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

Abstract

These correlations can take the form of correlations between 47 the spike rates of two individual cells, which we call across-neuron noise correlations. Similarly, 48 neural population activity at a given time in response to a stimulus is often correlated with the activity 49 of the same population at other times, which we refer to as across-time noise correlations. 50 51The impact of across-neuron and across-time correlations has been long debated. Much experimental 52and theoretical work has proposed that correlations limit the information capacity of a neural 53 population 6-9 . Because these correlations reflect trial-to-trial variability that is shared across neurons 54 or time, the detrimental effect of variability on stimulus decoding cannot be eliminated by averaging 55 the activity of neurons or time points. Noise correlations are therefore proposed to fundamentally 56 constrain what neural networks can compute, and limit the accuracy by which subjects can judge 57 differences between stimuli 6,7 . A reason to suspect that the effect of noise correlations may be more 58 nuanced, however, comes from a separate stream of biophysical and theoretical studies. This line of 59 work has shown that spatially and temporally correlated spiking can more strongly and more reliably 60propagate signals and drive responses in postsynaptic neural populations 10-14 . However, it remains 61 poorly tested if and how enhanced signal propagation may have a beneficial impact on behavioral 62 discrimination performance. 63 64We reasoned that we could investigate how noise correlations shape behavioral performance in 65 perceptual discrimination by studying at the same time not only how correlations impact the encoding 66 of sensory information, as has been emphasized frequently, but critically also how they impact the 67 reading out of this information by downstream neural circuits to guide behavioral choices. 68 69Correlations of PPC activity limit sensory coding during perceptual discrimination 70To examine how noise correlations affect both stimulus coding at the population level and behavioral 71 discrimination performance, we focused on the mouse posterior parietal cortex (PPC). PPC is thought 72 to participate in transforming multisensory signals into behavioral outputs and is thus likely at the 73 interface of encoding and reading out stimulus information. Furthermore, PPC is essential for 74 perceptual discrimination tasks during virtual-navigation 15,16 , and its activity has been shown to 75 contain stimulus information that relates to an animal's choices [16][17][18][19][20][21][22] . It is thus a relevant area to study 76 the impact of correlated neural activity on behavior. 77 78We examined across-time and across-neuron correlations in PPC population activity using previously 79 published calcium imaging datasets. To study across-time correlations, we used calcium imaging data 80 from a sound localization task 17 in which mice reported perceptual decisions about the location of an 81 auditory stimulus by navig...

Temporal stability of stimulus representation increases along rodent visual cortical hierarchies

“…In both primates and rodents, temporal scales of these intrinsic fluctuations increase along various cortical hierarchies 11,[43][44][45] . Mechanistically, the nature of these intrinsic processes remains unclear, but most authors attribute them to temporally extended input integration or recurrent computations, meant to sustain the neural representation over timescales that guide perception and behavior 44,45,63 . Experimentally, however, most work in the ventral stream has focused on static stimuli, and thus it is not known whether intrinsic dynamics contribute to the structure of population codes for stimuli that are themselves varying in time.…”

“…Intrinsic processing may be crucial for perception in a noisy, changing environment. For example, maintenance of sensory information by stimulus-independent temporal correlations in population activity can lead to better behavioral performance, when consistent estimates of a quantity are needed 45,63 . Alternatively, intrinsic processing may support predictive coding 39,41 , allowing neural circuits to use feedforward inputs to predict and represent what will happen next, an ability with obvious utility for behavior.…”

“…Intrinsic correlations in neural firing fluctuations at different times quantify the tendency of neurons to sustain a given firing rate regardless of sensory input. As such, intrinsic correlations are argued to be a hallmark of longer stimulus integration times, or to act as "stabilizers" of neuronal representations to better support reading of the latter by downstream decision areas 44,45,63 . In vision, intrinsic temporal correlations (Fig.…”

Rare long-range cortical connections enhance information processing

“…Complementary to this approach, it would be of considerable interest to study the Bauplan of the brain at a cellular level or circuit level. It is well-known that cell-to-cell (rather than region-to-region or even voxel-to-voxel) interactions on a very fine spatial scale can influence the dynamics of the brain even on relatively long the time scales (seconds) relevant for the dynamical measures considered here 56,57 . Progress has been made at finer scales as shown by the discovery of the signature of turbulence at the circuit level in the rodent hippocampus 29 .…”

Dimensionality reduction for neural population decoding

“…Importantly, dDR components explicitly preserve stimulus-independent population covariability. In addition to being important for overall information coding, this covariability is known to depend on behavior state 15,16,20,25,31 and stimulus condition. 21,[32][33][34] Therefore, approaches that do not preserve these dynamics, such as trial-averaged P CA, may not accurately characterize how information coding changes across varying behavior and/or stimulus conditions.…”