Nonlinear convergence boosts information coding in circuits with parallel outputs.
Nonlinear convergence boosts information coding in circuits with parallel outputs.
复制标题
非线性收敛增强了具有并行输出的电路中的信息编码。
DOI：
10.1073/pnas.1921882118
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发表时间：
2021
影响因子：
11.1
通讯作者：
Shea-Brown,EricT
中科院分区：
文献类型：
--
作者：
Gutierrez,GabrielleJ;Rieke,Fred;Shea-Brown,EricT
神经回路由收敛和发散连接层以及神经元和突触处的选择性诱导非线性构成。这些元件有可能妨碍电路输入的精确编码。过去的计算研究已经优化了单个神经元的非线性，或网络中的连接权重，以最大限度地提高编码信息，但还没有解决收敛电路结构和非线性响应函数对有效编码的同时影响。我们的方法是比较模型电路与收敛，发散和非线性神经元的不同组合，以发现这些组件之间的相互作用如何影响编码效率。我们发现，一个收敛的电路与发散的平行路径可以编码更多的信息与非线性亚单元比与线性亚单元，尽管压缩损失引起的收敛性和非线性分开考虑时。
Neural circuits are structured with layers of converging and diverging connectivity and selectivity-inducing nonlinearities at neurons and synapses. These components have the potential to hamper an accurate encoding of the circuit inputs. Past computational studies have optimized the nonlinearities of single neurons, or connection weights in networks, to maximize encoded information, but have not grappled with the simultaneous impact of convergent circuit structure and nonlinear response functions for efficient coding. Our approach is to compare model circuits with different combinations of convergence, divergence, and nonlinear neurons to discover how interactions between these components affect coding efficiency. We find that a convergent circuit with divergent parallel pathways can encode more information with nonlinear subunits than with linear subunits, despite the compressive loss induced by the convergence and the nonlinearities when considered separately.