Watermarkable Signature with Computational Function Preserving

2021 ◽  
Author(s):  
Kyohei SUDO ◽  
Keisuke HARA ◽  
Masayuki TEZUKA ◽  
Yusuke YOSHIDA ◽  
Keisuke TANAKA
Keyword(s):  
Computational Function

Watermarkable Signature with Computational Function Preserving

2020 ◽  
pp. 124-144
Author(s):  
Kyohei Sudo ◽  
Masayuki Tezuka ◽  
Keisuke Hara ◽  
Yusuke Yoshida ◽  
Keisuke Tanaka
Keyword(s):  
Computational Function

scholarly journals Event-based pattern detection in active dendrites

2019 ◽  
Author(s):  
Johannes Leugering ◽  
Pascal Nieters ◽  
Gordon Pipa
Keyword(s):  
Single Neuron ◽  
Logic Gate ◽  
Dendritic Tree ◽  
Dendritic Morphology ◽  
Neuron Models ◽  
Plateau Potentials ◽  
Computational Function ◽  
Movement Trajectories ◽  
Timing Variability ◽  
Long Timescales

AbstractMany behavioural tasks require an animal to integrate information on a slow timescale that can exceed hundreds of milliseconds. How this is realized by neurons with membrane time constants on the order of tens of milliseconds or less remains an open question. We show, how the interaction of two kinds of events within the dendritic tree, excitatory postsynaptic potentials and locally generated dendritic plateau potentials, can allow a single neuron to detect specific sequences of spiking input on such slow timescales. Our conceptual model reveals, how the morphology of a neuron’s dendritic tree determines its computational function, which can range from a simple logic gate to the gradual integration of evidence to the detection of complex spatio-temporal spike-sequences on long timescales. As an example, we illustrate in a simulated navigation task how this mechanism can even allow individual neurons to reliably detect specific movement trajectories with high tolerance for timing variability. We relate our results to conclusive findings in neurobiology and discuss implications for both experimental and theoretical neuroscience.Author SummaryThe recognition of patterns that span multiple timescales is a critical function of the brain. This is a conceptual challenge for all neuron models that rely on the passive integration of synaptic inputs and are therefore limited to the rigid millisecond timescale of post-synaptic currents. However, detailed biological measurements recently revealed that single neurons actively generate localized plateau potentials within the dendritic tree that can last hundreds of milliseconds. Here, we investigate single-neuron computation in a model that adheres to these findings but is intentionally simple. Our analysis reveals how plateaus act as memory traces, and their interaction as defined by the dendritic morphology of a neuron gives rise to complex non-linear computation. We demonstrate how this mechanism enables individual neurons to solve difficult, behaviorally relevant tasks that are commonly studied on the network-level, such as the detection of variable input sequences or the integration of evidence on long timescales. We also characterize computation in our model using rate-based analysis tools, demonstrate why our proposed mechanism of dendritic computation cannot be detected under this analysis and suggest an alternative based on plateau timings. The interaction of plateau events in dendritic trees is, according to our argument, an elementary principle of neural computation which implies the need for a fundamental change of perspective on the computational function of neurons.

A New Committee on Arithmetic

1937 ◽  
Vol 30 (7) ◽  
pp. 336-337
Author(s):  
R. L. Morton
Keyword(s):  
Annual Meeting ◽  
Teaching Practices ◽  
Atlantic City ◽  
National Society ◽  
Psychological Function ◽  
Computational Function ◽  
Large Influence ◽  
The Subject ◽  
Arithmetic Instruction ◽  
Research Studies

In February, 1930, the National Society for the Study of Education presented its well known Twenty-ninth Yearbook at the annual meeting which was held that year at Atlantic City. The Twenty-ninth Yearbook contains in some seven hundred pages the report of the Society's Committee on Arithmetic. There is much valuable material in the Twenty-nint h Yearbook. Part I devotes six chapters to “Some Aspects of Moderu Thought on Arithmetic.” Part II reports in 14 chapters a series of research studies in arithmetic. There is no doubt that this yearbook has had a large influence on the construction of courses of study and the writing of textbooks. Also, supervisors and teachers who read the yearbook have reflected its influence in teaching practices. However, as the Reviewing Committee points out in its 29–page Critique, emphasis in the yearbook is largely upon the computational function. Little attent ion is given to other functions of arithmetic instruction, notably the informational function, the sociological function, and the psychological function. Furthermore, the research studies quite naturally include what the Committee was able to collect and do not rover the subject at all completely.

scholarly journals A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning

eNeuro ◽  
2018 ◽  
Vol 5 (2) ◽  
pp. ENEURO.0301-17.2018 ◽  
Author(s):  
David Kappel ◽  
Robert Legenstein ◽  
Stefan Habenschuss ◽  
Michael Hsieh ◽  
Wolfgang Maass
Keyword(s):  
Neural Circuits ◽  
Computational Function

Peeling the Onion of Brain Representations

2019 ◽  
Vol 42 (1) ◽  
pp. 407-432 ◽  
Author(s):  
Nikolaus Kriegeskorte ◽  
Jörn Diedrichsen
Keyword(s):  
Dynamical System ◽  
Adaptive Behavior ◽  
Brain Activity ◽  
Critical Discussion ◽  
Future Directions ◽  
Computational Function ◽  
The World ◽  
And Mathematics ◽  
The Brain ◽  
Representational Models

The brain's function is to enable adaptive behavior in the world. To this end, the brain processes information about the world. The concept of representation links the information processed by the brain back to the world and enables us to understand what the brain does at a functional level. The appeal of making the connection between brain activity and what it represents has been irresistible to neuroscience, despite the fact that representational interpretations pose several challenges: We must define which aspects of brain activity matter, how the code works, and how it supports computations that contribute to adaptive behavior. It has been suggested that we might drop representational language altogether and seek to understand the brain, more simply, as a dynamical system. In this review, we argue that the concept of representation provides a useful link between dynamics and computational function and ask which aspects of brain activity should be analyzed to achieve a representational understanding. We peel the onion of brain representations in search of the layers (the aspects of brain activity) that matter to computation. The article provides an introduction to the motivation and mathematics of representational models, a critical discussion of their assumptions and limitations, and a preview of future directions in this area.

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