The primary visual cortex (V1) directly projects to the superior colliculus (SC) and is believed to provide sensory drive for eye movements. Consistent with this, a majority of saccade-related SC neurons also exhibit short-latency, stimulus-driven visual responses, which are additionally feature-tuned. However, direct neurophysiological comparisons of the visual response properties of the two anatomically-connected brain areas are surprisingly lacking, especially with respect to active looking behaviors. I will describe a series of experiments characterizing visual response properties in primate V1 and SC neurons, exploring feature dimensions like visual field location, spatial frequency, orientation, contrast, and luminance polarity. The results suggest a substantial, qualitative reformatting of SC visual responses when compared to V1. For example, SC visual response latencies are actively delayed, independent of individual neuron tuning preferences, as a function of increasing spatial frequency, and this phenomenon is directly correlated with saccadic reaction times. Such “coarse-to-fine” rank ordering of SC visual response latencies as a function of spatial frequency is much weaker in V1, suggesting a dissociation of V1 responses from saccade timing. Consistent with this, when we next explored trial-by-trial correlations of individual neurons’ visual response strengths and visual response latencies with saccadic reaction times, we found that most SC neurons exhibited, on a trial-by-trial basis, stronger and earlier visual responses for faster saccadic reaction times. Moreover, these correlations were substantially higher for visual-motor neurons in the intermediate and deep layers than for more superficial visual-only neurons. No such correlations existed systematically in V1. Thus, visual responses in SC and V1 serve fundamentally different roles in active vision: V1 jumpstarts sensing and image analysis, but SC jumpstarts moving. I will finish by demonstrating, using V1 reversible inactivation, that, despite reformatting of signals from V1 to the brainstem, V1 is still a necessary gateway for visually-driven oculomotor responses to occur, even for the most reflexive of eye movement phenomena. This is a fundamental difference from rodent studies demonstrating clear V1-independent processing in afferent visual pathways bypassing the geniculostriate one, and it demonstrates the importance of multi-species comparisons in the study of oculomotor control.

Within the last decade, Deep Artificial Neural Networks (DNNs) have emerged as powerful computer vision systems that match or exceed human performance on many benchmark tasks such as image classification. But whether current DNNs are suitable computational models of the human visual system remains an open question: While DNNs have proven to be capable of predicting neural activations in primate visual cortex, psychophysical experiments have shown behavioral differences between DNNs and human subjects, as quantified by error consistency. Error consistency is typically measured by briefly presenting natural or corrupted images to human subjects and asking them to perform an n-way classification task under time pressure. But for how long should stimuli ideally be presented to guarantee a fair comparison with DNNs? Here we investigate the influence of presentation time on error consistency, to test the hypothesis that higher-level processing drives behavioral differences. We systematically vary presentation times of backward-masked stimuli from 8.3ms to 266ms and measure human performance and reaction times on natural, lowpass-filtered and noisy images. Our experiment constitutes a fine-grained analysis of human image classification under both image corruptions and time pressure, showing that even drastically time-constrained humans who are exposed to the stimuli for only two frames, i.e. 16.6ms, can still solve our 8-way classification task with success rates way above chance. We also find that human-to-human error consistency is already stable at 16.6ms.

First, a new measure of MI for reaction times is proposed that takes the entire RT distribution into account. Second, we present some recent developments in TWIN modeling, including a new proposal for the sound-induced flash illusion (SIFI).

Spontaneous behavior in animals and humans shows a striking amount of variability both in the spatial domain (which actions to choose) and temporal domain (when to act). Concatenating actions into sequences and behavioral plans reveals the existence of a hierarchy of timescales ranging from hundreds of milliseconds to minutes. How do multiple timescales emerge from neural circuit dynamics? How do circuits modulate temporal responses to flexibly adapt to changing demands? In this talk, we will present recent results from experiments and theory suggesting a new computational mechanism generating the temporal variability underlying naturalistic behavior and cortical activity. We will show how neural activity from premotor areas unfolds through temporal sequences of attractors, which predict the intention to act. These sequences naturally emerge from recurrent cortical networks, where correlated neural variability plays a crucial role in explaining the observed variability in action timing. We will then discuss how reaction times can be accelerated or slowed down via gain modulation, flexibly induced by neuromodulation or perturbations; and how gain modulation may control response timing in the visual cortex. Finally, we will present a new biologically plausible way to generate a reservoir of multiple timescales in cortical circuits.

Spontaneous behavior in animals and humans shows a striking amount of variability both in the spatial domain (which actions to choose) and temporal domain (when to act). Concatenating actions into sequences and behavioral plans reveals the existence of a hierarchy of timescales ranging from hundreds of milliseconds to minutes. How do multiple timescales emerge from neural circuit dynamics? How do circuits modulate temporal responses to flexibly adapt to changing demands? In this talk, we will present recent results from experiments and theory suggesting a new computational mechanism generating the temporal variability underlying naturalistic behavior. We will show how neural activity from premotor areas unfolds through temporal sequences of attractors, which predict the intention to act. These sequences naturally emerge from recurrent cortical networks, where correlated neural variability plays a crucial role in explaining the observed variability in action timing. We will then discuss how reaction times in these recurrent circuits can be accelerated or slowed down via gain modulation, induced by neuromodulation or perturbations. Finally, we will present a general mechanism producing a reservoir of multiple timescales in recurrent networks.

Animals are motivated to acquire knowledge. A particularly striking example is information seeking behavior: animals often seek out sensory cues that will inform them about the properties of uncertain future rewards, even when there is no way for them to use this information to influence the reward outcome, and even when this information comes at a considerable cost. Evidence from monkey electrophysiology and human fMRI studies suggests that orbitofrontal cortex and midbrain dopamine neurons represent the subjective value of knowledge during information seeking behavior. However, it remains unclear how the brain assigns value to information and how it integrates this with other incentives to drive behavior. We have therefore developed a task to test if information preferences are present in mice and study how informational value is imparted on stimuli. Mice are trained to enter a center port and receive an initial odor that instructs them to either go to an informative side port, go to an uninformative side port, or choose freely between them. The chosen side port then yields a second odor cue followed by a delayed probabilistic water reward. The informative port’s odor cue indicates whether the upcoming reward will be big or small. The uninformative port’s odor cue is uncorrelated with the trial outcome. Crucially, the two ports only differ in their odor cues, not in their water value since both offer identical probabilities of big and small rewards. We find that mice prefer the informative port. This preference is evident as a higher percentage choice of the informative port when given a free choice (67% +/- 1.7%, n = 14, p < 0.03), as well as by faster reaction times when instructed to go to the informative port (544ms +/- 21ms vs 795ms +/- 21ms, n = 14, p < 0.001). The preference for information is robust to within-animal reversals of informative and uninformative port locations, and, moreover, mice are willing to pay for information by choosing the informative port even if its reward amount is reduced to be substantially lower than the uninformative port. These behavioral observations suggest that odor stimuli are imparted with informational value as mice learn the information seeking task. We are currently imaging neural activity in orbitofrontal cortex with microendoscopes to identify changes in neural activity that may reflect value associated with the acquisition of knowledge.

Dr Jennifer Lawlor Title: Tracking changes in complex auditory scenes along the cortical pathway Complex acoustic environments, such as a busy street, are characterised by their everchanging dynamics. Despite their complexity, listeners can readily tease apart relevant changes from irrelevant variations. This requires continuously tracking the appropriate sensory evidence while discarding noisy acoustic variations. Despite the apparent simplicity of this perceptual phenomenon, the neural basis of the extraction of relevant information in complex continuous streams for goal-directed behavior is currently not well understood. As a minimalistic model for change detection in complex auditory environments, we designed broad-range tone clouds whose first-order statistics change at a random time. Subjects (humans or ferrets) were trained to detect these changes.They were faced with the dual-task of estimating the baseline statistics and detecting a potential change in those statistics at any moment. To characterize the extraction and encoding of relevant sensory information along the cortical hierarchy, we first recorded the brain electrical activity of human subjects engaged in this task using electroencephalography. Human performance and reaction times improved with longer pre-change exposure, consistent with improved estimation of baseline statistics. Change-locked and decision-related EEG responses were found in a centro-parietal scalp location, whose slope depended on change size, consistent with sensory evidence accumulation. To further this investigation, we performed a series of electrophysiological recordings in the primary auditory cortex (A1), secondary auditory cortex (PEG) and frontal cortex (FC) of the fully trained behaving ferret. A1 neurons exhibited strong onset responses and change-related discharges specific to neuronal tuning. PEG population showed reduced onset-related responses, but more categorical change-related modulations. Finally, a subset of FC neurons (dlPFC/premotor) presented a generalized response to all change-related events only during behavior. We show using a Generalized Linear Model (GLM) that the same subpopulation in FC encodes sensory and decision signals, suggesting that FC neurons could operate conversion of sensory evidence to perceptual decision. All together, these area-specific responses suggest a behavior-dependent mechanism of sensory extraction and generalization of task-relevant event. Aleksandar Ivanov Title: How does the auditory system adapt to different environments: A song of echoes and adaptation