scholarly journals Correlated variability in primate superior colliculus depends on functional class

2021 ◽  
Author(s):  
Leor N Katz ◽  
Gongchen Yu ◽  
James P Herman ◽  
Richard J Krauzlis
Keyword(s):  
Working Memory ◽  
Superior Colliculus ◽  
Functional Class ◽  
Population Coding ◽  
Spike Count ◽  
Behavioral Task ◽  
Primate Brain ◽  
Functional Classes ◽  
Saccade Task ◽  
Neuronal Tuning

Correlated variability (spike count correlations, rSC) in a population of neurons can constrain how information is read out, depending on behavioral task and neuronal tuning. Here we tested whether rSC also depends on neuronal functional class. We recorded from populations of neurons in macaque superior colliculus (SC), a structure that contains well-defined functional classes. We found that during a guided saccade task, different classes of neurons exhibited differing degrees of rSC. "Delay class" neurons displayed the highest rSC, especially during the delay epoch of saccade tasks that relied on working memory. This was only present among Delay class neurons within the same hemisphere. The dependence of rSC on functional class indicates that subpopulations of SC neurons occupy distinct circuit niches with distinct inputs. Such subpopulations should be accounted for differentially when attempting to model or infer population coding principles in the SC, or elsewhere in the primate brain.

scholarly journals A test of spatial temporal decoding mechanisms in the superior colliculus

2012 ◽  
Vol 107 (9) ◽  
pp. 2442-2452 ◽  
Author(s):  
Husam A. Katnani ◽  
A. J. Van Opstal ◽  
Neeraj J. Gandhi
Keyword(s):  
Nervous System ◽  
Motor Control ◽  
Eye Movements ◽  
Superior Colliculus ◽  
Motor Behavior ◽  
Saccadic Eye Movements ◽  
Population Coding ◽  
Population Activity ◽  
Low Levels

Population coding is a ubiquitous principle in the nervous system for the proper control of motor behavior. A significant amount of research is dedicated to studying population activity in the superior colliculus (SC) to investigate the motor control of saccadic eye movements. Vector summation with saturation (VSS) has been proposed as a mechanism for how population activity in the SC can be decoded to generate saccades. Interestingly, the model produces different predictions when decoding two simultaneous populations at high vs. low levels of activity. We tested these predictions by generating two simultaneous populations in the SC with high or low levels of dual microstimulation. We also combined varying levels of stimulation with visually induced activity. We found that our results did not perfectly conform to the predictions of the VSS scheme and conclude that the simplest implementation of the model is incomplete. We propose that additional parameters to the model might account for the results of this investigation.

Responses of neurons in the cat's superior colliculus to acoustic stimuli. I. Monaural and binaural response properties

1985 ◽  
Vol 53 (3) ◽  
pp. 726-745 ◽  
Author(s):  
J. A. Hirsch ◽  
J. C. Chan ◽  
T. C. Yin
Keyword(s):  
Superior Colliculus ◽  
Characteristic Frequency ◽  
Spike Count ◽  
Threshold Intensity ◽  
Interaural Time Differences ◽  
Average Characteristic ◽  
Stimulus Interval ◽  
The Mean ◽  
Contralateral Ear ◽  
Stimulation Of

Using extracellular electrodes we studied acoustic responses in the superior colliculus (SC) of the barbiturate-anesthetized cat. Pure tonal stimuli were delivered through sealed and calibrated earphones and were presented either monaurally or binaurally with interaural intensity differences (IIDs) and interaural time differences (ITDs). Acoustically sensitive cells were found in the intermediate and deep layers of the SC throughout its rostrocaudal and mediolateral extent. Most cells (80%) discharged only at stimulus onset; the rest had more complex firing patterns. For 88% of our sample the mean first-spike latency measured at 20 dB above threshold ranged between 6 and 16 ms. The sharpness and threshold intensity of the frequency tuning curves varied widely. In the SC, the average characteristic frequency and threshold intensity were higher than in other auditory brain stem nuclei. Neurons whose characteristic frequency was low were never sharply tuned. The probability of response decreased when the repetition rate at which the stimuli were delivered increased. The mean stimulus interval at which spike count reached 50% of maximum was 360 ms. Most (83%) of the cells discharged only to monaural stimulation of the contralateral ear, 7% responded to tones applied to either ear and only 1% to only ipsilateral input. The remaining cells responded only to stimulation of both ears. With binaural stimuli, most neurons (80%) could be shown to receive input from both ears. Seventy percent of the binaural cells showed predominant binaural inhibition (BI), 25% binaural facilitation (BF), and 5% a more complex mixture. Because the majority of SC neurons had high characteristic frequencies, we examined their responses to IIDs. The spike count vs. IID functions of BI cells were monotonic and sigmoidal, those of BF cells were nonmonotonic and bell-shaped. The slopes and horizontal positions of the curves varied among neurons. IIDs favoring the contralateral ear were the most effective. For a given cell, increasing the mean binaural level extended the range of IIDs that evoked maximal discharge. A small number of cells was sensitive to physiologically significant interaural time differences of low-frequency tones or the envelopes of amplitude-modulated, high-frequency tones.

scholarly journals Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex

2016 ◽  
Vol 114 (2) ◽  
pp. 394-399 ◽  
Author(s):  
John D. Murray ◽  
Alberto Bernacchia ◽  
Nicholas A. Roy ◽  
Christos Constantinidis ◽  
Ranulfo Romo ◽  
...  
Keyword(s):  
Working Memory ◽  
Prefrontal Cortex ◽  
Neural Circuit ◽  
Temporal Dynamics ◽  
Population Level ◽  
Population Coding ◽  
Neural Dynamics ◽  
Stable Population ◽  
Neuron Activity ◽  
Delayed Response

Working memory (WM) is a cognitive function for temporary maintenance and manipulation of information, which requires conversion of stimulus-driven signals into internal representations that are maintained across seconds-long mnemonic delays. Within primate prefrontal cortex (PFC), a critical node of the brain’s WM network, neurons show stimulus-selective persistent activity during WM, but many of them exhibit strong temporal dynamics and heterogeneity, raising the questions of whether, and how, neuronal populations in PFC maintain stable mnemonic representations of stimuli during WM. Here we show that despite complex and heterogeneous temporal dynamics in single-neuron activity, PFC activity is endowed with a population-level coding of the mnemonic stimulus that is stable and robust throughout WM maintenance. We applied population-level analyses to hundreds of recorded single neurons from lateral PFC of monkeys performing two seminal tasks that demand parametric WM: oculomotor delayed response and vibrotactile delayed discrimination. We found that the high-dimensional state space of PFC population activity contains a low-dimensional subspace in which stimulus representations are stable across time during the cue and delay epochs, enabling robust and generalizable decoding compared with time-optimized subspaces. To explore potential mechanisms, we applied these same population-level analyses to theoretical neural circuit models of WM activity. Three previously proposed models failed to capture the key population-level features observed empirically. We propose network connectivity properties, implemented in a linear network model, which can underlie these features. This work uncovers stable population-level WM representations in PFC, despite strong temporal neural dynamics, thereby providing insights into neural circuit mechanisms supporting WM.

scholarly journals Cardinality of sets of closed functional classes in weak 3-valued logics

2013 ◽  
Vol 19 ◽  
pp. 334-343
Author(s):  
Н.Н. Преловский
Keyword(s):  
Functional Class ◽  
Functional Classes

This paper proves that sets of closed functional classes in 3-valued logics of Bochvar $B_3$ and Hallden $H_3$ contains a continuum of different closed classes. It is also proven that both of these logics contain a closed functional class which has no basis.

scholarly journals Isolating Working Memory Capacity Deficits from Sustained Attention Deficits in Patients with Schizophrenia Using a Single Behavioral Task

2021 ◽  
Vol 21 (9) ◽  
pp. 2899
Author(s):  
Geoffrey W. Harrison ◽  
Chelsea Wood-Ross ◽  
Mike Best ◽  
Jessie Eriksen ◽  
Daryl E. Wilson ◽  
...  
Keyword(s):  
Working Memory ◽  
Sustained Attention ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Attention Deficits ◽  
Behavioral Task

scholarly journals Genome plasticity, a key factor of evolution in prokaryotes

2018 ◽  
Author(s):  
Itamar Sela ◽  
Yuri I. Wolf ◽  
Eugene V. Koonin
Keyword(s):  
Scaling Laws ◽  
Statistical Tests ◽  
Prokaryotic Genome ◽  
Functional Class ◽  
Gene Gain ◽  
Genome Plasticity ◽  
Microbial Genomes ◽  
Number Of Genes ◽  
Key Factor ◽  
Functional Classes

In prokaryotic genomes, the number of genes that belong to distinct functional classes shows apparent universal scaling with the total number of genes [1–5] (Fig. 1). This scaling can be approximated with a power law, where the scaling power can be sublinear, near-linear or super-linear. Scaling laws are robust under various statistical tests [4], across different databases and for different gene classifications [1–5]. Several models aimed at explaining the observed scaling laws have been proposed, primarily, based on the specifics of the respective biological functions [1, 5–8]. However, a coherent theory to explain the emergence of scaling within the framework of population genetics is lacking. We employ a simple mathematical model for prokaryotic genome evolution [9] which, together with the analysis of 34 clusters of closely related microbial genomes [10], allows us to identify the underlying forces that dictate genome content evolution. In addition to the scaling of the number of genes in different functional classes, we explore gene contents divergence to characterize the evolutionary processes acting upon genomes [11]. We find that evolution of the gene content is dominated by two factors that are specific to a functional class, namely, selection landscape and genome plasticity. Selection landscape quantifies the fitness cost that is associated with deletion of a gene in a given functional class or the advantage of successful incorporation of an additional gene. Genome plasticity, that can be considered a measure of evolvability, reflects both the availability of the genes of a given functional class in the external gene pool that is accessible to the evolving microbial population, and the ability of microbial genomes to accommodate these genes. The selection landscape determines the gene loss rate, and genome plasticity is the principal determinant of the gene gain rate.

scholarly journals Spatiospectral brain networks reflective of improvisational experience

2021 ◽  
Author(s):  
Josef Faller ◽  
Andrew Goldman ◽  
Yida Lin ◽  
James R. McIntosh ◽  
Paul Sajda
Keyword(s):  
Large Scale ◽  
Time Window ◽  
Network Connectivity ◽  
Functional Class ◽  
Network Activity ◽  
Oddball Paradigm ◽  
Auditory Oddball ◽  
Scalp Eeg ◽  
Musical Structures ◽  
Functional Classes

AbstractMusical improvisers are trained to categorize certain musical structures into functional classes, which is thought to facilitate improvisation. Using a novel auditory oddball paradigm (Goldman et al., 2020) which enables us to disassociate a deviant (i.e. musical cord inversion) from a consistent functional class, we recorded scalp EEG from a group of musicians who spanned a range of improvisational and classically trained experience. Using a spatiospectral based inter and intra network connectivity analysis, we found that improvisers showed a variety of differences in connectivity within and between large-scale cortical networks compared to classically trained musicians, as a function of deviant type. Inter-network connectivity in the alpha band, for a time window leading up to the behavioural response, was strongly linked to improvisation experience, with the default mode network acting as a hub. Spatiospectral networks post response were substantially different between improvisers and classically trained musicians, with greater inter-network connectivity (specific to the alpha and beta bands) seen in improvisers whereas those with more classical training had largely reduced inter-network activity (mostly in the gamma band). More generally, we interpret our findings in the context of network-level correlates of expectation violation as a function of subject expertise, and we discuss how these may generalize to other and more ecologically valid scenarios.

scholarly journals How do Individual Differences in Attention Control and Working Memory Capacity Relate to Monitoring Automation?

2019 ◽  
Vol 63 (1) ◽  
pp. 1409-1413
Author(s):  
Cyrus K. Foroughi ◽  
Ericka Rovira ◽  
Kaley Rose ◽  
DaShawn Davis ◽  
Jaritzel J. Jurado ◽  
...  
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Positive Relationship ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Attention Control ◽  
Automated System ◽  
Significant Positive Relationship ◽  
Operation Span ◽  
Saccade Task

With the proliferation of automated tasks, software, and systems, humans are moving from an active participant in the function of a task to a passive monitor of an automated system that is completing that task. Unfortunately, humans are not well-suited for monitoring roles and there is a need to better understand the factors involved when humans successfully identify when an automated system fails. The goal for this research was to determine whether individual differences in attention control (as measured by the anti-saccade task) and working memory capacity (as measured by the shortened operation span) related to an individual’s ability to detect automation failures. In experiment 1, there was a significant positive relationship ( r = .31) between scores on the anti-saccade task and the number of automation failures that participants detected. In experiment 2, there was a significant positive relationship ( r = .32) between scores on the shortened operation span and the number of automation failures that participants’ detected. The results suggest that certain individuals are better suited for detecting automation failures. Selecting for these individuals may be a fruitful endeavor as automated systems continue to grow across society.

scholarly journals Dynamic population coding and its relationship to working memory

2018 ◽  
Vol 120 (5) ◽  
pp. 2260-2268 ◽  
Author(s):  
Ethan M. Meyers
Keyword(s):  
Working Memory ◽  
Population Coding ◽  
Future Research ◽  
Neural Basis ◽  
Firing Rates ◽  
Sustained Activity ◽  
Early Results ◽  
Population Codes ◽  
Dynamic Population ◽  
Insight Into

For over 45 years, neuroscientists have conducted experiments aimed at understanding the neural basis of working memory. Early results examining individual neurons highlighted that information is stored in working memory in persistent sustained activity where neurons maintained elevated firing rates over extended periods of time. However, more recent work has emphasized that information is often stored in working memory in dynamic population codes, where different neurons contain information at different periods in time. In this paper, I review findings that show that both sustained activity as well as dynamic codes are present in the prefrontal cortex and other regions during memory delay periods. I also review work showing that dynamic codes are capable of supporting working memory and that such dynamic codes could easily be “readout” by downstream regions. Finally, I discuss why dynamic codes could be useful for enabling animals to solve tasks that involve working memory. Although additional work is still needed to know definitively whether dynamic coding is critical for working memory, the findings reviewed here give insight into how different codes could contribute to working memory, which should be useful for guiding future research.

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