into understanding and creating consciousness by developing generative agents capable of information generation.
2) It proposes that consciousness arises from an agent's ability to generate counterfactual predictions about future and past states, detached from the present environment.
3) Various approaches are described for creating generative agents, including recurrent autoencoders and generative control of agents using latent space representations to select actions. The ultimate goal is to directly connect artificial and biological systems to investigate whether qualia can emerge in artificial systems.
BAAI Conference 2021: The Thousand Brains Theory - A Roadmap for Creating Mac...Numenta
Jeff Hawkins presented a talk on "The Thousand Brains Theory: A Roadmap to Machine Intelligence" at the Beijing Academy of Artificial Intelligence Conference on 1st June 2021. In this talk, he discussed the key components of The Thousand Brains Theory and Numenta's recent work.
Jeff Hawkins Human Brain Project Summit Keynote: "Location, Location, Locatio...Numenta
The document summarizes Jeff Hawkins' presentation on a proposed framework for understanding intelligence and cortical computation. Some key points:
- Hawkins proposes that grid cells exist in the neocortex and represent the location of sensory input relative to objects. Each cortical column learns a complete model of objects, including their location spaces.
- Objects can be composed of other objects via displacement cells, allowing efficient learning of new combinations without relearning parts. Behaviors can also be represented as sequences of displacements.
- This framework provides insights into neuroscience, concepts, limits of intelligence, and has implications for building true artificial intelligence based on distributed object-centric representations.
The document discusses attention and consciousness. It defines attention as the process of focusing on a particular stimulus while disregarding other stimuli. It presents a taxonomy of attention that divides attention into external attention, which selects incoming sensory information, and internal attention, which selects control strategies and maintains internally generated information. It describes Michael Posner's model of the attention system in the human brain, which includes alerting, orienting, and executive function systems. It discusses early theories of attention, such as William James' definition and dichotic listening experiments, and how these helped revive research on attention after it declined during the behaviorist era.
This document summarizes a lecture on attention and motor control. It discusses different types of attention like selective and sustained attention. It examines how attention and eye movements are controlled by brain regions like the frontal eye field and superior colliculus. The lecture also explores the dorsal and ventral attention networks in the brain and how the saliency model can be used to predict eye gaze and has clinical applications for disorders like ADHD, autism and schizophrenia.
Generative AI: Past, Present, and Future – A Practitioner's PerspectiveHuahai Yang
Generative AI: Past, Present, and Future – A Practitioner's Perspective
As the academic realm grapples with the profound implications of generative AI
and related applications like ChatGPT, I will present a grounded view from my
experience as a practitioner. Starting with the origins of neural networks in
the fields of logic, psychology, and computer science, I trace its history and
align it within the wider context of the pursuit of artificial intelligence.
This perspective will also draw parallels with historical developments in
psychology. Against this backdrop, I chart a proposed trajectory for the future.
Finally, I provide actionable insights for both academics and enterprising
individuals in the field.
Computational Neuroscience - The Brain - Computer Science InterfaceChristopher Currin
Understanding intelligence is one of the most challenging scientific problems faced by humanity.
This talk will provide an introduction to the multi-disciplinary field of Computational Neuroscience: the questions it seeks to answer and some of the (mathematical & computational) techniques used to investigate how we fundamentally think.
Currently doing his Computational Neuroscience PhD at the University of Cape Town, Chris has a wonderfully weird background in machine learning, neuroscience, and psychology. He is fascinated by how we think and learn, sometimes to a fault, and how this works in both biological and artificial intelligence.
The document discusses the complex motor control involved in the snatch lift from a neuroscience perspective. It describes how performing the snatch requires coordinated activation of muscles throughout the body within 2 seconds, posing an extreme challenge for the nervous system. It reviews the different areas of the brain and spinal cord that must work together, including the motor cortex, cerebellum, reflexes, and pattern generators. It discusses implications for coaching, including using demonstrations, video feedback, verbal cues, and different practice structures depending on the stage of learning.
This document provides an overview of cognitive ergonomics and human factors. It discusses topics like brain structure and function, attention, visual perception, memory models, mental models, problem solving, decision making, and tools like eye tracking. The agenda outlines concepts like human factors, cognitive ergonomics, and how research on the brain can inform design. Measurement methods and examples are provided for many topics. The document is an introduction to cognitive and human factors concepts.
This document discusses using a neural network model to simulate coordinate transformations performed by neurons in the brain. Specifically, it describes experiments using a three-layer backpropagation network to model neurons in area 7a of the macaque parietal cortex. The network takes in visual stimulus and eye position data and outputs head-centered coordinates. Initial results show the model can perform this transformation with average errors of less than 4 degrees. Gain modulation is discussed as a potential mechanism for these coordinate transformations computed by parietal neurons.
Errors of Artificial Intelligence, their Correction and Simplicity Revolution...Alexander Gorban
This document discusses two challenges in artificial intelligence: errors made by AI systems and the concept of "grandmother cells" in neuroscience. Regarding AI errors, it proposes using stochastic separation theorems from high-dimensional geometry to build fast, one-shot correctors for AI systems. Regarding grandmother cells, it reviews experiments showing neurons selectively responding to concepts and discusses how ensembles of neurons could model concept cells and neural selectivity. The document outlines applications in computer vision, robotics, and multi-agent learning and concludes that geometric theorems allow creation of efficient correctors and understanding of neural encoding schemes.
Attention & Perception - Cognitive Psychology.pptxLinda M
Perception and attention are key topics in cognitive psychology. Perception involves interpreting sensory impressions to understand one's environment. Gestalt psychologists viewed perception as organized patterns or "wholes", rather than separate parts. Both bottom-up and top-down processes influence perception. Attention allows us to focus on specific stimuli while filtering out others. Selective attention experiments show people often do not consciously perceive unattended information. Divided attention tasks demonstrate limitations in performing multiple tasks simultaneously.
The document discusses two prominent models of memory: the multi-store model and the working memory model. The multi-store model, proposed by Atkinson and Shiffrin, posits that memory consists of three stores - sensory memory, short-term memory, and long-term memory. Information moves from sensory memory to short-term memory to long-term memory through encoding and rehearsal processes. The working memory model refined this by proposing separate subsystems for visual-spatial and auditory information within short-term/working memory. Both models provided a framework for understanding how information is processed and stored in memory but also had limitations that further research helped to address.
This document provides an outline and information for the CS451/CS551/EE565 Artificial Intelligence course on learning and connectionism taught by Prof. Janice T. Searleman. It includes topics on learning agents, neural networks, reading assignments from relevant AI textbooks, details on the final exam and homework assignment. Concepts covered include different types of learning such as rote learning, reinforcement learning, supervised and unsupervised induction. Neural networks and connectionism are also discussed.
The document provides an introduction to cognitive psychology. It discusses that cognitive psychology is the study of mental processes, including attention, learning, memory, language, and emotions. It notes that cognitive psychology informs other areas of psychology and has real-world applications in areas like attention while driving, improving learning techniques, and designing understandable text. The document also summarizes common frameworks for explaining cognition, such as the information processing approach, production systems, semantic networks, and connectionism.
The document provides an introduction to cognitive psychology. It discusses that cognitive psychology is the study of mental processes, including attention, learning, memory, language, and emotions. It notes that cognitive psychology informs other areas of psychology and has real-world applications in areas like attention while driving, improving learning techniques, and designing understandable text. The document also summarizes common frameworks for explaining cognition, such as the information processing approach, production systems, semantic networks, and connectionism.
Similar to Neural fields, a cognitive approach (20)
This course is an introduction to digital typography rendering, providing key concepts of typography as well as introducing several computer graphics techniques to render text, from the oldest and most common techniques (texture based) to the latest methods taking full advantage of shaders with quasi-flawless rendering.
This document provides an overview of machine learning concepts including:
1) The main types of machine learning problems are classification, regression, clustering, and optimization.
2) Supervised, unsupervised, and reinforcement learning are the main approaches to teach a machine.
3) Building machine learning models involves data collection/preparation, model training/testing, and deployment of the trained model.
This document discusses using shader-based rendering for modern and interactive scientific visualization in Python. It outlines limitations of existing Python visualization libraries like Matplotlib, and how shader programming in OpenGL can address these through higher quality rendering of text, dashed lines, image filters, grids and other primitives. The approach improves rendering quality and speeds through GPU processing while maintaining an easy to use Python interface. Several open source projects aim to integrate these techniques into interactive visualization libraries.
This document summarizes key concepts related to open science including open access, open data, open source, open methodology, and open peer review. It provides definitions and examples for each concept. It also discusses incentives and challenges of open science as well as important figures who have advanced open science ideals through their work.
This document provides a summary of a 2.5 hour crash course on scientific visualization. It discusses what scientific visualization is, examples of its uses, and frameworks for the visualization pipeline. It also covers different data and graphical element types, visualization techniques, and 10 simple rules for creating better figures, such as knowing your audience and message, using color effectively, and avoiding misleading or overly decorated visualizations.
ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research is reproducible.
To achieve this goal, the whole publishing chain is radically different from other traditional scientific journals. ReScience lives on GitHub where each new implementation of a computational study is made available together with comments, explanations and tests. Each submission takes the form of a pull request that is publicly reviewed and tested in order to guarantee that any researcher can re-use it. If you ever replicated computational results from the literature in your research, ReScience is the perfect place to publish your new implementation.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Assessment and Planning in Educational technology.pptxKavitha Krishnan
In an education system, it is understood that assessment is only for the students, but on the other hand, the Assessment of teachers is also an important aspect of the education system that ensures teachers are providing high-quality instruction to students. The assessment process can be used to provide feedback and support for professional development, to inform decisions about teacher retention or promotion, or to evaluate teacher effectiveness for accountability purposes.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
1. Neural Fields, a Cognitive Approach
Attention, Decision & Plasticity
Nicolas P. Rougier
Inria - Institute of Neurodegenerative Diseases - Bordeaux, France
Computational Neuroscience Course - Nice, April 1st
, 2019
3. Mnemosyne Project
Major cognitive and behavioral functions emerge from adaptive sensorimotor loops
involving the external world, the body and the brain. We study, model and implement
such loops and their interactions toward a fully autonomous behavior. With such a
systemic approach, we mean that such complex systems can only be truly
apprehended as a whole and in natural behavioral situation.
4. What is cognition ?
What are the questions ?
• What are the main forms of cognition ?
• What are the minimal mechanisms ?
• What is/are the right biological levels ?
• How do we identify a satisfactory answer ?
• What is the role of the observer ?
6. The case of Caenorhabditis elegans
Sensory motor behavior
Q. Wen, M.D. Po, E. Hulme, S. Chen, X. Liu, S. Wai Kwok, M. Gershow, A. M. Leifer, V. Butler, C. Fang-Yen, T. Kawano, W.R.
Schafer, G. Whitesides, M. Wyart, D.B. Chklovskii, M. Zhen, A.D.T. Samueln, Proprioceptive Coupling within Motor
Neurons Drives C. elegans Forward Locomotion. Neuron, 2012.
Plasticity, learning and memory
H. Sasakura and I. Mori, Behavioral plasticity, learning, and memory in C. elegans, Current Opinion in Neurobiology,
2013.
Decision making
T.A. Jarrell, Y. Wang, A.E. Bloniarz, C.A. Brittin, M. Xu, J.N. Thomson, D.G. Albertson, D.H. Hall, S.W. Emmons, The
Connectome of a Decision-Making Neural Network, Science, 2012.
5
7. What really matters for cognition ?
Some figures
• C.Elegans → 302 neurons
• Mouse → 71,000,000 neurons
• Rat → 200,000,000 neurons
• Macaque → 6,376,000,000 neurons
• Human → 86,000,000,000 neurons
The human brain in numbers [...], S. Herculano-Houzel,
Frontier in Human Neuroscience, 2009
6
8. What really matters for cognition ?
Specific structures ?
• Neocortex
• Basal Ganglia
• Amygdala
• Frontal cortex
Specific architecture ?
• Connectivity
• Density
• Modularity
• Self-organisation
Specific abilities ?
• To recognize self/others
• To imitate
• To use tools
• To communicate
7
9. Theoretical frameworks
Biological framework
→ Anatomical facts
→ Physiological recordings
→ Experimental data
Cognitive framework
→ Subsumption architecture
→ Embodied cognition
→ Affordances, emotions, etc.
Computational framework
→ Computational paradigm
→ Plasticity & learning
→ Evaluation of models
Philosophical framework
→ Strong AI / Weak AI
→ Emergence
→ Theories of mind
8
10. Where do we start ?
What is/are the right biological level(s) of description ?
• Molecule ? → neurotransmitters
• Organelle ? → axons, dendrites, synapses
• Cell ? → neurons, glial cells
• Tissue ? → brain lobes & structures
• Organ ? → brain
Neural fields, between cells and tissue
→ Complex Dynamic
→ Large population
→ Fast simulation
9
11. Spatio-temporal framework
Continuous space
... stable patterns of neuronal activation ultimately steer the periphery into
dynamical states, from which behavior emerges, without any need to ever ab-
stract from the space-time contiguous processes that embody cognition.
G.Schöner (2008)
Continuous time
The first challenge is that sensori-motor systems evolve continuously in real
time, but cognition can jump from one state to another, that is, from one
thought to another
Spencer et al. (2009)
10
12. Computational Framework
A unit is a set of arbitrary values that can vary along time under the influence of other
units and learning.
• Distributed
→ No supervisor nor executive
• Asynchronous
→ No central clock
• Numerical
→ No symbols
• Adaptive
→ No a priori knowledge
Group Group
Unit
Link
Layer
Link
Value
Value
Value
We want to make sure that emerging properties are those of the model and not those
of the software running the model (see dana.loria.fr).
11
13. Neural Fields
Equation
Let u(x,t) be the membrane potential at position x and time t, f a transfer function and
w a kernel of lateral interaction. The temporal evolution of u(x,t) is given by:
τ
time constant
·
∂u(x, t)
∂t
= −u(x, t)
leak term
+
∫ +∞
−∞
w(x, y) · f(u(y, t))
lateral interactions
dy + I(x)
input
+ h
resting potential
Velocity
Unless specified otherwise, we’ll generally consider infinite speed, i.e. instantaneous
transmission of information.
12
14. Neural Fields
Function
The activity of a neural field can be interpreted from a functional point of view.
Filter Decision Tracking Memory
Measure
Using a specific set of parameters, the activity of a neural field can also be interpreted
as a measure of the input.
Input = 0.25 Input = 0.50 Input = 0.75 Input = 1.00
13
17. On ne voit que ce que l’on regarde
Everyone knows what attention is. It is the possession by the mind, in clear and vivid
form, of one out of what seem several simultaneously possible objects or trains of
thought. Focalization, concentration, of consciousness are of its essence. It implies
withdrawal from some things in order to deal effectively with others, and is a
condition which has a real opposite in the confused, dazed, scatterbrained state
which in French is called distraction, and Zerstreutheit in German.
W. James, (1890)
15
19. The grand illusion of seeing...
• Images on retina are formed
upside-down
• There is a blind spot on the retina
where optic nerves passes
throught it
• Retina receptors are not uniformly
distributed over the surface of the
retina
• Eye is always moving even when
fixing a point (micro-tremors)
17
20. The many visual pathways
The dorsal pathway
• Where or How pathway
• Motion and object locations
The ventral pathway
• What pathway
• Form and object representation
The frontal pathway
• Executive control
• Organization of behavior
• Visual Awareness 32 cortical areas, 10 hierarchical levels
18
22. Attention
Clinical Description (Sohlberg & Mateer, 1989)
Focused to respond discretely to a specific stimuli.
Sustained to maintain a consistent behavioral response
Selective to maintain attention in the face of distractors
Alternating to shift focus of attention
Divided to respond simultaneously to multiple tasks
Cognitive Description
Motor movements preparation, priming, etc.
Sensory auditory, visual, proprioception, etc.
Overt motor response (explicit)
Covert cognitive response (implicit)
Top-down goal driven, bias, etc.
Bottom-Up stimulus driven, pop-out, etc.
20
23. Visual Attention
Spotlight metaphor
Attention is the capacity to select a relevant region of the sensory space
• Topological region of the sensory space → spatial attention
• Featural region of the sensory space → feature oriented attention
• Object as such → object oriented attention
21
28. A computational approach
Theories of Visual Attention
→ Saliency Maps (Itti & Koch, 2001)
Saliency map is a topographically arranged map that represents visual saliency of
a corresponding visual scene.
• Inhibition Of Return (IOR, Posner, 1980)
IOR operates to decrease the likelihood that a previsously inspected item in the
visual scene will be reinspected.
• Premotor Theory of Attention (Rizzolati, 1987)
Attention may derive from weaker activation of same fronto-parietal circuits.
26
30. Saliency maps
• Simple model of visual tracking
• Robustness to noise, distractors and
saliency
• Dynamic & reactive behavior
Saliency
Input
Focus
Competition
28
31. A computational approach
Theories of Visual Attention
• Saliency Maps (Itti & Koch, 2001)
Saliency map is a topographically arranged map that represents visual saliency of
a corresponding visual scene.
→ Inhibition Of Return (IOR, Posner, 1980)
IOR operates to decrease the likelihood that a previsously inspected item in the
visual scene will be reinspected.
• Premotor Theory of Attention (Rizzolati, 1987)
Attention may derive from weaker activation of same fronto-parietal circuits.
29
32. Inhibition of Return
Fixation frame
Uncued Cued
Cue
Time
0 100 200 300 400 500
Cue Target SOA (ms)
350
375
400
425
Reactiontime(ms)
Cued
Uncued
30
34. A computational approach
Theories of Visual Attention
• Saliency Maps (Itti & Koch, 2001)
Saliency map is a topographically arranged map that represents visual saliency of
a corresponding visual scene.
• Inhibition Of Return (IOR, Posner, 1980)
IOR operates to decrease the likelihood that a previsously inspected item in the
visual scene will be reinspected.
→ Premotor Theory of Attention (Rizzolati, 1987)
Attention may derive from weaker activation of same fronto-parietal circuits.
32
36. Visual anticipation
Spatial reference
• Independent of eye movements
• Eye-centered
Action in perception
• To anticipate the consequences of
own actions
• To update working memory
accordingly
Bias/Gating
Saliency
Memory
Anticipation
Update
Saccade
Bias
Focus
Prediction
Input
Competition
Memorization
34
37. A model of covert and overt attention
Search task
The camera is placed in front of a
visual scene and is able to pan and
tilt. The task can be either to look for a
specific orientation or colour or to
look for a conjunction of such features.
Visual Scene
Camera position
Visual Field
Camera image
35
38. A model of covert and overt attention
Memorized
locations
Working
memory Anticipation
Saliency Focus
Switch
Current
memory
Prediction
Gating
Competition
Update Prepared
saccade
Spatial
inhibition
Spatial processing
Orientation
filters
Color filters
Feature maps
Spatial bias
Perceived
features
Target
Feature processing
Motor commands
SwitchMove
Feature based
attention
Spatial
attention
36
39. A model of covert and overt attention
Time
• Feature based attention facilitates processing of relevant features
• Spatial based attention facilitates processing of relevant region
• Working memory prevents to explore already seen location
• Model exhibits both overt and covert attention using same substrate
37
40. Towards the organization of visual behavior
A bottom-up sequential exploratory behavior has emerged based on distributed &
numeric computation but...
From an automated behavior...
• Visual attention can be spatialy or featurally biased
• Most salient stimulus are likely to be attended
• How to circumvent this automated behavior ?
...to a motivated one
• To consider saccadic behavior as a motivated exploration
• To make hypothesis about the world make saccades to try to confirm them
38
43. How a decision is made ?
Equation
Let u(x,t) be the membrane potential at position x and time t, f a transfer function and
w a kernel of lateral interaction. The temporal evolution of u(x,t) is given by:
τ
time constant
·
∂u(x, t)
∂t
= −u(x, t)
leak term
+
∫ +∞
−∞
w(x, y) · f(u(y, t))
lateral interactions
dy + I(x)
input
+ h
resting potential
No decision Noisy decision
40
44. A degenerated neural field
˙x = α × (1 − x) + (x − y) × (1 − x), x > 0
˙y = α × (1 − y) + (y − x) × (1 − y), y > 0
0.0 0.2 0.4 0.6 0.8 1.0
x position
0.0
0.2
0.4
0.6
0.8
1.0
yposition
250 trajectories of a dual particle system (x,y)
41
46. Superior colliculus
Superficial layers
I (SZ) II (SGS) III (SO)
Intermediate layers
IV (SGI) V (SAI)
Deep layers
VI (SGP) VII (SAP)
Brainstem
Retina
LGN
Visual
Associative
Motor/Sensory
Basal ganglia
• I (SZ): stratum zonale
• II(SGS): stratum griseum superficiale
• III (SO): stratum opticum
• IV (SGI): stratum griseum intermediale
• V (SAI): stratum album intermediale
• VI (SGP): stratum griseum profundum
• VII (SAP): stratum album profundum
43
47. Superior colliculus
How are saccades encoded
• Population coding ? sum ? average ?
• What level of precision ?
• What does the receptive fields look like ?
• What is the influence of lesions ?
How decision is made ?
• What if two stimuli are presented ?
• What are the preferred stimuli ?
What is the dynamic ?
• What is the influence of stimulus size, magnitude or position ?
• What is the influence of distractors ?
44
58. Conclusion
Major cognitive and behavioral functions emerge from such sensorimotor loops
involving the external world, the body and the brain. We study, model
Embodiment constrains decision
The very shape of the retina provide an implicit computation that influence the final
decision on where to saccade.
55
61. The Somatosensory System
Donald O. Hebb
• Neurons that fire together wire together
Hubel and Wiesel
• Simple and complex cells (1959)
• Ocular dominance columns (1962)
• Critical period, no plasticity after that period (1963-1965)
Merzenich, Kaas and Rasmusson
• Cortical organization of the primary somatosensory cortex (1981)
• Reorganization of the adult primary somatosensory cortex (1983)
57
63. Plasticity in the Somatosensory System
Area 3b
Topographic organization of somatosensory
cortical area 3b of owl monkeys after dorsal
column transection.
A. Normal somatotopy of the hand representation
B. Complete dorsal column section at cervical
levels deprives the hand representation of all
activating inputs
C. Over the course of weeks, the influence of the
spared inputs expands
59
64. Plasticity in the Somatosensory System
• When, where and how organization occurs in the first place ?
• How representations can be both stable and plastic ?
• How to cope with cortical and/or sensory lesions ?
• Do current computational models give a fair account on cortical plasticity ?
• Are there other mechanisms or structures involved ?
• What is actually represented through cortical activity ?
• What is the role ot the motor-sensory loop ?
60
66. Neural Gas
Efficient VQ...
• Density driven
• Unsupervised learning
• No dead units
but...
• A posteriori topology
• Decreasing learning rate
• Frozen terminal state
• Winner-takes-all algorithm
62
67. Dynamic Self-Organizing maps
To what extent it is possible to have both stable and dynamic representations ?
Dynamic
The model must dynamically adapts itself to the data.
Stability
Model representations must be stable if the input is stable.
Topology
Two physically neighborhood cells mut have similar representations.
63
68. Dynamic Self-Organizing maps
DSOM is a neural map equipped with a structure (a hypercube or hexagonal lattice)
and each neuron i is assigned a fixed position pi in Rq where q is the dimension of
the lattice (usually 1 or 2). The learning process is an iterative process where vectors
v ∈ Ω are sequentially presented to the map with respect to the probability density
function f. For each presented vector v, a winner s ∈ N is determined and all codes
wi from the codebook W are shifted towards v according to
∆wi = ε∥v − wi∥Ω hη(i, s, v) (v − wi)
64
74. Dynamic Neural Fields
Equation
Let u(x,t) be the membrane potential at position x and time t, f a transfer function and
w a kernel of lateral interaction. The temporal evolution of u(x,t) is given by:
τ
time constant
·
∂u(x, t)
∂t
= −u(x, t)
leak term
+
∫ +∞
−∞
w(x − y) · f(u(y, t))
lateral interactions
dy + I(x)
input
+ h
resting potential
Matching property
Input = 0.25 Input = 0.50 Input = 0.75 Input = 1.00
70
75. Dynamic Neural Fields
Dynamic gain modulation
Dynamic gain enforces competition and can further modulate learning.
A
Slice
0.49 0.81
0
1
Discargerate
Slice detail
Trigger threshold
B
Slice
0.53 0.77
0
1
Discargerate
Trigger threshold
Slice detail
C
Slice
0.55 0.75
0
1
Discargerate
Trigger threshold
Slice detail
0.00 1.25 0.00 1.25 0.00 1.25
71
76. Dynamic Neural Fields
Competition
Let u(x,t) be the membrane potential at position x and time t, f a transfer function and
w a kernel of lateral interaction. The temporal evolution of u(x,t) is given by:
τ
time constant
·
∂u(x, t)
∂t
= −u(x, t)
leak term
+
∫ +∞
−∞
wl(x, y) · f(u(y, t))
lateral interactions
dy + I(x)
input
+ h
resting potential
Learning
Learning occurs at every time step.
τ
time constant
·
∂wf(x, t)
∂t
= γ
learning rate
(
s(z, t) − wf(x, t)
)
thalamo-cortical
∫ +∞
−∞
we(x, y)
excitatory lateral
f(u(y, t))dy
Using wl(x, y) = we(x, y) − wi(x, y) = Ke exp
(
−d2
2σ2
e
)
− Ki exp
(
−d2
2σ2
i
)
72
78. Computational model of area 3b
• Neural field promotes competition
• Lateral connections are fixed and
dynamic
• Feed-forwards connections are plastic
• Learning shapes receptive fields
Cortical Layer
Neuron
wf
Input Layer
Stimulus
Receptor
we(x)
wi(x)
74
79. Computational model of area 3b
• Model has been trained on 50000
random samples
• Learning occurs at every time step
• Thalamo-cortical connections have
been shaped
• Receptive fields covers uniformly the
skin patch
• Topology is enforced everywhere
75
80. Computational model of area 3b
Temporal evolution of a receptive field
The shaping of receptive fields occurs through an early expansion stage followed by a
shrinking and a specialization stage.
76
81. Cortical lesion
• 25% of neurons are killed
• 3 types of lesion
• Reorganization in three phases
• Silence
• Expansion
• Shrinkage
• Expansion to non-represented
skin areas.
• Partial recovery
77
83. Sensory deprivation
• 25% of receptors are silenced
• 3 types of lesion
• Reorganization in three phases
• Silence
• Expansion
• Shrinkage
• Migration of receptive fields
• Full recovery
79
88. Some questions received (hypothetical) answers
✓ When, where and how organization occurs in the first place ?
✓ How representations can be both stable and plastic ?
✓ How to cope with cortical and/or sensory lesions ?
✓ Do current computational models give a fair account on cortical plasticity ?
□ Are there other mechanisms or structures involved ?
□ What is actually represented through cortical activity ?
□ What is the role ot the motor-sensory loop ?
84
90. Conclusion
Neural fields
• Powerful modeling tool
• Strong mathematical framework
• Experimental evidences
Cognition
• Still many questions to be addressed
• Embodiment and emergence are key concepts
• Mathematical analysis unlikely
Mathematical solutions do not characterize functional blocks
Filter Decision Tracking Memory
85
91. Is there something like an objective behavior ?
Curious (visual tracking) ?
Saliency
Input
Focus
C
om
petition
Camera
We can connect the model to a pan-tilt camera
such that it follows a given stimulus
Shy (visual avoidance) ?
Saliency
Input
Focus
C
om
petition
Camera
We can connect the model to a pan-tilt camera
such that it looks away from a given stimulus
The actual behavior of the model depends on the links to its body. Ultimately, the
modeler is the one who decide on the behavior.
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92. What is a model ?
Supposons qu’un être (ou une situation) extérieur(e) X présente un comportement énigmatique, et
que nous nous posions à son sujet une (ou plusieurs) question(s). Pour répondre à cette question,
on va s’efforcer de modéliser X, c’est-à-dire, on va construire un objet (réel ou abstrait) M, considéré
comme l’image, l’analogue de X : M sera dit le modèle de X.
R. Thom, Modélisation et scientificité, 1978
Question (Q')
Question (Q)
Being (X)
Analogy
Model (M)
Answer (A')
Answer (A)
To an observer B, an object A* is a model of an object A to the extent that B can use A* to answer
questions that interest him about A.
M. Minsky, Matter, Mind and Models, 1965
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93. Evaluation of models
The standard model (theory)
provides a direct access to the question
MODEL OBSERVER
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94. Evaluation of models
The standard model (theory)
provides a direct access to the question
The computational model
objective mathematical properties
subjective functional interpretations
Brain
MODEL OBSERVER
Brain
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95. Evaluation of models
The standard model (theory)
provides a direct access to the question
The computational model
objective mathematical properties
subjective functional interpretations
The embodied model
behavior through embodiment
quantifiable performances
Brain
Body
MODEL OBSERVER
Brain
Body
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96. Evaluation of models
The standard model (theory)
provides a direct access to the question
The computational model
objective mathematical properties
subjective functional interpretations
The embodied model
behavior through embodiment
quantifiable performances
The cognitive model
observation through interaction
interpretation may depends on our own behavior
Brain
Body
SENSORY/MOTOR
World
MODEL
OBSERVATION
INTERACTION
OBSERVER
Brain
Body
SENSORY/MOTOR
World
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97. All code available on github or my homepage
• http://www.labri.fr/perso/nrougier/
• https://github.com/rougier
If not, mail to nicolas.rougier@inria.fr
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