scholarly journals A Biologically Plausible Mechanism to Learn Clusters of Neural Activity

2018 ◽  
Author(s):  
Adrianna R. Loback ◽  
Michael J. Berry
Keyword(s):  
Ganglion Cell ◽  
Real Time ◽  
Neural Activity ◽  
Activity Pattern ◽  
Neural Population ◽  
Retinal Ganglion ◽  
Population Activity ◽  
Visual Areas ◽  
Cortical Hierarchy ◽  
Plausible Mechanism

When correlations within a neural population are strong enough, neural activity in early visual areas is organized into a discrete set of clusters. Here, we show that a simple, biologically plausible circuit can learn and then readout in real-time the identity of experimentally measured clusters of retinal ganglion cell population activity. After learning, individual readout neurons develop cluster tuning, meaning that they respond strongly to any neural activity pattern in one cluster and weakly to all other inputs. Different readout neurons specialize for different clusters, and all input clusters can be learned, as long as the number of readout units is mildly larger than the number of input clusters. We argue that this operation can be repeated as signals flow up the cortical hierarchy.

scholarly journals Perception and propagation of activity through the cortical hierarchy is determined by neural variability

2021 ◽  
Author(s):  
James M Rowland ◽  
Thijs L van der Plas ◽  
Matthias Loidolt ◽  
Robert Michael Lees ◽  
Joshua Keeling ◽  
...  
Keyword(s):  
Neural Activity ◽  
Signal To Noise Ratio ◽  
Brain Regions ◽  
Cortical Function ◽  
Population Activity ◽  
Two Photon ◽  
Secondary Somatosensory Cortex ◽  
Cortical Hierarchy ◽  
Robust Transmission ◽  
Neural Variability

The brains of higher organisms are composed of anatomically and functionally distinct regions performing specialised tasks; but regions do not operate in isolation. Orchestration of complex behaviours requires communication between brain regions, but how neural activity dynamics are organised to facilitate reliable transmission is not well understood. We studied this process directly by generating neural activity that propagates between brain regions and drives behaviour, allowing us to assess how populations of neurons in sensory cortex cooperate to transmit information. We achieved this by imaging two hierarchically organised and densely interconnected regions, the primary and secondary somatosensory cortex (S1 and S2) in mice while performing two-photon photostimulation of S1 neurons and assigning behavioural salience to the photostimulation. We found that the probability of perception is determined not only by the strength of the photostimulation signal, but also by the variability of S1 neural activity. Therefore, maximising the signal-to-noise ratio of the stimulus representation in cortex is critical to its continued propagation downstream. Further, we show that propagated, behaviourally salient activity elicits balanced, persistent, and generalised activation of the downstream region. Hence, our work adds to existing understanding of cortical function by identifying how population activity is formatted to ensure robust transmission of information, allowing specialised brain regions to communicate and coordinate behaviour.

scholarly journals Real-time spike sorting platform for high-density extracellular probes with ground-truth validation and drift correction

2017 ◽  
Author(s):  
James J. Jun ◽  
Catalin Mitelut ◽  
Chongxi Lai ◽  
Sergey L. Gratiy ◽  
Costas A. Anastassiou ◽  
...  
Keyword(s):  
Real Time ◽  
Ground Truth ◽  
Neural Population ◽  
Spike Sorting ◽  
Population Activity ◽  
Density Peak ◽  
Precise Control ◽  
Peak Finding ◽  
Single Spike ◽  
Loop Feedback

AbstractElectrical recordings from a large array of electrodes give us access to neural population activity with single-cell, single-spike resolution. These recordings contain extracellular spikes which must be correctly detected and assigned to individual neurons. Despite numerous spike-sorting techniques developed in the past, a lack of high-quality ground-truth datasets hinders the validation of spike-sorting approaches. Furthermore, existing approaches requiring manual corrections are not scalable for hours of recordings exceeding 100 channels. To address these issues, we built a comprehensive spike-sorting pipeline that performs reliably under noise and probe drift by incorporating covariance-based features and unsupervised clustering based on fast density-peak finding. We validated performance of our workflow using multiple ground-truth datasets that recently became available. Our software scales linearly and processes up to 1000-channel recording in real-time using a single workstation. Accurate, real-time spike sorting from large recording arrays will enable more precise control of closed-loop feedback experiments and brain-computer interfaces.

Assessing retinal ganglion cell death and neuroprotective agents using real time imaging

2019 ◽  
Vol 1714 ◽  
pp. 65-72 ◽  
Author(s):  
Azusa Ito ◽  
Satoru Tsuda ◽  
Hiroshi Kunikata ◽  
Asano Toshifumi ◽  
Kota Sato ◽  
...  
Keyword(s):  
Cell Death ◽  
Ganglion Cell ◽  
Real Time ◽  
Retinal Ganglion Cell ◽  
Retinal Ganglion ◽  
Neuroprotective Agents ◽  
Real Time Imaging ◽  
Retinal Ganglion Cell Death ◽  
Ganglion Cell Death

scholarly journals New neural activity patterns emerge with long-term learning

2019 ◽  
Vol 116 (30) ◽  
pp. 15210-15215 ◽  
Author(s):  
Emily R. Oby ◽  
Matthew D. Golub ◽  
Jay A. Hennig ◽  
Alan D. Degenhart ◽  
Elizabeth C. Tyler-Kabara ◽  
...  
Keyword(s):  
Neural Activity ◽  
Activity Patterns ◽  
Causal Link ◽  
Neural Population ◽  
Population Activity ◽  
The Brain ◽  
Neural Population Activity

Learning has been associated with changes in the brain at every level of organization. However, it remains difficult to establish a causal link between specific changes in the brain and new behavioral abilities. We establish that new neural activity patterns emerge with learning. We demonstrate that these new neural activity patterns cause the new behavior. Thus, the formation of new patterns of neural population activity can underlie the learning of new skills.

scholarly journals Neural activity and branching of embryonic retinal ganglion cell dendrites

2012 ◽  
Vol 129 (5-8) ◽  
pp. 125-135 ◽  
Author(s):  
J.C. Hocking ◽  
N.S. Pollock ◽  
J. Johnston ◽  
R.J.A. Wilson ◽  
A. Shankar ◽  
...  
Keyword(s):  
Ganglion Cell ◽  
Retinal Ganglion Cell ◽  
Neural Activity ◽  
Retinal Ganglion

scholarly journals Real-Time Imaging of Retinal Ganglion Cell Apoptosis

Cells ◽  
2018 ◽  
Vol 7 (6) ◽  
pp. 60 ◽  
Author(s):  
Timothy Yap ◽  
Piero Donna ◽  
Melanie Almonte ◽  
Maria Cordeiro
Keyword(s):  
Ganglion Cell ◽  
Real Time ◽  
Retinal Ganglion Cell ◽  
Cell Apoptosis ◽  
Retinal Ganglion ◽  
Real Time Imaging

scholarly journals Representation learning for neural population activity with Neural Data Transformers

2021 ◽  
Author(s):  
Joel Ye ◽  
Chethan Pandarinath
Keyword(s):  
Real Time ◽  
Representation Learning ◽  
Neural Population ◽  
Dynamical Structure ◽  
Population Activity ◽  
Neural Data ◽  
Reaching Task ◽  
Explicit Dynamics ◽  
Real Time Applications ◽  
Neural Population Activity

AbstractNeural population activity is theorized to reflect an underlying dynamical structure. This structure can be accurately captured using state space models with explicit dynamics, such as those based on recurrent neural networks (RNNs). However, using recurrence to explicitly model dynamics necessitates sequential processing of data, slowing real-time applications such as brain-computer interfaces. Here we introduce the Neural Data Transformer (NDT), a non-recurrent alternative. We test the NDT’s ability to capture autonomous dynamical systems by applying it to synthetic datasets with known dynamics and data from monkey motor cortex during a reaching task well-modeled by RNNs. The NDT models these datasets as well as state-of-the-art recurrent models. Further, its non-recurrence enables 3.9ms inference, well within the loop time of real-time applications and more than 6 times faster than recurrent baselines on the monkey reaching dataset. These results suggest that an explicit dynamics model is not necessary to model autonomous neural population dynamics.Codegithub.com/snel-repo/neural-data-transformers.

scholarly journals Correlations enhance the behavioral readout of neural population activity in association cortex

2020 ◽  
Author(s):  
Martina Valente ◽  
Giuseppe Pica ◽  
Caroline A. Runyan ◽  
Ari S. Morcos ◽  
Christopher D. Harvey ◽  
...  
Keyword(s):  
Neural Activity ◽  
Posterior Parietal Cortex ◽  
Sensory Information ◽  
Neural Population ◽  
Association Cortex ◽  
Perceptual Discrimination ◽  
Imaging Data ◽  
Stimulus Information ◽  
Population Activity ◽  
Behavioral Choice

The spatiotemporal structure of activity in populations of neurons is critical for accurate perception and behavior. Experimental and theoretical studies have focused on “noise” correlations – trial-to-trial covariations in neural activity for a given stimulus – as a key feature of population activity structure. Much work has shown that these correlations limit the stimulus information encoded by a population of neurons, leading to the widely-held prediction that correlations are detrimental for perceptual discrimination behaviors. However, this prediction relies on an untested assumption: that the neural mechanisms that read out sensory information to inform behavior depend only on a population’s total stimulus information independently of how correlations constrain this information across neurons or time. Here we make the critical advance of simultaneously studying how correlations affect both the encoding and the readout of sensory information. We analyzed calcium imaging data from mouse posterior parietal cortex during two perceptual discrimination tasks. Correlations limited the ability to encode stimulus information, but (seemingly paradoxically) correlations were higher when mice made correct choices than when they made errors. On a single-trial basis, a mouse’s behavioral choice depended not only on the stimulus information in the activity of the population as a whole, but unexpectedly also on the consistency of information across neurons and time. Because correlations increased information consistency, sensory information was more efficiently converted into a behavioral choice in the presence of correlations. Given this enhanced-by-consistency readout, we estimated that correlations produced a behavioral benefit that compensated or overcame their detrimental information-limiting effects. These results call for a re-evaluation of the role of correlated neural activity, and suggest that correlations in association cortex can benefit task performance even if they decrease sensory information.

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