Neural Assembly Computing

Communication in neural circuits across the cortex is thought to be mediated by spontaneous temporally organized patterns of population activity lasting ~50 –200 ms. Closed-loop manipulations have the unique power to reveal direct and causal links between such patterns and their contribution to cognition. Current brain–computer interfaces, however, are not designed to interpret multi-neuronal spiking patterns at the millisecond timescale. To bridge this gap, we developed a system for classifying ensemble patterns in a closed-loop setting and demonstrated its application in the online identification of hippocampal neuronal replay sequences in the rat. Our system decodes multi-neuronal patterns at 10 ms resolution, identifies within 50 ms experience-related patterns with over 70% sensitivity and specificity, and classifies their content with 95% accuracy. This technology scales to high-count electrode arrays and will help to shed new light on the contribution of internally generated neural activity to coordinated neural assembly interactions and cognition.

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An Excitatory Neural Assembly Encodes Working Memory in the Prefrontal Cortex

SSRN Electronic Journal ◽

10.2139/ssrn.3155957 ◽

2018 ◽

Author(s):

Yonglu Tian ◽

Chaojuan Yang ◽

Yaxuan Cui ◽

Feng Su ◽

Yongjie Wang ◽

...

Keyword(s):

Working Memory ◽

Prefrontal Cortex ◽

Neural Assembly

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More on Gnostic Units and Cortical Columns

Sherrington's Loom ◽

10.1093/oso/9780190936549.003.0012 ◽

2019 ◽

pp. 200-225

Author(s):

Alan J. McComas

Keyword(s):

Single Neuron ◽

Building Blocks ◽

Cortical Function ◽

Neural Structure ◽

Cortical Columns ◽

Electrophysiological Recordings ◽

Neural Assembly ◽

The Face ◽

The Subject

This chapter returns to the subject of gnostic units discussed in Chapter 9, as well cortical columns, both of which form the building blocks of cortical function. Gnostic units are used to describe a neural assembly having knowledge (information). The chapter first expounds on gnostic units and how they relate to the concept/grandmother cells already discussed previously. It then goes on to consider the type of neural structure, which might correspond to a gnostic unit. At the simplest level, electrophysiological recordings have shown that a single neuron could be regarded as a gnostic unit. From here, the chapter conceives of a hierarchy of analyzers in the form of cortical columns. At the highest level will be the column(s) specific for a particular face or object—these cells will fire, and the face or object will be recognized by the conscious brain.

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Detecting spatiotemporal firing patterns among simultaneously recorded single neurons

Journal of Neurophysiology ◽

10.1152/jn.1988.60.3.909 ◽

1988 ◽

Vol 60 (3) ◽

pp. 909-924 ◽

Cited By ~ 227

Author(s):

M. Abeles ◽

G. L. Gerstein

Keyword(s):

High Frequency ◽

Firing Pattern ◽

Spike Trains ◽

Confidence Limits ◽

Large Excess ◽

Neural Assembly ◽

Repeated Pattern ◽

Expected Values ◽

Good Agreement ◽

Repeating Patterns

1. A particular firing pattern among simultaneously observed neurons represents a particular sequence of activity. If any multineuron pattern repeats significantly more than expected by chance, we may be observing a repeated state of a neural assembly as it processes similar units of information. 2. We present here an algorithm that rapidly finds all single or multineuron patterns that repeat two or more times within a block of data, as well as equations for calculating the number of patterns of given length and repetition that would be expected. The complexity of patterns for which it is practical to compute expected numbers is three to six spikes (inclusive). 3. Confidence limits are based on these expected numbers of patterns, so that is possible to identify groups of patterns that are worthy of further analysis. 4. These methods are tested against simulated multineuron data that has various types of known nonstationarities, with good agreement between observed and expected values. 5. Application to real spike trains shows a large excess of observed repeating patterns, of which some, but not all, are shown to be due to bursts of high frequency firing. 6. It should be possible to apply the new method as a filter in real time in order to search for an association between repeated pattern events and externally observable events (stimulus, behavior, etc.). Any repeated pattern events which cannot be so associated may represent a new indicator of internal events in the nervous system.

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Neural assembly generation by selective connection weight updating

The 2010 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2010.5596716 ◽

2010 ◽

Cited By ~ 4

Author(s):

Yoshitsugu Kakemoto ◽

Shinichi Nakasuka

Keyword(s):

Connection Weight ◽

Neural Assembly

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Neural assembly models derived through nano-scale measurements.

10.2172/993899 ◽

2009 ◽

Author(s):

Hongyou Fan ◽

Catherine Branda ◽

Richard Louis Schiek ◽

Christina E. Warrender ◽

James Chris Forsythe

Keyword(s):

Nano Scale ◽

Neural Assembly ◽

Assembly Models

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Evaluating Information Transfer Between Auditory Cortical Neurons

Journal of Neurophysiology ◽

10.1152/jn.01106.2006 ◽

2007 ◽

Vol 97 (3) ◽

pp. 2533-2543 ◽

Cited By ~ 127

Author(s):

Boris Gourévitch ◽

Jos J. Eggermont

Keyword(s):

Auditory Cortex ◽

Cortical Neurons ◽

Information Transfer ◽

Cross Correlation ◽

Past History ◽

Temporal Integration ◽

Acoustic Stimulus ◽

Primary Auditory Cortex ◽

Transfer Entropy ◽

Neural Assembly

Transfer entropy, presented as a new tool for investigating neural assemblies, quantifies the fraction of information in a neuron found in the past history of another neuron. The asymmetry of the measure allows feedback evaluations. In particular, this tool has potential applications in investigating windows of temporal integration and stimulus-induced modulation of firing rate. Transfer entropy is also able to eliminate some effects of common history in spike trains and obtains results that are different from cross-correlation. The basic transfer entropy properties are illustrated with simulations. The information transfer through a network of 16 simultaneous multiunit recordings in cat's auditory cortex was examined for a large number of acoustic stimulus types. Application of the transfer entropy to a large database of multiple single-unit activity in cat's primary auditory cortex revealed that most windows of temporal integration found during spontaneous activity range between 2 and 15 ms. The normalized transfer entropy shows similarities and differences with the strength of cross-correlation; these form the basis for revisiting the neural assembly concept.

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