Photoelectrochemical TiO 2 ‐Au‐Nanowire‐Based Motor for Precise Modulation of Single‐Neuron Activities

AbstractDeep neural networks are among the most widely applied machine learning tools showing outstanding performance in a broad range of tasks. We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops. This single-neuron deep neural network comprises only a single nonlinearity and appropriately adjusted modulations of the feedback signals. The network states emerge in time as a temporal unfolding of the neuron’s dynamics. By adjusting the feedback-modulation within the loops, we adapt the network’s connection weights. These connection weights are determined via a back-propagation algorithm, where both the delay-induced and local network connections must be taken into account. Our approach can fully represent standard Deep Neural Networks (DNN), encompasses sparse DNNs, and extends the DNN concept toward dynamical systems implementations. The new method, which we call Folded-in-time DNN (Fit-DNN), exhibits promising performance in a set of benchmark tasks.

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Information diversity in individual auditory cortical neurons is associated with functionally distinct coordinated neuronal ensembles

Scientific Reports ◽

10.1038/s41598-021-83565-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jermyn Z. See ◽

Natsumi Y. Homma ◽

Craig A. Atencio ◽

Vikaas S. Sohal ◽

Christoph E. Schreiner

Keyword(s):

Cortical Neurons ◽

Single Neuron ◽

Receptive Fields ◽

Acoustic Feature ◽

Neuronal Ensembles ◽

Neuron Spike ◽

Information Diversity ◽

Feature Selectivity ◽

Stimulus Features ◽

Time Specific

AbstractNeuronal activity in auditory cortex is often highly synchronous between neighboring neurons. Such coordinated activity is thought to be crucial for information processing. We determined the functional properties of coordinated neuronal ensembles (cNEs) within primary auditory cortical (AI) columns relative to the contributing neurons. Nearly half of AI cNEs showed robust spectro-temporal receptive fields whereas the remaining cNEs showed little or no acoustic feature selectivity. cNEs can therefore capture either specific, time-locked information of spectro-temporal stimulus features or reflect stimulus-unspecific, less-time specific processing aspects. By contrast, we show that individual neurons can represent both of those aspects through membership in multiple cNEs with either high or absent feature selectivity. These associations produce functionally heterogeneous spikes identifiable by instantaneous association with different cNEs. This demonstrates that single neuron spike trains can sequentially convey multiple aspects that contribute to cortical processing, including stimulus-specific and unspecific information.

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