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Neural representations of compositional structures: representing and manipulating vector spaces with spiking neurons
2011
Connection science
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In particular, we describe methods for constructing spiking networks that can represent and manipulate structured, symbol-like representations. ...
This paper re-examines the question of localist vs. distributed neural representations using a biologically realistic framework based on the central notion of neurons having a preferred direction vector ...
Figure 1 is dependent on the neural model used to convert current into spikes. For this paper, we use the standard Leaky Integrate-and-Fire neuron. 2. ...
doi:10.1080/09540091.2011.571761
fatcat:bigxgbwpmfdkla27bb4sui3tam
Perceptual Spaces: Mathematical Structures to Neural Mechanisms
2013
Journal of Neuroscience
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A central goal of neuroscience is to understand how populations of neurons build and manipulate representations of percepts that provide useful information about the environment. ...
This symposium explores the fundamental properties of these representations and the perceptual spaces in which they are organized. ...
At the top of the hierarchy is familiar Euclidean geometry and its non-Euclidean relatives, which allow representing stimuli as vectors, with well-defined sizes and angles. ...
doi:10.1523/jneurosci.3343-13.2013
pmid:24198350
pmcid:PMC3818541
fatcat:g7o4kys47nfgxmmy6a5hsmyxdq
Structured sequence processing and combinatorial binding: neurobiologically and computationally informed hypotheses
2019
Philosophical Transactions of the Royal Society of London. Biological Sciences
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The model integrates established vector symbolic additive and conjunctive binding operators with neurobiologically plausible oscillatory dynamics, and is compatible with modern spiking neural network simulation ...
The neural mechanisms for segmenting continuous streams of sensory input and establishing representations of dependencies remain largely unknown, as do the transformations and computations occurring between ...
We thank Elizabeth Buffalo, Pascal Fries, Karl Friston, Tim Griffiths, David Poeppel, Mark Stokes and Chris Summerfield for inspiring discussions on complex combinatorial binding. ...
doi:10.1098/rstb.2019.0304
pmid:31840585
pmcid:PMC6939361
fatcat:3arw5gklwvc5jaxrxfhilya6le
Analogical mapping and inference with binary spatter codes and sparse distributed memory
2013
The 2013 International Joint Conference on Neural Networks (IJCNN)
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Vector symbolic architectures (VSAs) are a class of connectionist models for the representation and manipulation of compositional structures, which can be used to model analogy. ...
Analogies require complex, relational representations of learned structures, which is challenging for both symbolic and neurally inspired models. ...
Given that the representation and manipulation of structure is central to analogy and that VSAs can easily represent and manipulate structure there have been multiple applications of VSAs to different ...
doi:10.1109/ijcnn.2013.6706829
dblp:conf/ijcnn/EmruliGS13
fatcat:bm34lczbhbaebobtu2vlz5bxyq
Behavioral decomposition reveals rich encoding structure employed across neocortex
[article]
2022
bioRxiv
pre-print
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Momentary actions, such as rearing or turning, were represented ubiquitously and could be decoded from all sampled structures. ...
However, more elementary and continuous features, such as pose and movement, followed region-specific organization, with neurons in visual and auditory cortices preferentially encoding mutually distinct ...
Widerøe of the Norwegian University of Science and Technology (NTNU) MRI Core Facility and S. Eggen for veterinary oversight. ...
doi:10.1101/2022.02.08.479515
fatcat:rrb22y32yrgghmwbooe34ohnae
Simulating and Predicting Dynamical Systems With Spatial Semantic Pointers
2021
Neural Computation
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While neural networks are highly effective at learning task-relevant representations from data, they typically do not learn representations with the kind of symbolic structure that is hypothesized to support ...
and (2) be integrated with deep neural networks to predict the future of physical trajectories. ...
The SPA provides methods for realizing and manipulating these representations in spiking (and nonspiking) neural networks. ...
doi:10.1162/neco_a_01410
pmid:34310679
fatcat:ebiydkydyvh2hbiboeiyxgev6m
Neural blackboard architectures of combinatorial structures in cognition
2006
Behavioral and Brain Sciences
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These architectures are briefly discussed, together with an example of a combinatorial structure in which the blackboard architectures for language and vision are combined. ...
Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. ...
Reduced vector coding Gayler and Sommer & Kanerva argue for an alternative solution of combinatorial structures in terms of reduced representations in high-dimensional vector spaces. ...
doi:10.1017/s0140525x06009022
pmid:16542539
fatcat:yjti7u4gxnaojk6m6gspdasmka
The Role of Temporal Structure in Human Vision
2005
Behavioral & Cognitive Neuroscience Reviews
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They start with an overview of evidence bearing on temporal acuity of human vision, covering studies dealing with temporal integration and temporal differentiation. ...
The authors conclude with a brief discussion of neurophysiological implications of these results. ...
recreate a usable representation of the composite. ...
doi:10.1177/1534582305276839
pmid:15886401
fatcat:yvuatkje4ngwfi4dvi2kix3evy
Spike sequences and their consequences
2000
Journal of Physiology - Paris
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Spatio-temporal patterns of spikes have an advantage of representing information by their spike composition similar to words of languages. ...
Their hidden dependency structure can be revealed by spike 'sequences', defined as a set of neurons which fire in a specific temporal order with certain delay between successive spikes. ...
The author was supported by The Lady Davis Postdoctoral Fellowship and the Kranzberg Fund at the Hebrew University of Jerusalem. ...
doi:10.1016/s0928-4257(00)01103-7
pmid:11165916
fatcat:3qltafkoxjfydpjoezacxrctvq
The AHA! Experience: Creativity Through Emergent Binding in Neural Networks
2010
Cognitive Science
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Many kinds of creativity result from combination of mental representations. ...
How do people's brains come up with new ideas, theories, technologies, organizations, and aesthetic accomplishments? What neural processes underlie the wonderful AHA! ...
Acknowledgements: Our research has been supported by the Natural Sciences and Engineering Research Council of Canada and SHARC/NET. ...
doi:10.1111/j.1551-6709.2010.01142.x
pmid:21428991
fatcat:rsezlspwqjatpd2tn3bkh2zssq
A geometric framework to predict structure from function in neural networks
[article]
2021
arXiv
pre-print
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The structural connectivity matrix of synaptic weights between neurons is a critical determinant of overall network function, but quantitative links between neural network structure and function are complex ...
Neural computation in biological and artificial networks relies on the nonlinear summation of many inputs. ...
In neural networks, the structure of synaptic connectivity critically shapes the functional responses of neurons [4, 5] , and large-scale techniques for measuring neural network structure and function ...
arXiv:2010.09660v3
fatcat:tajxv5cfhncotgwe2tnkxcinuq
Hybrid preference machines based on inspiration from neuroscience
2002
Cognitive Systems Research
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It is a first hybrid framework which allows a link between spiking neural networks, connectionist preference machines and symbolic finite state machines. ...
In the past, a variety of computational problems have been tackled with different connectionist network approaches. ...
an n-dimensional space. vertically and horizontally, and exploits recurrent That way, a symbolic machine represented a higher, and pulsed neural networks for more neuron-like more abstract representation ...
doi:10.1016/s1389-0417(01)00061-4
fatcat:u3xrqmrdirbztoyvkxi2k3iheu
Spike-based symbolic computations on bit strings and numbers
[article]
2021
bioRxiv
pre-print
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In addition, we present new results which show that surprisingly small spiking neural networks can perform symbolic computations on bit sequences and numbers and even learn such computations using a biologically ...
We review the current state of research on spike-based symbolic computations of this type. ...
Funding: This research was partially supported by the Human Brain Project (Grant Agreement number 785907 and 945539), the SYNCH project (Grant Agreement number 824162) of the European Union, and under ...
doi:10.1101/2021.07.14.452347
fatcat:wy4aynhxrnavnc67higeo3dgqu
A mechanism for the cortical computation of hierarchical linguistic structure
2017
PLoS Biology
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Such redundancy in the cortical and machine signals is indicative of formal and mechanistic alignment between representational structure building and "cortical" oscillations. ...
A single mechanism-using time to encode information across a layered network-generates the kind of (de)compositional representational hierarchy that is crucial for human language and offers a mechanistic ...
The ability to learn structured representations contrasts with current Bayesian models, which assume structured representations a priori or fit them from a specified space of possible representations predefined ...
doi:10.1371/journal.pbio.2000663
pmid:28253256
pmcid:PMC5333798
fatcat:654avixjpnfplpxy3p5auz5ln4
Learning structured representations from experience
[chapter]
2018
The psychology of learning and motivation
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DORA begins with representations of objects as flat feature vectors. ...
In particular, because the model represents relations as weights in feature space and not in terms of any specific invariant properties, it has difficulty with full relational reasoning. ...
doi:10.1016/bs.plm.2018.10.002
fatcat:s25msu3l7ra7dhhsbdg55rtsni
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