The Magnitude, But Not the Sign, of MT Single-Trial Spike-Time Correlations Predicts Motion Detection Performance

Hashemi, Alireza; Golzar, Ashkan; Smith, Jackson E. T.; Cook, Erik P.

doi:10.1523/jneurosci.1182-17.2018

Cited by 4 publications

(4 citation statements)

References 86 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We next measured how the neural activity encodes whether or not the animal turned the wheel correctly and referred to this measurement as 'Detect probability' (Hashemi, Golzar, Smith, & Cook, 2018). We similarly divided the trials into 12 groups according to the different combinations of right and left stimulus contrast levels (0, 25, 50, 100), ignoring equal contrast pairs.…”

Section: Detect Probabilitymentioning

confidence: 99%

The predictive power of intrinsic timescale during the perceptual decision-making process across the mouse brain

Imani

Hashemi

Radkani

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Across the cortical hierarchy, single neurons are characterized by differences in the extent to which they can sustain their firing rate over time (i.e., their "intrinsic timescale"). Previous studies have demonstrated that neurons in a given brain region mostly exhibit either short or long intrinsic timescales. In this study, we sought to identify populations of neurons that accumulate information over different timescales in the mouse brain and to characterize their functions in the context of a visual discrimination task. Thus, we separately examined the neural population dynamics of neurons with long or short intrinsic timescales across different brain regions. More specifically, we looked at the decoding performance of these neural populations aligned to different task variables (stimulus onset, movement). Taken together, our population-level findings support the hypothesis that long intrinsic timescale neurons encode abstract variables related to decision formation. Furthermore, we investigated whether there was a relationship between how well a single neuron represents the animal's choice or stimuli and their intrinsic timescale. We did not observe any significant relationship between the decoding of these task variables and a single neuron's intrinsic timescale. In summary, our findings support the idea that the long intrinsic timescale population of neurons, which appear at different levels of the cortical hierarchy, are primarily more involved in representing the decision variable.

show abstract

Section: Detect Probabilitymentioning

confidence: 99%

The predictive power of intrinsic timescale during the perceptual decision-making process across the mouse brain

Imani

Hashemi

Radkani

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…For the stimulus decoding, we measured the differences between the spike count distribution of trials with contralateral stimulus higher than zero and trials with zero contra stimulus contrast level for all 12 conditions. Similarly, decision decoding was evaluated by measuring the differences between Hit and Missed trials within 12 conditions referred to as 'Detect Probability' (DP) (Hashemi et al, 2018). Our results showed that the stimulus-selective neurons detected by dPCA, indeed encoded the stimulus more strongly than the decision.…”

Section: Distributed Evidence Accumulation Across the Mice's Brainmentioning

confidence: 84%

Distributed coding of evidence accumulation across the mouse brain using microcircuits with a diversity of timescales

Imani

Radkani

Hashemi

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similarly, decision decoding was evaluated by measuring the differences between Hit and Missed trials within 12 conditions referred to as “detect probability” (DP; Hashemi et al, 2018 ). Our results showed that the stimulus-selective neurons detected by dPCA, indeed encoded the stimulus more strongly than the decision.…”

Section: Resultsmentioning

confidence: 99%

“…We also measured how well the neural activity encodes whether or not the animal turned the wheel correctly and referred to this measurement as “detect probability” ( Hashemi et al, 2018 ). Accordingly, the trials were divided into 12 groups based on the different combinations of the right and left stimulus contrast levels, excluding the conditions with equal contrast levels.…”

Section: Methodsmentioning

confidence: 99%

Distributed Coding of Evidence Accumulation across the Mouse Brain Using Microcircuits with a Diversity of Timescales

Imani,

Radkani,

Hashemi

et al. 2023

eNeuro

Self Cite

View full text Add to dashboard Cite

The gradual accumulation of noisy evidence for or against options is the main step in the perceptual decision-making process. Using brain-wide electrophysiological recording in mice (Steinmetz et al., 2019), we examined neural correlates of evidence accumulation across brain areas. We demonstrated that the neurons with Drift-Diffusion-Model-like firing rate activity (i.e., evidence-sensitive ramping firing rate) were distributed across the brain. Exploring the underlying neural mechanism of evidence accumulation for the DDM-like neurons revealed different accumulation mechanisms (i.e. single and race) both within and across the brain areas. Our findings support the hypothesis that evidence accumulation is happening through multiple integration mechanisms in the brain. We further explored the timescale of the integration process in the single and race accumulator models. The results demonstrated that the accumulator microcircuits within each brain area had distinct properties in terms of their integration timescale, which were organized hierarchically across the brain. These findings support the existence of evidence accumulation over multiple timescales. Besides the variability of integration timescale across the brain, a heterogeneity of timescales was observed within each brain area as well. We demonstrated that this variability reflected the diversity of microcircuit parameters, such that accumulators with longer integration timescales had higher recurrent excitation strength.Significance StatementIn this paper we characterized the perceptual decision-making process across the mouse brain. Our findings shed more light on the decision-making process by analyzing the brain-wide electrophysiological recording dataset. This paper contains a comprehensive analysis to characterize different aspects of the evidence accumulation process, including the distribution of accumulator-like neurons, the timescale of information integration, accumulation architecture, and the relationship between accumulators’ timescale and their integration circuit properties.

show abstract

The Magnitude, But Not the Sign, of MT Single-Trial Spike-Time Correlations Predicts Motion Detection Performance

Cited by 4 publications

References 86 publications

The predictive power of intrinsic timescale during the perceptual decision-making process across the mouse brain

The predictive power of intrinsic timescale during the perceptual decision-making process across the mouse brain

Distributed coding of evidence accumulation across the mouse brain using microcircuits with a diversity of timescales

Distributed Coding of Evidence Accumulation across the Mouse Brain Using Microcircuits with a Diversity of Timescales

Contact Info

Product

Resources

About