High precision coding in visual cortex

Stringer, Carsen; Michaelos, Michalis; Pachitariu, Marius

doi:10.1101/679324

Cited by 51 publications

(119 citation statements)

References 71 publications

Supporting

Mentioning

111

Contrasting

Order By: Relevance

“…A recent rodent study using calcium imaging of more than 20,000 neurons also demonstrated the presence of strong differential correlations in V1 (Stringer, Michaelos, and Pachitariu 2019). The authors found that information saturates as the number of neurons goes to infinity, and the discrimination threshold at saturation is about 0.3 degrees.…”

Section: Discussionmentioning

confidence: 94%

Strong information-limiting correlations in early visual areas

Montijn

Liu

Aschner

et al. 2019

Preprint

View full text Add to dashboard Cite

If the brain processes incoming data efficiently, information should degrade little between early and later neural processing stages, and so information in early stages should match behavioral performance. For instance, if there is enough information in a visual cortical area to determine the orientation of a grating to within 1 degree, and the code is simple enough to be read out by downstream circuits, then animals should be able to achieve that performance behaviourally. Despite over 30 years of research, it is still not known how efficient the brain is. For tasks involving a large number of neurons, the amount of information encoded by neural circuits is limited by differential correlations. Therefore, determining how much information is encoded requires quantifying the strength of differential correlations. Detecting them, however, is difficult. We report here a new method, which requires on the order of 100s of neurons and trials. This method relies on computing the alignment of the neural stimulus encoding direction, f′, with the eigenvectors of the noise covariance matrix, Σ. In the presence of strong differential correlations, f′ must be spanned by a small number of the eigenvectors with largest eigenvalues. Using simulations with a leaky-integrate-and-fire neuron model of the LGN-V1 circuit, we confirmed that this method can indeed detect differential correlations consistent with those that would limit orientation discrimination thresholds to 0.5-3 degrees. We applied this technique to V1 recordings in awake monkeys and found signatures of differential correlations, consistent with a discrimination threshold of 0.47-1.20 degrees, which is not far from typical discrimination thresholds (1-2 deg). These results suggest that, at least in macaque monkeys, V1 contains about as much information as is seen in behaviour, implying that downstream circuits are efficient at extracting the information available in V1.

show abstract

Section: Discussionmentioning

confidence: 94%

Strong information-limiting correlations in early visual areas

Montijn

Liu

Aschner

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…1), and in part included spike timing information (Denman & Reid, 2019) in addition to the spike counts used here. Recent recordings from ~20,000 neurons in mouse V1 suggest information about visual stimuli does saturate (Stringer, Michaelos, & Pachitariu, 2019; Fig. S6), but it appears to do so above the population sizes we estimated.…”

Section: Discussionmentioning

confidence: 53%

Scaling of information in large neural populations reveals signatures of information-limiting correlations

Kafashan

Jaffe

Chettih

et al. 2020

Preprint

View full text Add to dashboard Cite

How is information distributed across large neuronal populations within a given brain area? One possibility is that information is distributed roughly evenly across neurons, so that total information scales linearly with the number of recorded neurons. Alternatively, the neural code might be highly redundant, meaning that total information saturates. Here we investigated how information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex (V1). We found that information scales sublinearly, due to the presence of correlated noise in these populations. Using recent theoretical advances, we compartmentalized noise correlations into informationlimiting and nonlimiting components, and then extrapolated to predict how information grows when neural populations are even larger. We predict that tens of thousands of neurons are required to encode 95% of the information about visual stimulus direction, a number much smaller than the number of neurons in V1. Overall, these findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most information from smaller subpopulations. Figure 1. Information scaling in large neural populations, and the impact of noise correlations on information.a. The information that a population of neurons can encode about some stimulus value is always a non-decreasing function of the population size. Information might on average increase with every added neuron (unbounded scaling; red) if the information is evenly distributed across all neurons. In contrast, information can rapidly saturate if information is redundant, and thus it is not strictly limited by population size, but by other factors. In general, it has only been possible to record from a very small subset of neurons of a particular area (grey shaded), from which it is hard to tell the difference between the two scenarios if the sampled population size is too small. b. The encoded information is modulated by noise correlations. This is illustrated using two neurons with different tunings to the stimulus value (top). The amount of information to discriminate between two stimulus values ( " /red and # /blue) depends on the difference in mean population activity (crosses) between stimuli, and the noise correlations (shaded ellipsoids) for either stimulus (bottom, showing joint neural activity of both neurons). The information is largest when the noise is smallest in the direction of the mean population activity difference (black arrow), which leads to the largest separation across the optimal discrimination boundary (grey line). In this example, positive correlations boost information (middle), whereas negative correlations lower it (right), when compared to uncorrelated neurons (left). In general, the impact of noise correlations depends on how they interact with the population's tuning curves.

show abstract

“…(1) Previous work suggests that trial by trial fluctuations in first-order gain are a major source of correlated variability in neural populations. 22,37,41,47,48 It is possible that such fluctuations are aligned with the principal axis of covariability identified during TDR. If so, improvements in d 2 would be largest where noise interference is high, due to increased gain helping to separate stimuli along the discrimination axis.…”

Section: Arousal Improves Neural Discrimination Of Natural Sound Stimulimentioning

confidence: 99%

“…Although evidence for intrinsic correlation is widespread, experimental studies have provided conflicting evidence as to whether or not it does in fact interfere with population coding. [31][32][33][34][35][36][37]48 There are at least two reasons why effects of correlated variability might vary across studies. First, the dimensions containing interfering noise could be very low variance.…”

Section: Effects Of Shared Intrinsic Variability On Discriminability mentioning

confidence: 99%

Selective effects of arousal on population coding of natural sounds in auditory cortex

Heller

Schwartz

Saderi

et al. 2020

Preprint

View full text Add to dashboard Cite

In addition to encoding sound stimulus features, activity in primary auditory cortex (A1) is modulated by non-sensory behavioral state variables, including arousal. Here, we investigated how arousal, measured by pupil size, influences stimulus discriminability in A1. To do this, we recorded from populations of A1 neurons in awake animals while presenting a diverse set of natural sound stimuli. In contrast to previous work, the large stimulus set allowed us to investigate effects of arousal across a wide range of sensory response space. Arousal consistently increased evoked response magnitude and reduced pairwise noise correlations. On average, these changes improved the accuracy of the neural code. However, effects varied across stimuli; neural coding was most improved for areas of the sensory space where noise correlations interfered with the sensory discrimination axis. We also found that first-order modulation of evoked responses and second-order modulation of correlated variability acted on distinct neural populations and timescales, suggesting that arousal interacts with multiple circuits underlying activity in A1.

show abstract

High precision coding in visual cortex

Cited by 51 publications

References 71 publications

Strong information-limiting correlations in early visual areas

Strong information-limiting correlations in early visual areas

Scaling of information in large neural populations reveals signatures of information-limiting correlations

Selective effects of arousal on population coding of natural sounds in auditory cortex

Contact Info

Product

Resources

About