From circuits to behavior: a bridge too far?

@article{Carandini2012FromCT,
  title={From circuits to behavior: a bridge too far?},
  author={Matteo Carandini},
  journal={Nature Neuroscience},
  year={2012},
  volume={15},
  pages={507-509}
}
  • M. Carandini
  • Published 27 March 2012
  • Biology, Psychology
  • Nature Neuroscience
Neuroscience seeks to understand how neural circuits lead to behavior. However, the gap between circuits and behavior is too wide. An intermediate level is one of neural computations, which occur in individual neurons and populations of neurons. Some computations seem to be canonical: repeated and combined in different ways across the brain. To understand neural computations, we must record from a myriad of neurons in multiple brain regions. Understanding computation guides research in the… 

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References

SHOWING 1-10 OF 40 REFERENCES
Neuroscience: Towards functional connectomics
TLDR
Using a combination of functional imaging and three-dimensional serial electron-microscopic reconstruction at an unprecedented scale, two groups present detailed representations of the connectivity of single cells in the mouse visual system.
Computational Neuroscience
TLDR
It is argued, and examples are provided to show, that computational neuroscience is an essential ingredient for a complete picture of perceptual, motor, and cognitive function.
Recurrent excitation in neocortical circuits
TLDR
How populations of neurons in cat visual cortex can use excitatory feedback, characterized as an effective "network conductance", to amplify their feedforward input signals is described and how neuronal discharge can be kept proportional to stimulus strength despite strong, recurrent connections that threaten to cause runaway excitation is demonstrated.
Large-scale recording of neuronal ensembles
TLDR
Large-scale recordings from neuronal ensembles now offer the opportunity to test competing theoretical frameworks and require further development of the neuron–electrode interface, automated and efficient spike-sorting algorithms for effective isolation and identification of single neurons, and new mathematical insights for the analysis of network properties.
Similar network activity from disparate circuit parameters
TLDR
It is found that virtually indistinguishable network activity can arise from widely disparate sets of underlying mechanisms, suggesting that there could be considerable animal-to-animal variability in many of the parameters that control network activity.
The Blue Brain Project
TLDR
It is argued that the time is right to begin assimilating the wealth of data that has been accumulated over the past century and start building biologically accurate models of the brain from first principles to aid the understanding of brain function and dysfunction.
Do We Know What the Early Visual System Does?
TLDR
Research is progressing with the goals of defining a single “standard model” for each stage of the visual pathway and testing the predictive power of these models on the responses to movies of natural scenes, which would be an invaluable guide for understanding the underlying biophysical and anatomical mechanisms and relating neural responses to visual perception.
Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series)
TLDR
This work presents simplified models of individual neurons, and unconventional coupling, of action-potential generation and phase space analysis of neuronal excitability in response to the Hodgkin-Huxley model.
...
1
2
3
4
...