Simon Stringer | |
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| Alma mater | BSc University of Kent PhD University of Reading |
| Scientific career | |
| Fields | Theoretical Neuroscience Computational Neuroscience Artificial Intelligence |
| Institutions | University of Oxford |
| Doctoral advisor | Nancy K. Nichols |
| Website | www |
Simon Stringer is a departmental lecturer, [1] Director of the Oxford Centre for Theoretical Neuroscience and Artificial Intelligence, and Editor-in-Chief of Network: Computation in Neural Systems [2] published by Taylor & Francis.
Stringer and his research group develop biological computer simulations [3] of the neuronal mechanisms underpinning various areas of brain function, including visual object recognition, spatial processing and navigation, motor function, language and consciousness.
In particular, the study published in Psychological Review [4] and Interface Focus 2018, [5] the Royal Society's cross-disciplinary journal, proposes a novel approach to solve the Binding problem. Spiking neural network simulations [6] of the primate ventral visual system have shown the gradual emergence of a subpopulation of neurons, called polychronous neuronal groups (PNGs), that exhibits regularly repeating spatiotemporal patterns of spikes. The underlying phenomenon of these characteristic patterns of neural activity is known as polychronization. [7]
The main point is that within these PNGs exist neurons, called binding neurons. Binding neurons learn to represent the hierarchical binding relationships between lower and higher level visual features in the hierarchy of visual primitives, at every spatial scale and across the entire visual field. This observation is consistent with the hierarchical nature of primate vision proposed by the two neuroscientists John Duncan and Glyn W. Humphreys almost thirty years ago. [8]
Furthermore, this proposed mechanism for solving the binding problem suggests that information about visual features at every spatial scale, including the binding relations between these features, would be projected upwards to the higher layers of the network, where spatial information would be available for readout by later brain systems to guide behavior. This mechanism has been called the holographic principle.
These feature binding representations are at the core of the capacity of the visual brain to perceive and make sense of its visuospatial world and of the consciousness itself. This finding represents an advancement towards the future development of artificial general intelligence and machine consciousness. [9] According to Stringer:
Today’s machines are unable to perceive and comprehend their working environment in the same rich semantic way as the human brain. By incorporating these biological details into our models[...] will allow computers to begin to make sense of their visuospatial world in the same way as the [human] brain. [10] [11]
Cognitive science is the interdisciplinary, scientific study of the mind and its processes. It examines the nature, the tasks, and the functions of cognition. Mental faculties of concern to cognitive scientists include language, perception, memory, attention, reasoning, and emotion; to understand these faculties, cognitive scientists borrow from fields such as linguistics, psychology, artificial intelligence, philosophy, neuroscience, and anthropology. The typical analysis of cognitive science spans many levels of organization, from learning and decision to logic and planning; from neural circuitry to modular brain organization. One of the fundamental concepts of cognitive science is that "thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures."
Computational neuroscience is a branch of neuroscience which employs mathematics, computer science, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system.
Neuroethology is the evolutionary and comparative approach to the study of animal behavior and its underlying mechanistic control by the nervous system. It is an interdisciplinary science that combines both neuroscience and ethology. A central theme of neuroethology, which differentiates it from other branches of neuroscience, is its focus on behaviors that have been favored by natural selection rather than on behaviors that are specific to a particular disease state or laboratory experiment.
The consciousness and binding problem is the problem of how objects, background, and abstract or emotional features are combined into a single experience.
Christof Koch is a German-American neurophysiologist and computational neuroscientist best known for his work on the neural basis of consciousness. He was the president and chief scientist of the Allen Institute for Brain Science in Seattle. He remains at the Institute as a Meritorious Investigator. He is also the Chief Scientist of the Tiny Blue Dot Foundation in Santa Monica, that funds research meant to alleviate suffering, anxiety and other forms of distress in all people.
A gamma wave or gamma rhythm is a pattern of neural oscillation in humans with a frequency between 25 and 140 Hz, the 40 Hz point being of particular interest. Gamma rhythms are correlated with large-scale brain network activity and cognitive phenomena such as working memory, attention, and perceptual grouping, and can be increased in amplitude via meditation or neurostimulation. Altered gamma activity has been observed in many mood and cognitive disorders such as Alzheimer's disease, epilepsy, and schizophrenia.
A neural circuit is a population of neurons interconnected by synapses to carry out a specific function when activated. Multiple neural circuits interconnect with one another to form large scale brain networks.
Neural oscillations, or brainwaves, are rhythmic or repetitive patterns of neural activity in the central nervous system. Neural tissue can generate oscillatory activity in many ways, driven either by mechanisms within individual neurons or by interactions between neurons. In individual neurons, oscillations can appear either as oscillations in membrane potential or as rhythmic patterns of action potentials, which then produce oscillatory activation of post-synaptic neurons. At the level of neural ensembles, synchronized activity of large numbers of neurons can give rise to macroscopic oscillations, which can be observed in an electroencephalogram. Oscillatory activity in groups of neurons generally arises from feedback connections between the neurons that result in the synchronization of their firing patterns. The interaction between neurons can give rise to oscillations at a different frequency than the firing frequency of individual neurons. A well-known example of macroscopic neural oscillations is alpha activity.
A neuronal ensemble is a population of nervous system cells involved in a particular neural computation.
Neural binding is the neuroscientific aspect of what is commonly known as the binding problem: the interdisciplinary difficulty of creating a comprehensive and verifiable model for the unity of consciousness. "Binding" refers to the integration of highly diverse neural information in the forming of one's cohesive experience. The neural binding hypothesis states that neural signals are paired through synchronized oscillations of neuronal activity that combine and recombine to allow for a wide variety of responses to context-dependent stimuli. These dynamic neural networks are thought to account for the flexibility and nuanced response of the brain to various situations. The coupling of these networks is transient, on the order of milliseconds, and allows for rapid activity.
Sensory neuroscience is a subfield of neuroscience which explores the anatomy and physiology of neurons that are part of sensory systems such as vision, hearing, and olfaction. Neurons in sensory regions of the brain respond to stimuli by firing one or more nerve impulses following stimulus presentation. How is information about the outside world encoded by the rate, timing, and pattern of action potentials? This so-called neural code is currently poorly understood and sensory neuroscience plays an important role in the attempt to decipher it. Looking at early sensory processing is advantageous since brain regions that are "higher up" contain neurons which encode more abstract representations. However, the hope is that there are unifying principles which govern how the brain encodes and processes information. Studying sensory systems is an important stepping stone in our understanding of brain function in general.
Neural coding is a neuroscience field concerned with characterising the hypothetical relationship between the stimulus and the neuronal responses, and the relationship among the electrical activities of the neurons in the ensemble. Based on the theory that sensory and other information is represented in the brain by networks of neurons, it is believed that neurons can encode both digital and analog information.
Synaptic pruning, a phase in the development of the nervous system, is the process of synapse elimination that occurs between early childhood and the onset of puberty in many mammals, including humans. Pruning starts near the time of birth and continues into the late-20s. During the pruning of a synapse, both the axon and the dendrite decay and die off. Synaptic pruning was traditionally considered to be complete by the time of sexual maturation, but MRI studies have discounted this idea.
Spiking neural networks (SNNs) are artificial neural networks (ANN) that more closely mimic natural neural networks. In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not transmit information at each propagation cycle, but rather transmit information only when a membrane potential—an intrinsic quality of the neuron related to its membrane electrical charge—reaches a specific value, called the threshold. When the membrane potential reaches the threshold, the neuron fires, and generates a signal that travels to other neurons which, in turn, increase or decrease their potentials in response to this signal. A neuron model that fires at the moment of threshold crossing is also called a spiking neuron model.
Hierarchical temporal memory (HTM) is a biologically constrained machine intelligence technology developed by Numenta. Originally described in the 2004 book On Intelligence by Jeff Hawkins with Sandra Blakeslee, HTM is primarily used today for anomaly detection in streaming data. The technology is based on neuroscience and the physiology and interaction of pyramidal neurons in the neocortex of the mammalian brain.
The neural correlates of consciousness (NCC) are the minimal set of neuronal events and mechanisms sufficient for the occurrence of the mental states to which they are related. Neuroscientists use empirical approaches to discover neural correlates of subjective phenomena; that is, neural changes which necessarily and regularly correlate with a specific experience. The set should be minimal because, under the materialist assumption that the brain is sufficient to give rise to any given conscious experience, the question is which of its components are necessary to produce it.
Models of neural computation are attempts to elucidate, in an abstract and mathematical fashion, the core principles that underlie information processing in biological nervous systems, or functional components thereof. This article aims to provide an overview of the most definitive models of neuro-biological computation as well as the tools commonly used to construct and analyze them.
The Dehaene–Changeux model (DCM), also known as the global neuronal workspace, or global cognitive workspace model, is a part of Bernard Baars's global workspace model for consciousness.
The network of the human nervous system is composed of nodes that are connected by links. The connectivity may be viewed anatomically, functionally, or electrophysiologically. These are presented in several Wikipedia articles that include Connectionism, Biological neural network, Artificial neural network, Computational neuroscience, as well as in several books by Ascoli, G. A. (2002), Sterratt, D., Graham, B., Gillies, A., & Willshaw, D. (2011), Gerstner, W., & Kistler, W. (2002), and Rumelhart, J. L., McClelland, J. L., and PDP Research Group (1986) among others. The focus of this article is a comprehensive view of modeling a neural network. Once an approach based on the perspective and connectivity is chosen, the models are developed at microscopic, mesoscopic, or macroscopic (system) levels. Computational modeling refers to models that are developed using computing tools.
Wulfram Gerstner is a German and Swiss computational neuroscientist. His research focuses on neural spiking patterns in neural networks, and their connection to learning, spatial representation and navigation. Since 2006 Gerstner has been a full professor of Computer Science and Life Sciences at École Polytechnique Fédérale de Lausanne (EPFL), where he also serves as a Director of the Laboratory of Computational Neuroscience.
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