A digital methodology integrating experimental and theoretical neuroscience

2003 ◽  
Author(s):  
R.K. Snider ◽  
A.J. Lukes ◽  
Y. Zhu ◽  
J.P. Miller
Keyword(s):  
Theoretical Neuroscience

What Does Theoretical Neuroscience Have to Offer the Study of Behavioral Development?

2007 ◽  
pp. 320-361 ◽  
Author(s):  
John P. Spencer ◽  
Vanessa R. Simmering ◽  
Anne R. Schutte ◽  
Gregor Schöner
Keyword(s):  
Behavioral Development ◽  
Theoretical Neuroscience

scholarly journals Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1330
Author(s):  
Rodrigo Cofré ◽  
Cesar Maldonado ◽  
Bruno Cessac
Keyword(s):  
Gibbs Measures ◽  
Thermodynamic Formalism ◽  
Maximum Entropy Principle ◽  
Mathematical Framework ◽  
Neuronal Dynamics ◽  
Inference Procedure ◽  
Open Problems ◽  
Theoretical Neuroscience ◽  
Entropy Principle ◽  
Operational Tool

The Thermodynamic Formalism provides a rigorous mathematical framework for studying quantitative and qualitative aspects of dynamical systems. At its core, there is a variational principle that corresponds, in its simplest form, to the Maximum Entropy principle. It is used as a statistical inference procedure to represent, by specific probability measures (Gibbs measures), the collective behaviour of complex systems. This framework has found applications in different domains of science. In particular, it has been fruitful and influential in neurosciences. In this article, we review how the Thermodynamic Formalism can be exploited in the field of theoretical neuroscience, as a conceptual and operational tool, in order to link the dynamics of interacting neurons and the statistics of action potentials from either experimental data or mathematical models. We comment on perspectives and open problems in theoretical neuroscience that could be addressed within this formalism.

scholarly journals Editorial: Linking experimental and computational connectomics

2019 ◽  
Vol 3 (4) ◽  
pp. 902-904
Author(s):  
Alexander Peyser ◽  
Sandra Diaz Pier ◽  
Wouter Klijn ◽  
Abigail Morrison ◽  
Jochen Triesch
Keyword(s):  
High Performance Computing ◽  
In Silico ◽  
High Performance ◽  
Large Scale ◽  
Generative Models ◽  
Anatomical Structure ◽  
Global Function ◽  
Theoretical Neuroscience ◽  
New Generation ◽  
Performance Computing

Large-scale in silico experimentation depends on the generation of connectomes beyond available anatomical structure. We suggest that linking research across the fields of experimental connectomics, theoretical neuroscience, and high-performance computing can enable a new generation of models bridging the gap between biophysical detail and global function. This Focus Feature on ”Linking Experimental and Computational Connectomics” aims to bring together some examples from these domains as a step toward the development of more comprehensive generative models of multiscale connectomes.

scholarly journals Theory and Simulation in Neuroscience

Science ◽  
2012 ◽  
Vol 338 (6103) ◽  
pp. 60-65 ◽  
Author(s):  
Wulfram Gerstner ◽  
Henning Sprekeler ◽  
Gustavo Deco
Keyword(s):  
Mathematical Analysis ◽  
Conceptual Models ◽  
Top Down ◽  
Bottom Up ◽  
Theoretical Neuroscience ◽  
Behavioral Target

Modeling work in neuroscience can be classified using two different criteria. The first one is the complexity of the model, ranging from simplified conceptual models that are amenable to mathematical analysis to detailed models that require simulations in order to understand their properties. The second criterion is that of direction of workflow, which can be from microscopic to macroscopic scales (bottom-up) or from behavioral target functions to properties of components (top-down). We review the interaction of theory and simulation using examples of top-down and bottom-up studies and point to some current developments in the fields of computational and theoretical neuroscience.

scholarly journals Memory, modelling and Marr: a commentary on Marr (1971) ‘Simple memory: a theory of archicortex’

2015 ◽  
Vol 370 (1666) ◽  
pp. 20140383 ◽  
Author(s):  
D. J. Willshaw ◽  
P. Dayan ◽  
R. G. M. Morris
Keyword(s):  
Brain Structure ◽  
Royal Society ◽  
Partial Information ◽  
Hippocampal Formation ◽  
Theoretical Work ◽  
Theoretical Neuroscience ◽  
Memory Modelling ◽  
Contemporary Context ◽  
350Th Anniversary

David Marr's theory of the archicortex, a brain structure now more commonly known as the hippocampus and hippocampal formation, is an epochal contribution to theoretical neuroscience. Addressing the problem of how information about 10 000 events could be stored in the archicortex during the day so that they can be retrieved using partial information and then transferred to the neocortex overnight, the paper presages a whole wealth of later empirical and theoretical work, proving impressively prescient. Despite this impending success, Marr later apparently grew dissatisfied with this style of modelling, but he went on to make seminal suggestions that continue to resonate loudly throughout the field of theoretical neuroscience. We describe Marr's theory of the archicortex and his theory of theories, setting them into their original and a contemporary context, and assessing their impact. This commentary was written to celebrate the 350th anniversary of the journal Philosophical Transactions of the Royal Society .

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