How do humans learn from raw sensory experience? Throughout life, but most obviously in infancy, we learn without explicit instruction. We propose a detailed biological mechanism for the widely embraced idea that learning is driven by the differences between predictions and actual outcomes (i.e., predictive error-driven learning). Specifically, numerous weak projections into the pulvinar nucleus of the thalamus generate top–down predictions, and sparse driver inputs from lower areas supply the actual outcome, originating in Layer 5 intrinsic bursting neurons. Thus, the outcome representation is only briefly activated, roughly every 100 msec (i.e., 10 Hz, alpha), resulting in a temporal difference error signal, which drives local synaptic changes throughout the neocortex. This results in a biologically plausible form of error backpropagation learning. We implemented these mechanisms in a large-scale model of the visual system and found that the simulated inferotemporal pathway learns to systematically categorize 3-D objects according to invariant shape properties, based solely on predictive learning from raw visual inputs. These categories match human judgments on the same stimuli and are consistent with neural representations in inferotemporal cortex in primates.

Standard methods in deep learning for natural language processing fail to capture the compositional structure of human language that allows for systematic generalization outside of the training distribution. However, human learners readily generalize in this way, e.g. by applying known grammatical rules to novel words. Inspired by work in cognitive science suggesting a functional distinction between systems for syntactic and semantic processing, we implement a modification to an existing approach in neural machine translation, imposing an analogous separation between alignment and translation. The resulting architecture substantially outperforms standard recurrent networks on the SCAN dataset, a compositional generalization task, without any additional supervision. Our work suggests that learning to align and to translate in separate modules may be a useful heuristic for capturing compositional structure.

A hallmark of human intelligence is the ability to adapt to new situations by applying learned rules to new content (systematicity) and thereby enabling an open-ended number of inferences and actions (generativity). Here, we propose that the human brain accomplishes these feats through pathways in the parietal cortex that encode the abstract structure of space, events, and tasks and pathways in the temporal cortex that encode information about specific people, places, and things (content). Recent neural network models show how the separation of structure and content might emerge through a combination of architectural biases and learning, and these networks show dramatic improvements over previous models in the ability to capture systematic, generative behavior. We close by considering how the hippocampal formation may form integrative memories that enable rapid learning of new structure and content representations.

Fragile X syndrome (FXS) is caused by silencing of the FMR1 gene and consequent absence of its protein product, fragile X mental retardation protein (FMRP). FMRP is an RNA‐binding protein that can suppress translation. The absence of FMRP leads to symptoms of FXS including intellectual disability and has been proposed to lead to abnormalities in synaptic plasticity. Synaptic plasticity, protein synthesis, and cellular growth pathways have been studied extensively in hippocampal slices from a mouse model of FXS (Fmr1 KO). Enhanced metabotropic glutamate receptor 5 (mGluR5)‐dependent long‐term depression (LTD), increased rates of protein synthesis, and effects on signaling molecules have been reported. These phenotypes were found under amino acid starvation, a condition that has widespread, powerful effects on activation and translation of proteins involved in regulating protein synthesis. We asked if this non‐physiological condition could have effects on Fmr1 KO phenotypes reported in hippocampal slices. We performed hippocampal slice experiments in the presence and absence of amino acids. We measured rates of incorporation of a radiolabeled amino acid into protein to determine protein synthesis rates. By means of western blots, we assessed relative levels of total and phosphorylated forms of proteins involved in signaling pathways regulating translation. We measured evoked field potentials in area CA1 to assess the strength of the long‐term depression response to mGluR activation. In the absence of amino acids, we replicate many of the reported findings in Fmr1 KO hippocampal slices, but in the more physiological condition of inclusion of amino acids in the medium, we did not find evidence of enhanced mGluR5‐dependent LTD. Activation of mGluR5 increased protein synthesis in both wild type and Fmr1 KO. Moreover, mGluR5 activation increased eIF2α phosphorylation and decreased phosphorylation of p70S6k in slices from Fmr1 KO. We propose that the eIF2α response is a cellular attempt to compensate for the lack of regulation of translation by FMRP. Our findings call for a re‐examination of the mGluR theory of FXS.

We address the distinction between habitual/automatic vs. goal-directed/controlled behavior, from the perspective of a computational model of the frontostriatal loops. The model exhibits a continuum of behavior between these poles, as a function of the interactive dynamics among different functionally-specialized brain areas, operating iteratively over multiple sequential steps, and having multiple nested loops of similar decision making circuits. This framework blurs the lines between these traditional distinctions in many ways. For example, although habitual actions have traditionally been considered purely automatic, the outer loop must first decide to allow such habitual actions to proceed. Furthermore, because the part of the brain that generates proposed action plans is common across habitual and controlled/goal-directed behavior, the key differences are instead in how many iterations of sequential decision-making are taken, and to what extent various forms of predictive (model-based) processes are engaged. At the core of every iterative step in our model, the basal ganglia provides a "model-free" dopamine-trained Go/NoGo evaluation of the entire distributed plan/goal/evaluation/prediction state. This evaluation serves as the fulcrum of serializing otherwise parallel neural processing. Goal-based inputs to the nominally model-free basal ganglia system are among several ways in which the popular model-based vs. model-free framework may not capture the most behaviorally and neurally relevant distinctions in this area.

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A hallmark of human intelligence is the ability to adapt to new situations, by applying learned rules to new content (systematicity) and thereby enabling an open-ended number of inferences and actions (generativity). Here, we propose that the human brain accomplishes these feats through pathways in the parietal cortex that encode the abstract structure of space, events, and tasks, and pathways in the temporal cortex that encode information about specific people, places, and things (content). Recent neural network models show how the separation of structure and content might emerge through a combination of architectural biases and learning, and these networks show dramatic improvements in the ability to capture systematic, generative behavior. We close by considering how the hippocampal formation may form integrative memories that enable rapid learning of new structure and content representations.

Recent work has shown that abstract, non-spatial relationships between entities or task states are organized into representations called cognitive maps. Here we investigated how cognitive control enables flexible top-down selection of goal-relevant information from multidimensional cognitive maps retrieved from memory. We examined the relationship between cognitive control and representational geometry by conducting parallel analyses of fMRI data and recurrent neural network (RNN) models trained to perform the same task. We found both 2D map-like representations in a medial temporal lobe and orbitofrontal cortical network and simultaneous 1D orthogonal representations of relevant task dimensions in a frontoparietal network, supporting representational stability and flexibility, respectively. These representational motifs also emerged with distinct temporal profiles over the course of training in the RNN. We further show that increasing control demands due to incongruence (conflicting responses) between current task-relevant and irrelevant dimensions produce warping along the context-invariant axis in subjective representations, and the degree of warping further accounts for individual differences in cognitive control. Together, our findings show how complementary representational geometries balance generalization and behavioral flexibility, and reveal an intricate bidirectional relationship between cognitive control and cognitive map geometry.