From the neuron doctrine to neural networks

By Rafael Yuste. 2015

693 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Plant Science

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Citations in the corpus

Orthogonal representations for robust context-dependent task performance in brains and neural networks (2022, neutral): “The question of whether neural codes are task agnostic or task specific speaks to core problems in neural theory with widespread implications for understanding the coding properties of neurons and neural populations (Yuste, 2015; Saxena and Cunningham, 2019).”
Memory engrams: Recalling the past and imagining the future (2020, background): “, a “grandmother cell” (226, 227)]; instead, these findings suggest that the basic unit of computation in the brain is an engram (228, 229).”
Projections of non-invasive human recordings into state space show unfolding of spontaneous and over-trained choice (2020, methodology): “More precisely, within a given trial, the order of presentation was as follows (Figure 1A): (1) an instruction cue presented for 150ms showed a green and red dot to signal that color was relevant or a left and right arrow to signal that motion was the relevant dimension to attend to for the AS; (2) ”
An Integrated Brain-Machine Interface Platform With Thousands of Channels (2019, neutral): “Most BMI’s have used invasive techniques because themost precise readout of neural representations requires recording single action potentials from neurons in distributed, functionally-linked ensembles [10].”
Towards the neural population doctrine. (2019, background): “In fact, the conﬂict between single neurons and populations of neurons as the perceptual, behavioral, and cognitive unit of computation dates back to the beginning of the 20th century [4,6], with the ﬁrst concrete theories of networks of neurons introduced in the 1940s [7,8].”
Three-dimensional memristor circuits as complex neural networks (2020, background): “Their simplified connections cannot efficiently implement the full topology of often more complex structures in DNNs. Ultimately, it is the connections between neurons, which generate the emergent functional properties and states, that define the capability of a neural network 19 .”
Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework (2016, background): “…responses can reveal a great deal about the neural mechanisms underlying various sensory, motor, and cognitive functions, neural mechanisms often involve the coordinated activity of many neurons whose complex individual dynamics are not easily explained by tuning to experimental parameters [1 – 4].”
Dynamic Reorganization of Neuronal Activity Patterns in Parietal Cortex. (2017, positive): “Together, our work and others suggest that the role of individual neurons could be less important than the overall population activity pattern (Yuste, 2015).”
Grandmother cells and localist representations: a review of current thinking (2017, background): “, 2015), and multiple review articles on single-cell recording studies (Quian Quiroga, 2012, 2016; Quian Quiroga, Fried, & Koch, 2013; Roy, 2012, 2015; Yuste, 2015).”
Neuroscience Needs Behavior: Correcting a Reductionist Bias. (2017, positive): “), identification of these neural states will help us better understand the link between the brain and behavior (although again behavior is at best given backseat status) (Yuste, 2015).”
Simple, scalable proteomic imaging for high-dimensional profiling of intact systems (2015, neutral): “Biological systems are comprised of vast numbers of molecules, cell types, and intricate tissue organizations (Alivisatos et al., 2013; Kasthuri et al., 2015; Yuste, 2015).”
Why neurons mix: high dimensionality for higher cognition. (2016, background): “This fits with a recent update to the neuron doctrine notion, that ensembles, not individual neurons, are the functional unit of the nervous system [18].”
Cortical-limbic circuit dynamics of approach-avoidance conflict in humans (2026, unknown): “Low-frequency theta oscillations coordinate neural activity betweendistalregions 46 – 48 ,facilitatinginformationtransfer 49,50 .”