Vision for perception versus vision for action: dissociable contributions of visual sensory drives from primary visual cortex and superior colliculus neurons to orienting behaviors

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.

Visual-vestibular cue comparison for perception of environmental stationarity

Note the later time!

Verb metaphors are processed as analogies

Metaphor is a pervasive phenomenon in language and cognition. To date, the vast majority of psycholinguistic research on metaphor has focused on noun-noun metaphors of the form An X is a Y (e.g., My job is a jail). Yet there is evidence that verb metaphor (e.g., I sailed through my exams) is more common. Despite this, comparatively little work has examined how verb metaphors are processed. In this talk, I will propose a novel account for verb metaphor comprehension: verb metaphors are understood in the same way that analogies are—as comparisons processed via structure-mapping. I will discuss the predictions that arise from applying the analogical framework to verb metaphor and present a series of experiments showing that verb metaphoric extension is consistent with those predictions.

Applying Structural Alignment theory to Early Verb Learning

Learning verbs is difficult and critical to learning one's native language. Children appear to benefit from seeing multiple events and comparing them to each other, and structural alignment theory provides a good theoretical framework to guide research into how preschool children may be comparing events as they learn new verbs. The talk will include 6 studies of early verb learning that make use of eye-tracking procedures as well as other behavioral (pointing) procedures, and that test key predictions from SA theory including the prediction that seeing similar examples before more varied examples helps observers learn how to compare (progressive alignment) and the prediction that when events have very low alignability with other events, that is one cue that the events should be ignored. Whether or how statistical learning may also be at work will be considered.

Navigating Increasing Levels of Relational Complexity: Perceptual, Analogical, and System Mappings

Relational thinking involves comparing abstract relationships between mental representations that vary in complexity; however, this complexity is rarely made explicit during everyday comparisons. This study explored how people naturally navigate relational complexity and interference using a novel relational match-to-sample (RMTS) task with both minimal and relationally directed instruction to observe changes in performance across three levels of relational complexity: perceptual, analogy, and system mappings. Individual working memory and relational abilities were examined to understand RMTS performance and susceptibility to interfering relational structures. Trials were presented without practice across four blocks and participants received feedback after each attempt to guide learning. Experiment 1 instructed participants to select the target that best matched the sample, while Experiment 2 additionally directed participants’ attention to same and different relations. Participants in Experiment 2 demonstrated improved performance when solving analogical mappings, suggesting that directing attention to relational characteristics affected behavior. Higher performing participants—those above chance performance on the final block of system mappings—solved more analogical RMTS problems and had greater visuospatial working memory, abstraction, verbal analogy, and scene analogy scores compared to lower performers. Lower performers were less dynamic in their performance across blocks and demonstrated negative relationships between analogy and system mapping accuracy, suggesting increased interference between these relational structures. Participant performance on RMTS problems did not change monotonically with relational complexity, suggesting that increases in relational complexity places nonlinear demands on working memory. We argue that competing relational information causes additional interference, especially in individuals with lower executive function abilities.

How do protein-RNA condensates form and contribute to disease?

In recent years, it has become clear that intrinsically disordered regions (IDRs) of RBPs, and the structure of RNAs, often contribute to the condensation of RNPs. To understand the transcriptomic features of such RNP condensates, we’ve used an improved individual nucleotide resolution CLIP protocol (iiCLIP), which produces highly sensitive and specific data, and thus enables quantitative comparisons of interactions across conditions (Lee et al., 2021). This showed how the IDR-dependent condensation properties of TDP-43 specify its RNA binding and regulatory repertoire (Hallegger et al., 2021). Moreover, we developed software for discovery and visualisation of RNA binding motifs that uncovered common binding patterns of RBPs on long multivalent RNA regions that are composed of dispersed motif clusters (Kuret et al, 2021). Finally, we used hybrid iCLIP (hiCLIP) to characterise the RNA structures mediating the assembly of Staufen RNPs across mammalian brain development, which demonstrated the roles of long-range RNA duplexes in the compaction of long 3’UTRs. I will present how the combined analysis of the characteristics of IDRs in RBPs, multivalent RNA regions and RNA structures is required to understand the formation and functions of RNP condensates, and how they change in diseases.

Parametric control of flexible timing through low-dimensional neural manifolds

Biological brains possess an exceptional ability to infer relevant behavioral responses to a wide range of stimuli from only a few examples. This capacity to generalize beyond the training set has been proven particularly challenging to realize in artificial systems. How neural processes enable this capacity to extrapolate to novel stimuli is a fundamental open question. A prominent but underexplored hypothesis suggests that generalization is facilitated by a low-dimensional organization of collective neural activity, yet evidence for the underlying neural mechanisms remains wanting. Combining network modeling, theory and neural data analysis, we tested this hypothesis in the framework of flexible timing tasks, which rely on the interplay between inputs and recurrent dynamics. We first trained recurrent neural networks on a set of timing tasks while minimizing the dimensionality of neural activity by imposing low-rank constraints on the connectivity, and compared the performance and generalization capabilities with networks trained without any constraint. We then examined the trained networks, characterized the dynamical mechanisms underlying the computations, and verified their predictions in neural recordings. Our key finding is that low-dimensional dynamics strongly increases the ability to extrapolate to inputs outside of the range used in training. Critically, this capacity to generalize relies on controlling the low-dimensional dynamics by a parametric contextual input. We found that this parametric control of extrapolation was based on a mechanism where tonic inputs modulate the dynamics along non-linear manifolds in activity space while preserving their geometry. Comparisons with neural recordings in the dorsomedial frontal cortex of macaque monkeys performing flexible timing tasks confirmed the geometric and dynamical signatures of this mechanism. Altogether, our results tie together a number of previous experimental findings and suggest that the low-dimensional organization of neural dynamics plays a central role in generalizable behaviors.

NMC4 Short Talk: Maggot brain, mirror image? A statistical analysis of bilateral symmetry in an insect brain connectome

Neuroscientists have many questions about connectomes that revolve around the ability to compare networks. For example, comparing connectomes could help explain how neural wiring is related to individual differences, genetics, disease, development, or learning. One such question is that of bilateral symmetry: are the left and right sides of a connectome the same? Here, we investigate the bilateral symmetry of a recently presented connectome of an insect brain, the Drosophila larva. We approach this question from the perspective of two-sample testing for networks. First, we show how this question of “sameness” can be framed as a variety of different statistical hypotheses, each with different assumptions. Then, we describe test procedures for each of these hypotheses. We show how these different test procedures perform on both the observed connectome as well as a suite of synthetic perturbations to the connectome. We also point out that these tests require careful attention to parameter alignment and differences in network density in order to provide biologically meaningful results. Taken together, these results provide the first statistical characterization of bilateral symmetry for an entire brain at the single-neuron level, while also giving practical recommendations for future comparisons of connectome networks.

NMC4 Keynote: Latent variable modeling of neural population dynamics - where do we go from here?

Large-scale recordings of neural activity are providing new opportunities to study network-level dynamics with unprecedented detail. However, the sheer volume of data and its dynamical complexity are major barriers to uncovering and interpreting these dynamics. I will present machine learning frameworks that enable inference of dynamics from neuronal population spiking activity on single trials and millisecond timescales, from diverse brain areas, and without regard to behavior. I will then demonstrate extensions that allow recovery of dynamics from two-photon calcium imaging data with surprising precision. Finally, I will discuss our efforts to facilitate comparisons within our field by curating datasets and standardizing model evaluation, including a currently active modeling challenge, the 2021 Neural Latents Benchmark [neurallatents.github.io].

Direction selectivity in hearing: monaural phase sensitivity in octopus neurons

The processing of temporal sound features is fundamental to hearing, and the auditory system displays a plethora of specializations, at many levels, to enable such processing. Octopus neurons are the most extreme temporally-specialized cells in the auditory (and perhaps entire) brain, which make them intriguing but also difficult to study. Notwithstanding the scant physiological data, these neurons have been a favorite cell type of modeling studies which have proposed that octopus cells have critical roles in pitch and speech perception. We used a range of in vivo recording and labeling methods to examine the hypothesis that tonotopic ordering of cochlear afferents combines with dendritic delays to compensate for cochlear delay - which would explain the highly entrained responses of octopus cells to sound transients. Unexpectedly, the experiments revealed that these neurons have marked selectivity to the direction of fast frequency glides, which is tied in a surprising way to intrinsic membrane properties and subthreshold events. The data suggest that octopus cells have a role in temporal comparisons across frequency and may play a role in auditory scene analysis.

Synthetic Developmental Biology - Cross-species comparison and manipulation of organoids

Cortical and subcortical grey matter micro-structure is associated with polygenic risk for schizophrenia

Background: Recent discovery of hundreds of common gene variants associated with schizophrenia has enabled polygenic risk scores (PRS) to be measured in the population. It is hypothesized that normal variation in genetic risk of schizophrenia should be associated with MRI changes in brain morphometry and tissue composition. Methods: We used the largest extant genome-wide association dataset (N = 69,369 cases and N = 236,642 healthy controls) to measure PRS for schizophrenia in a large sample of adults from the UK Biobank (Nmax = 29,878) who had multiple micro- and macro-structural MRI metrics measured at each of 180 cortical areas and seven subcortical structures. Linear mixed effect models were used to investigate associations between schizophrenia PRS and brain structure at global and regional scales, controlled for multiple comparisons. Results: Micro-structural phenotypes were more robustly associated with schizophrenia PRS than macro-structural phenotypes. Polygenic risk was significantly associated with reduced neurite density index (NDI) at global brain scale, at 149 cortical regions, and five subcortical structures. Other micro-structural parameters, e.g., fractional anisotropy, that were correlated with NDI were also significantly associated with schizophrenia PRS. Genetic effects on multiple MRI phenotypes were co-located in temporal, cingulate and prefrontal cortical areas, insula, and hippocampus. (Preprint: https://www.medrxiv.org/content/10.1101/2021.02.06.21251073v1)

Context and Comparison During Open-Ended Induction

A key component of humans' striking creativity in solving problems is our ability to construct novel descriptions to help us characterize novel categories. Bongard problems, which challenge the problem solver to come up with a rule for distinguishing visual scenes that fall into two categories, provide an elegant test of this ability. Bongard problems are challenging for both human and machine category learners because only a handful of example scenes are presented for each category, and they often require the open-ended creation of new descriptions. A new sub-type of Bongard problem called Physical Bongard Problems (PBPs) is introduced, which require solvers to perceive and predict the physical spatial dynamics implicit in the depicted scenes. The PATHS (Perceiving And Testing Hypotheses on Structures) computational model which can solve many PBPs is presented, and compared to human performance on the same problems. PATHS and humans are similarly affected by the ordering of scenes within a PBP, with spatially and temporally juxtaposed scenes promoting category learning when they are similar and belong to different categories, or dissimilar and belong to the same category. The core theoretical commitments of PATHS which we believe to also exemplify human open-ended category learning are a) the continual perception of new scene descriptions over the course of category learning; b) the context-dependent nature of that perceptual process, in which the scenes establish the context for one another; c) hypothesis construction by combining descriptions into logical expressions; and d) bi-directional interactions between perceiving new aspects of scenes and constructing hypotheses for the rule that distinguishes categories.

The Structural Anchoring of Spontaneous Analogies

It is generally acknowledged that analogy is a core mechanism of human cognition, but paradoxically, analogies based on structural similarities would rarely be implemented spontaneously (e.g. without an explicit invitation to compare two representations). The scarcity of deep spontaneous analogies is at odds with the demonstration that familiar concepts from our daily-life are spontaneously used to encode the structure of our experiences. Based on this idea, we will present experimental works highlighting the predominant role of structural similarities in analogical retrieval. The educational stakes lurking behind the tendency to encode the problem’s structures through familiar concepts will also be addressed.

Can subjective experience be quantified? Critically examining computational cognitive neuroscience approaches

Computational and cognitive neuroscience techniques have made great strides towards describing the neural computations underlying perceptual inference and decision-making under uncertainty. These tools tell us how and why perceptual illusions occur, which brain areas may represent noisy information in a probabilistic manner, and so on. However, an understanding of the subjective, qualitative aspects of perception remains elusive: qualia, or the personal, intrinsic properties of phenomenal awareness, have remained out of reach of these computational analytic insights. Here, I propose that metacognitive computations, and the subjective feelings that go along with them, give us a solid starting point for understanding subjective experience in general. Specifically, perceptual metacognition possesses ontological and practical properties that provide a powerful and unique opportunity for studying the studying the neural and computational correlates of subjective experience using established tools of computational and cognitive neuroscience. By capitalizing on decades of developments in formal computational model comparisons as applied to the specific properties of perceptual metacognition, we are now in a privileged position to reveal new and exciting insights about how the brain constructs our subjective conscious experiences.

Using Developmental Trajectories to Understand Change in Children’s Analogical Reasoning

Analogical reasoning is a complex ‘high-level’ cognitive process characterised by making inferences based on analogical comparisons. As with other high-level processes, development takes place over a protracted time period and believed to result from changes in multiple ‘lower-level’ systems. In the case of analogical reasoning, changes in systems responsible for conceptual knowledge, task knowledge, inhibition, and working memory have all been causally implicated in development. Whilst there is evidence that each of these systems contributes to development, what the relative contribution of each across development is, and how they interact with each, remain largely unanswered questions. In this presentation, I will describe how cross-sectional trajectory analysis can be used as a complementary method to shed light on these questions.

Monkey Talk – what studies about nonhuman primate vocal communication reveal about the evolution of speech

The evolution of speech is considered to be one of the hardest problems in science. Studies of the communicative abilities of our closest living relatives, the nonhuman primates, aim to contribute to a better understanding of the emergence of this uniquely human capability. Following a brief introduction over the key building blocks that make up the human speech faculty, I will focus on the question of meaning in nonhuman primate vocalizations. While nonhuman primate calls may be highly context specific, thus giving rise to the notion of ‘referentiality’, comparisons across closely related species suggest that this specificity is evolved rather than learned. Yet, as in humans, the structure of calls varies with arousal and affective state, and there is some evidence for effects of sensory-motor integration in vocal production. Thus, the vocal production of nonhuman primates bears little resemblance to the symbolic and combinatorial features of human speech, while basic production mechanisms are shared. Listeners, in contrast, are able learning the meaning of new sounds. A recent study using artificial predator shows that this learning may be extremely rapid. Furthermore, listeners are able to integrate information from multiple sources to make adaptive decisions, which renders the vocal communication system as a whole relatively flexible and powerful. In conclusion, constraints at the side of vocal production, including limits in social cognition and motivation to share experiences, rather than constraints at the side of the recipient explain the differences in communicative abilities between humans and other animals.

Who can turn faster? Comparison of the head direction circuit of two species

Ants, bees and other insects have the ability to return to their nest or hive using a navigation strategy known as path integration. Similarly, fruit flies employ path integration to return to a previously visited food source. An important component of path integration is the ability of the insect to keep track of its heading relative to salient visual cues. A highly conserved brain region known as the central complex has been identified as being of key importance for the computations required for an insect to keep track of its heading. However, the similarities or differences of the underlying heading tracking circuit between species are not well understood. We sought to address this shortcoming by using reverse engineering techniques to derive the effective underlying neural circuits of two evolutionary distant species, the fruit fly and the locust. Our analysis revealed that regardless of the anatomical differences between the two species the essential circuit structure has not changed. Both effective neural circuits have the structural topology of a ring attractor with an eight-fold radial symmetry (Fig. 1). However, despite the strong similarities between the two ring attractors, there remain differences. Using computational modelling we found that two apparently small anatomical differences have significant functional effect on the ability of the two circuits to track fast rotational movements and to maintain a stable heading signal. In particular, the fruit fly circuit responds faster to abrupt heading changes of the animal while the locust circuit maintains a heading signal that is more robust to inhomogeneities in cell membrane properties and synaptic weights. We suggest that the effects of these differences are consistent with the behavioural ecology of the two species. On the one hand, the faster response of the ring attractor circuit in the fruit fly accommodates the fast body saccades that fruit flies are known to perform. On the other hand, the locust is a migratory species, so its behaviour demands maintenance of a defined heading for a long period of time. Our results highlight that even seemingly small differences in the distribution of dendritic fibres can have a significant effect on the dynamics of the effective ring attractor circuit with consequences for the behavioural capabilities of each species. These differences, emerging from morphologically distinct single neurons highlight the importance of a comparative approach to neuroscience.