On memories, neural ensembles and mental flexibility

Pinotsis, Dimitris A.; Brincat, Scott L.; Miller, Earl K.

doi:10.1016/j.neuroimage.2017.05.068

Cited by 12 publications

(77 citation statements)

References 87 publications

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“…In this section, we argue that the theory of neural ensembles and the “embedded ensemble encoding” hypothesis are both critically dependent on electrical signaling (Grewe et al, ; Pinotsis, Brincat, & Miller, ). Researchers accept that electrical signaling is the key mechanism for information transmission and processing in the brain.…”

Section: Part 1: Electrical Signalingmentioning

confidence: 99%

Embedded ensemble encoding hypothesis: The role of the “Prepared” cell

Antic

Hines

Lytton

2018

J of Neuroscience Research

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We here reconsider current theories of neural ensembles in the context of recent discoveries about neuronal dendritic physiology. The key physiological observation is that the dendritic plateau potential produces sustained depolarization of the cell body (amplitude 10-20 mV, duration 200-500 ms). Our central hypothesis is that synaptically-evoked dendritic plateau potentials lead to a prepared state of a neuron that favors spike generation. The plateau both depolarizes the cell toward spike threshold, and provides faster response to inputs through a shortened membrane time constant. As a result, the speed of synaptic-to-action potential (AP) transfer is faster during the plateau phase. Our hypothesis relates the changes from "resting" to "depolarized" neuronal state to changes in ensemble dynamics and in network information flow. The plateau provides the Prepared state (sustained depolarization of the cell body) with a time window of 200-500 ms. During this time, a neuron can tune into ongoing network activity and synchronize spiking with other neurons to provide a coordinated Active state (robust firing of somatic APs), which would permit "binding" of signals through coordination of neural activity across a population. The transient Active ensemble of neurons is embedded in the longer-lasting Prepared ensemble of neurons. We hypothesize that "embedded ensemble encoding" may be an important organizing principle in networks of neurons.

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Section: Part 1: Electrical Signalingmentioning

confidence: 99%

Embedded ensemble encoding hypothesis: The role of the “Prepared” cell

Antic

Hines

Lytton

2018

J of Neuroscience Research

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“…Temporal correlations in turn, have been suggested to link low level visual features into object categories in the context of binding theory (Milner, 1974;Von Der Malsburg, 1994). In earlier work, we have also hypothesized that synchronization might dynamically link category or stimulus selective neurons in decision making and memory tasks (Buschman and Miller, 2009;Buschman et al, 2012;Jones, 2016;Pinotsis et al, 2017).…”

Section: Discussionmentioning

confidence: 81%

Sensory processing and categorization in cortical and deep neural networks

2019

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Many recent advances in artificial intelligence (AI) are rooted in visual neuroscience. However, ideas from more complicated paradigms like decision-making are less used. Although automated decision-making systems are ubiquitous (driverless cars, pilot support systems, medical diagnosis algorithms etc.), achieving human-level performance in decision making tasks is still a challenge. At the same time, these tasks that are hard for AI are easy for humans. Thus, understanding human brain dynamics during these decision-making tasks and modeling them using deep neural networks could improve AI performance. Here we modelled some of the complex neural interactions during a sensorimotor decision making task. We investigated how brain dynamics flexibly represented and distinguished between sensory processing and categorization in two sensory domains: motion direction and color. We used two different approaches for understanding neural representations. We compared brain responses to 1) the geometry of a sensory or category domain (domain selectivity) and 2) predictions from deep neural networks (computation selectivity). Both approaches gave us similar results. This confirmed the validity of our analyses. Using the first approach, we found that neural representations changed depending on context. We then trained deep recurrent neural networks to perform the same tasks as the animals. Using the second approach, we found that computations in different brain areas also changed flexibly depending on context. Color computations appeared to rely more on sensory processing, while motion computations more on abstract categories. Overall, our results shed light to the biological basis of categorization and differences in selectivity and computations in different brain areas. They also suggest a way for studying sensory and categorical representations in the brain: compare

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“…The horizontal axis are electrode number (location) and time (in ms). We assumed that FEF comprised a large number of neural populations (indexed by j=1,…,N=32), that was equal to the number of electrodes we sampled from, see also (Pinotsis et al, 2017). Each of these populations can be thought of as centred around a point ( , ) a a u v on the 2D cortical surface.…”

Section: Resultsmentioning

confidence: 99%

Beyond dimension reduction: Stable electric fields emerge from and allow representational drift

Pinotsis

Miller

2021

Preprint

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It is known that the exact neurons maintaining a given memory (the neural ensemble) change from trial to trial. This raises the question of how the brain achieves stability in the face of this representational drift. Here, we demonstrate that this stability emerges at the level of the electric fields that arise from neural activity. The electric fields, in turn, can act as “guard rails” that funnel higher dimensional variable neural activity along stable lower dimensional routes. We show that electric fields carry information about working memory content. We obtained the latent space associated with each memory. We then confirmed the stability of the electric field by mapping the latent space to different cortical patches (that comprise a neural ensemble) and reconstructing information flow between patches. Stable electric fields can allow latent states to be transferred between brain areas, in accord with modern engram theory.

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On memories, neural ensembles and mental flexibility

Cited by 12 publications

References 87 publications

Embedded ensemble encoding hypothesis: The role of the “Prepared” cell

Embedded ensemble encoding hypothesis: The role of the “Prepared” cell

Sensory processing and categorization in cortical and deep neural networks

Beyond dimension reduction: Stable electric fields emerge from and allow representational drift

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