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• W4294920334 endingPage "3053" @default.
• W4294920334 startingPage "3037" @default.
• W4294920334 abstract "Abstract Due to the staggering complexity of the brain and its neural circuitry, neuroscientists rely on the analysis of mathematical models to elucidate its function. From Hodgkin and Huxley's detailed description of the action potential in 1952 to today, new theories and increasing computational power have opened up novel avenues to study how neural circuits implement the computations that underlie behaviour. Computational neuroscientists have developed many models of neural circuits that differ in complexity, biological realism or emergent network properties. With recent advances in experimental techniques for detailed anatomical reconstructions or large‐scale activity recordings, rich biological data have become more available. The challenge when building network models is to reflect experimental results, either through a high level of detail or by finding an appropriate level of abstraction. Meanwhile, machine learning has facilitated the development of artificial neural networks, which are trained to perform specific tasks. While they have proven successful at achieving task‐oriented behaviour, they are often abstract constructs that differ in many features from the physiology of brain circuits. Thus, it is unclear whether the mechanisms underlying computation in biological circuits can be investigated by analysing artificial networks that accomplish the same function but differ in their mechanisms. Here, we argue that building biologically realistic network models is crucial to establishing causal relationships between neurons, synapses, circuits and behaviour. More specifically, we advocate for network models that consider the connectivity structure and the recorded activity dynamics while evaluating task performance. image" @default.
• W4294920334 created "2022-09-08" @default.
• W4294920334 creator A5016976066 @default.
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• W4294920334 creator A5037266643 @default.
• W4294920334 creator A5042732350 @default.
• W4294920334 creator A5055262448 @default.
• W4294920334 date "2022-09-25" @default.
• W4294920334 modified "2023-09-26" @default.
• W4294920334 title "How to incorporate biological insights into network models and why it matters" @default.
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