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Discoveries over the past two decades demonstrate that regions distributed throughout the association cortex, often called the default network, are suppressed during tasks that demand external attention and are active during remembering, envisioning the future and making social inferences. This Review describes progress in understanding the organization and function of networks embedded within these association regions. Detailed high-resolution analyses of single individuals suggest that the default network is not a single network, as historically described, but instead comprises multiple interwoven networks. The multiple networks share a common organizational motif (also evident in marmoset and macaque anatomical circuits) that might support a general class of processing function dependent on internally constructed rather than externally constrained representations, with each separate interwoven network specialized for a distinct processing domain. Direct neuronal recordings in humans and monkeys reveal evidence for competitive relationships between the internally and externally oriented networks. Findings from rodent studies suggest that the thalamus might be essential to controlling which networks are engaged through specialized thalamic reticular neurons, including antagonistic subpopulations. These association networks (and presumably thalamocortical circuits) are expanded in humans and might be particularly vulnerable to dysregulation implicated in mental illness.

Sound symbolism is a non-arbitrary relationship between speech sounds and meaning. We review evidence that, contrary to the traditional view in linguistics, sound symbolism is an important design feature of language, which affects online processing of language, and most importantly, language acquisition. We propose the sound symbolism bootstrapping hypothesis, claiming that (i) pre-verbal infants are sensitive to sound symbolism, due to a biologically endowed ability to map and integrate multi-modal input, (ii) sound symbolism helps infants gain referential insight for speech sounds, (iii) sound symbolism helps infants and toddlers associate speech sounds with their referents to establish a lexical representation and (iv) sound symbolism helps toddlers learn words by allowing them to focus on referents embedded in a complex scene, alleviating Quine's problem. We further explore the possibility that sound symbolism is deeply related to language evolution, drawing the parallel between historical development of language across generations and ontogenetic development within individuals. Finally, we suggest that sound symbolism bootstrapping is a part of a more general phenomenon of bootstrapping by means of iconic representations, drawing on similarities and close behavioural links between sound symbolism and speech-accompanying iconic gesture.

Although synaptic behaviours of memristors have been widely demonstrated, implementation of an even simple artificial neural network is still a great challenge. In this work, we demonstrate the associative memory on the basis of a memristive Hopfield network. Different patterns can be stored into the memristive Hopfield network by tuning the resistance of the memristors, and the pre-stored patterns can be successfully retrieved directly or through some associative intermediate states, being analogous to the associative memory behaviour. Both single-associative memory and multi-associative memories can be realized with the memristive Hopfield network. Memristors are passive electrical components that can act like simple memories. Here, the authors use an array of hafnium oxide memristors to create a type of artificial neural network, known as a Hopfield network, that is capable of retrieving data from partial information

Synchronous recruitment of fast-spiking (FS) parvalbumin (PV) interneurons generates gamma oscillations, rhythms that emerge during performance of cognitive tasks. Administration of N -methyl- D -aspartate (NMDA) receptor antagonists alters gamma rhythms, and can induce cognitive as well as psychosis-like symptoms in humans. The disruption of NMDA receptor (NMDAR) signaling specifically in FS PV interneurons is therefore hypothesized to give rise to neural network dysfunction that could underlie these symptoms. To address the connection between NMDAR activity, FS PV interneurons, gamma oscillations and behavior, we generated mice lacking NMDAR neurotransmission only in PV cells (PV-Cre/NR1f/f mice). Here, we show that mutant mice exhibit enhanced baseline cortical gamma rhythms, impaired gamma rhythm induction after optogenetic drive of PV interneurons and reduced sensitivity to the effects of NMDAR antagonists on gamma oscillations and stereotypies. Mutant mice show largely normal behaviors except for selective cognitive impairments, including deficits in habituation, working memory and associative learning. Our results provide evidence for the critical role of NMDAR in PV interneurons for expression of normal gamma rhythms and specific cognitive behaviors.

A long-standing idea in modern neuroscience is that the brain computes inferences about the outside world rather than passively observing its environment. The authors record from midbrain dopamine neurons during tasks with different reward contingencies and show that responses are consistent with a learning rule that harnesses hidden-state inference. Midbrain dopamine neurons signal reward prediction error (RPE), or actual minus expected reward. The temporal difference (TD) learning model has been a cornerstone in understanding how dopamine RPEs could drive associative learning. Classically, TD learning imparts value to features that serially track elapsed time relative to observable stimuli. In the real world, however, sensory stimuli provide ambiguous information about the hidden state of the environment, leading to the proposal that TD learning might instead compute a value signal based on an inferred distribution of hidden states (a 'belief state'). Here we asked whether dopaminergic signaling supports a TD learning framework that operates over hidden states. We found that dopamine signaling showed a notable difference between two tasks that differed only with respect to whether reward was delivered in a deterministic manner. Our results favor an associative learning rule that combines cached values with hidden-state inference.

Organisms must learn new strategies to adapt to changing environments. Activity in different neurons often exhibits synchronization that can dynamically enhance their communication and might create flexible brain states that facilitate changes in behavior. We studied the role of gamma-frequency (~40 Hz) synchrony between prefrontal parvalbumin (PV) interneurons in mice learning multiple new cue–reward associations. Voltage indicators revealed cell-type-specific increases of cross-hemispheric gamma synchrony between PV interneurons when mice received feedback that previously learned associations were no longer valid. Disrupting this synchronization by delivering out-of-phase optogenetic stimulation caused mice to perseverate on outdated associations, an effect not reproduced by in-phase stimulation or out-of-phase stimulation at other frequencies. Gamma synchrony was specifically required when new associations used familiar cues that were previously irrelevant to behavioral outcomes, not when associations involved new cues or for reversing previously learned associations. Thus, gamma synchrony is indispensable for reappraising the behavioral salience of external cues.Learning new associations that reappraise the behavioral significance of previously irrelevant cues requires gamma-frequency synchronization between parvalbumin interneurons in the left and right prefrontal cortex.

Navigating the physical world requires learning probabilistic associations between sensory events and their change in time (volatility). Bayesian accounts of this learning process rest on hierarchical prediction errors (PEs) that are weighted by estimates of uncertainty (or its inverse, precision). In a previous fMRI study we found that low-level precision-weighted PEs about visual outcomes (that update beliefs about associations) activated the putative dopaminergic midbrain; by contrast, precision-weighted PEs about cue-outcome associations (that update beliefs about volatility) activated the cholinergic basal forebrain. These findings suggested selective dopaminergic and cholinergic influences on precision-weighted PEs at different hierarchical levels. Here, we tested this hypothesis, repeating our fMRI study under pharmacological manipulations in healthy participants. Specifically, we performed two pharmacological fMRI studies with a between-subject double-blind placebo-controlled design: study 1 used antagonists of dopaminergic (amisulpride) and muscarinic (biperiden) receptors, study 2 used enhancing drugs of dopaminergic (levodopa) and cholinergic (galantamine) modulation. Pooled across all pharmacological conditions of study 1 and study 2, respectively, we found that low-level precision-weighted PEs activated the midbrain and high-level precision-weighted PEs the basal forebrain as in our previous study. However, we found pharmacological effects on brain activity associated with these computational quantities only when splitting the precision-weighted PEs into their PE and precision components: in a brainstem region putatively containing cholinergic (pedunculopontine and laterodorsal tegmental) nuclei, biperiden (compared to placebo) enhanced low-level PE responses and attenuated high-level PE activity, while amisulpride reduced high-level PE responses. Additionally, in the putative dopaminergic midbrain, galantamine compared to placebo enhanced low-level PE responses (in a body-weight dependent manner) and amisulpride enhanced high-level precision activity. Task behaviour was not affected by any of the drugs. These results do not support our hypothesis of a clear-cut dichotomy between different hierarchical inference levels and neurotransmitter systems, but suggest a more complex interaction between these neuromodulatory systems and hierarchical Bayesian quantities. However, our present results may have been affected by confounds inherent to pharmacological fMRI. We discuss these confounds and outline improved experimental tests for the future.

Prefrontal cortical areas mediate flexible adaptive control of behavior, but the specific contributions of individual areas and the circuit mechanisms through which they interact to modulate learning have remained poorly understood. Using viral tracing and pharmacogenetic techniques, we show that prelimbic (PreL) and infralimbic cortex (IL) exhibit reciprocal PreL↔IL layer 5/6 connectivity. In set-shifting tasks and in fear/extinction learning, activity in PreL is required during new learning to apply previously learned associations, whereas activity in IL is required to learn associations alternative to previous ones. IL→PreL connectivity is specifically required during IL-dependent learning, whereas reciprocal PreL↔IL connectivity is required during a time window of 12–14 h after association learning, to set up the role of IL in subsequent learning. Our results define specific and opposing roles of PreL and IL to together flexibly support new learning, and provide circuit evidence that IL-mediated learning of alternative associations depends on direct reciprocal PreL↔IL connectivity. Prelimbic (PL) and infralimbic (IL) cortical areas are known to have complementary roles in learning and decision making. Here the authors report reciprocal connectivity between the two areas and elucidate their functional impact on different aspects of learning.

Intelligent behavior involves associations between high-dimensional sensory representations and behaviorally relevant qualities such as valence. Learning of associations involves plasticity of excitatory connectivity, but it remains poorly understood how information flow is reorganized in networks and how inhibition contributes to this process. We trained adult zebrafish in an appetitive odor discrimination task and analyzed odor representations in a specific compartment of the posterior zone of the dorsal telencephalon (Dp), the homolog of mammalian olfactory cortex. Associative conditioning enhanced responses with a preference for the positively conditioned odor. Moreover, conditioning systematically remapped odor representations along an axis in coding space that represented attractiveness (valence). Interindividual variations in this mapping predicted variations in behavioral odor preference. Photoinhibition of interneurons resulted in specific modifications of odor representations that mirrored effects of conditioning and reduced experience-dependent, interindividual variations in odor–valence mapping. These results reveal an individualized odor-to-valence map that is shaped by inhibition and reorganized during learning.