No abstract

The cerebral cortex utilizes spatiotemporal continuity in the world to help build invariant representations. In vision, these might be representations of objects. The temporal continuity typical of objects has been used in an associative learning rule with a short-term memory trace to help build invariant object representations. In this paper, we show that spatial continuity can also provide a basis for helping a system to self-organize invariant representations. We introduce a new learning paradigm "continuous transformation learning" which operates by mapping spatially similar input patterns to the same postsynaptic neurons in a competitive learning system. As the inputs move through the space of possible continuous transforms (e.g. translation, rotation, etc.), the active synapses are modified onto the set of postsynaptic neurons. Because other transforms of the same stimulus overlap with previously learned exemplars, a common set of postsynaptic neurons is activated by the new transforms, and learning of the new active inputs onto the same postsynaptic neurons is facilitated. We demonstrate that a hierarchical model of cortical processing in the ventral visual system can be trained with continuous transform learning, and highlight differences in the learning of invariant representations to those achieved by trace learning.

This is the accepted version of the paper.This version of the publication may differ from the final published version. In the standard one-shot Prisoner's Dilemma game, participants often choose to cooperate, when the optimal strategy is to defect. This puzzling finding has attracted considerable interest both in terms of possible computational frameworks for modeling human judgment, but also regarding the more general debate of human altruism. In this research, we ask how much of human behavior in this task can be explained by a putative bias for cooperative behavior and whether this, in turn, is influenced by personality. We compared performance on the standard task with performance on a matched neutral one; we manipulated the optimal strategy (defect or cooperate); and we manipulated the amount of payoff for responding optimally. Results revealed little evidence for a bias for cooperative behavior, but significant associations with the personality factors of Behavioural Activation System (BAS) Reward Responsivity and Agreeableness were found. These findings are discussed in terms of the attempt to explain judgment in one-shot, Prisoner's Dilemma tasks with statistical or probabilistic models. Permanent repository link

The suprachiasmatic nucleus (SCN) regulates a wide range of daily behaviors and has been described as the master circadian pacemaker. The role of daily rhythmicity in other tissues, however, is unknown. We hypothesized that circadian changes in olfactory discrimination depend on a genetic circadian oscillator outside the SCN. We developed an automated assay to monitor olfactory discrimination in individual mice throughout the day. We found olfactory sensitivity increased approximately 6-fold from a minimum during the day to a peak in the early night. This circadian rhythm was maintained in SCN-lesioned mice and mice deficient for the Npas2 gene but was lost in mice lacking Bmal1 or both Per1 and Per2 genes. We conclude that daily rhythms in olfactory sensitivity depend on the expression of canonical clock genes. Olfaction is, thus, the first circadian behavior that is not based on locomotor activity and does not require the SCN.

This article describes the first application of a generic (empirical) Bayesian analysis of between‐subject effects in the dynamic causal modeling (DCM) of electrophysiological (MEG) data. It shows that (i) non‐invasive (MEG) data can be used to characterize subject‐specific differences in cortical microcircuitry and (ii) presents a validation of DCM with neural fields that exploits intersubject variability in gamma oscillations. We find that intersubject variability in visually induced gamma responses reflects changes in the excitation‐inhibition balance in a canonical cortical circuit. Crucially, this variability can be explained by subject‐specific differences in intrinsic connections to and from inhibitory interneurons that form a pyramidal‐interneuron gamma network. Our approach uses Bayesian model reduction to evaluate the evidence for (large sets of) nested models—and optimize the corresponding connectivity estimates at the within and between‐subject level. We also consider Bayesian cross‐validation to obtain predictive estimates for gamma‐response phenotypes, using a leave‐one‐out procedure. Hum Brain Mapp 37:4597–4614, 2016. © The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Risk profile scores (RPS) derived from genome-wide association studies (GWAS) explain a considerable amount of susceptibility for schizophrenia (SCZ). However, little is known about how common genetic risk factors for SCZ influence the structure and function of the human brain, largely due to the constraints of imaging sample sizes. In the current study, we use a novel recall-by-genotype (RbG) methodological approach, where we sample young adults from a population cohort (Avon Longitudinal Study of Parents and Children: N genotyped = 8365) based on their SCZ-RPS. We compared 197 healthy individuals at extremes of low (N = 99) or high (N = 98) SCZ-RPS with behavioral tests, and structural and functional magnetic resonance imaging (fMRI). We first provide methodological details that will inform the design of future RbG studies for common SCZ genetic risk. We further provide an between group analysis of the RbG individuals (low vs high SCZ-RPS) who underwent structural neuroimaging data (T1-weighted scans) and fMRI data during a reversal learning task. While we found little evidence for morphometric differences between the low and high SCZ-RPS groups, we observed an impact of SCZ-RPS on blood oxygen level-dependent (BOLD) signal during reward processing in the ventral striatum (PFWE-VS-CORRECTED = .037), a previously investigated broader reward-related network (PFWE-ROIS-CORRECTED = .008), and across the whole brain (PFWE-WHOLE-BRAIN-CORRECTED = .013). We also describe the study strategy and discuss specific challenges of RbG for SCZ risk (such as SCZ-RPS related homoscedasticity). This study will help to elucidate the behavioral and imaging phenotypes that are associated with SCZ genetic risk.

We show in a 4-layer competitive neuronal network that continuous transformation learning, which uses spatial correlations and a purely associative (Hebbian) synaptic modification rule, can build view invariant representations of complex 3D objects. This occurs even when views of the different objects are interleaved, a condition where temporal trace learning fails. Human psychophysical experiments showed that view invariant object learning can occur when spatial but not temporal continuity applies because of interleaving of stimuli, although sequential presentation, which produces temporal continuity, can facilitate learning. Thus continuous transformation learning is an important principle that may contribute to view invariant object recognition.

Recognizing emotion in faces is important in human interaction and survival, yet existing studies do not paint a consistent picture of the neural representation supporting this task. To address this, we collected magnetoencephalography (MEG) data while participants passively viewed happy, angry and neutral faces. Using time‐resolved decoding of sensor‐level data, we show that responses to angry faces can be discriminated from happy and neutral faces as early as 90 ms after stimulus onset and only 10 ms later than faces can be discriminated from scrambled stimuli, even in the absence of differences in evoked responses. Time‐resolved relevance patterns in source space track expression‐related information from the visual cortex (100 ms) to higher‐level temporal and frontal areas (200–500 ms). Together, our results point to a system optimised for rapid processing of emotional faces and preferentially tuned to threat, consistent with the important evolutionary role that such a system must have played in the development of human social interactions.