Neuronal pattern separation of motion-relevant input in LIP activity.

In various regions of the brain, neurons discriminate sensory stimuli by decreasing the similarity between ambiguous input patterns. Here, we examine whether this process of pattern separation may drive the rapid discrimination of visual motion stimuli in the lateral intraparietal area (LIP). Starting with a simple mean-rate population model that captures neuronal activity in LIP, we show that overlapping input patterns can be reformatted dynamically to give rise to separated patterns of neuronal activity. The population model predicts that a key ingredient of pattern separation is the presence of heterogeneity in the response of individual units. Furthermore, the model proposes that pattern separation relies on heterogeneity in the temporal dynamics of neural activity and not merely in the mean firing rates of individual neurons over time. We confirm these predictions in recordings of macaque LIP neurons and show that the accuracy of pattern separation is a strong predictor of behavioral performance. Overall, results propose that LIP relies on neuronal pattern separation to facilitate decision-relevant discrimination of sensory stimuli.NEW & NOTEWORTHY A new hypothesis is proposed on the role of the lateral intraparietal (LIP) region of cortex during rapid decision making. This hypothesis suggests that LIP alters the representation of ambiguous inputs to reduce their overlap, thus improving sensory discrimination. A combination of computational modeling, theoretical analysis, and electrophysiological data shows that the pattern separation hypothesis links neural activity to behavior and offers novel predictions on the role of LIP during sensory discrimination.

Maximum likelihood estimators for truncated and censored power-law distributions show how neuronal avalanches may be misevaluated.

The coordination of activity amongst populations of neurons in the brain is critical to cognition and behavior. One form of coordinated activity that has been widely studied in recent years is the so-called neuronal avalanche, whereby ongoing bursts of activity follow a power-law distribution. Avalanches that follow a power law are not unique to neuroscience, but arise in a broad range of natural systems, including earthquakes, magnetic fields, biological extinctions, fluid dynamics, and superconductors. Here, we show that common techniques that estimate this distribution fail to take into account important characteristics of the data and may lead to a sizable misestimation of the slope of power laws. We develop an alternative series of maximum likelihood estimators for discrete, continuous, bounded, and censored data. Using numerical simulations, we show that these estimators lead to accurate evaluations of power-law distributions, improving on common approaches. Next, we apply these estimators to recordings of in vitro rat neocortical activity. We show that different estimators lead to marked discrepancies in the evaluation of power-law distributions. These results call into question a broad range of findings that may misestimate the slope of power laws by failing to take into account key aspects of the observed data.

Abnormal strategies during visual discrimination reversal learning in ephrin-A2(-/-) mice.

Eph receptors and ephrins are involved in establishing topographic connectivity in primary sensory brain regions, but also in higher order structures including the cortex and hippocampus. Ephrin-A2(-/-) mice have abnormal topography in the primary visual system but have normal visual and learning performance on a simple visual discrimination task. Here we use signal detection theory to analyse learning behaviour of these mice. Wild-type (WT) and ephrin-A2(-/-) (KO) mice performed equally well in a two-stimulus visual discrimination task, with similar learning rates and response latencies. However, during reversal learning, when the rewarded stimulus was switched, the two genotypes exhibited differences in response strategies: while WTs favoured a win-stay strategy, KOs remained relatively neutral. KOs also exhibited a stronger lateralization bias in the initial stages of learning, choosing the same arm of the maze with high probability. In addition, use of a Bayesian "optimal observer" revealed that compared to WT, KO mice adapted their decisions less rapidly to a change in stimulus-reward relationship. We suggest that the misexpression of ephrin-A2 may lead to abnormal connectivity in regions known for their involvement in reversal learning and perseverative behaviours, including thalamic-prefrontal cortical-striatal circuitry and particularly orbitofrontal cortex. The implication is that topographic organisation of higher order brain regions may play an important role in learning and decision making.