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"Learning - physiology"
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Principles of neural design
In this work, Peter Sterling and Simon Laughlin, two leading neuroscientists, outline a set of organizing principles to explain the whys of neural design that allow the brain to compute so efficiently. Setting out to 'reverse engineer' the brain - disassembling it to understand it - Sterling and Laughlin first consider why an animal should need a brain, tracing computational abilities from bacterium to protozoan to worm.
Book
Modular organization of the brainstem noradrenaline system coordinates opposing learning states
A small population of brainstem noradrenaline neurons powerfully modulates global brain function, but how they regulate diverse—and at times opposing—functions is not clear. The authors report that a modular organization in this neuromodulatory system, coupled with context-dependent activation modes, controls the balance between opposing emotional and flexible learning states.
Noradrenaline modulates global brain states and diverse behaviors through what is traditionally believed to be a homogeneous cell population in the brainstem locus coeruleus (LC). However, it is unclear how LC coordinates disparate behavioral functions. We report a modular LC organization in rats, endowed with distinct neural projection patterns and coding properties for flexible specification of opposing behavioral learning states. LC projection mapping revealed functionally distinct cell modules with specific anatomical connectivity. An amygdala-projecting ensemble promoted aversive learning, while an independent medial prefrontal cortex-projecting ensemble extinguished aversive responses to enable flexible behavior. LC neurons displayed context-dependent inter-relationships, with moderate, discrete activation of distinct cell populations by fear or safety cues and robust, global recruitment of most cells by strong aversive stimuli. These results demonstrate a modular organization in LC in which combinatorial activation modes are coordinated with projection- and behavior-specific cell populations, enabling adaptive tuning of emotional responding and behavioral flexibility.
Journal Article
Encoding of contextual fear memory in hippocampal–amygdala circuit
In contextual fear conditioning, experimental subjects learn to associate a neutral context with an aversive stimulus and display fear responses to a context that predicts danger. Although the hippocampal–amygdala pathway has been implicated in the retrieval of contextual fear memory, the mechanism by which fear memory is encoded in this circuit has not been investigated. Here, we show that activity in the ventral CA1 (vCA1) hippocampal projections to the basal amygdala (BA), paired with aversive stimuli, contributes to encoding conditioned fear memory. Contextual fear conditioning induced selective strengthening of a subset of vCA1–BA synapses, which was prevented under anisomycin-induced retrograde amnesia. Moreover, a subpopulation of BA neurons receives stronger monosynaptic inputs from context-responding vCA1 neurons, whose activity was required for contextual fear learning and synaptic potentiation in the vCA1–BA pathway. Our study suggests that synaptic strengthening of vCA1 inputs conveying contextual information to a subset of BA neurons contributes to encoding adaptive fear memory for the threat-predictive context.
Previous studies implicate the hippocampal–amygdala pathway in contextual fear conditioning, in which animals learn to associate a neutral context with an aversive stimulus and display fear responses to dangerous situations. Here the authors show that selective strengthening of hippocampal–amygdala pathway contributes to encoding adaptive fear memory for threat-predictive context.
Journal Article
Richness of information about novel words influences how episodic and semantic memory networks interact during lexicalization
The complementary learning systems account of declarative memory suggests two distinct memory networks, a fast-mapping, episodic system involving the hippocampus, and a slower semantic memory system distributed across the neocortex in which new information is gradually integrated with existing representations. In this study, we investigated the extent to which these two networks are involved in the integration of novel words into the lexicon after extensive learning, and how the involvement of these networks changes after 24h. In particular, we explored whether having richer information at encoding influences the lexicalization trajectory. We trained participants with two sets of novel words, one where exposure was only to the words' phonological forms (the form-only condition), and one where pictures of unfamiliar objects were associated with the words' phonological forms (the picture-associated condition). A behavioral measure of lexical competition (indexing lexicalization) indicated stronger competition effects for the form-only words. Imaging (fMRI) results revealed greater involvement of phonological lexical processing areas immediately after training in the form-only condition, suggesting that tight connections were formed between novel words and existing lexical entries already at encoding. Retrieval of picture-associated novel words involved the episodic/hippocampal memory system more extensively. Although lexicalization was weaker in the picture-associated condition, overall memory strength was greater when tested after a 24hour delay, probably due to the availability of both episodic and lexical memory networks to aid retrieval. It appears that, during lexicalization of a novel word, the relative involvement of different memory networks differs according to the richness of the information about that word available at encoding.
•Novel words are better remembered if associated with pictorial information.•Novel words with pictures involved the hippocampal memory system at retrieval.•Neocortical activation for novel words with pictures increased with consolidation.•Novel words without pictures showed increased lexical competition after 24h.•Novel words without pictures involved more phonological processing.
Journal Article
Neural mechanisms underlying improved new-word learning with high-density transcranial direct current stimulation
•Anodal HD-tDCS facilitated verb learning.•Enhanced connectivity between IFG and TPJ improved verb learning.•Robust word learning linked to enhanced network interactions.•Behavioral activation of one area and tDCS of another area can induce network modulation.
Neurobehavioral studies have provided evidence for the effectiveness of anodal tDCS on language production, by stimulation of the left Inferior Frontal Gyrus (IFG) or of left Temporo-Parietal Junction (TPJ). However, tDCS is currently not used in clinical practice outside of trials, because behavioral effects have been inconsistent and underlying neural effects unclear. Here, we propose to elucidate the neural correlates of verb and noun learning and to determine if they can be modulated with anodal high-definition (HD) tDCS stimulation. Thirty-six neurotypical participants were randomly allocated to anodal HD-tDCS over either the left IFG, the left TPJ, or sham stimulation. On day one, participants performed a naming task (pre-test). On day two, participants underwent a new-word learning task with rare nouns and verbs concurrently to HD-tDCS for 20 min. The third day consisted of a post-test of naming performance. EEG was recorded at rest and during naming on each day. Verb learning was significantly facilitated by left IFG stimulation. HD-tDCS over the left IFG enhanced functional connectivity between the left IFG and TPJ and this correlated with improved learning. HD-tDCS over the left TPJ enabled stronger local activation of the stimulated area (as indexed by greater alpha and beta-band power decrease) during naming, but this did not translate into better learning. Thus, tDCS can induce local activation or modulation of network interactions. Only the enhancement of network interactions, but not the increase in local activation, leads to robust improvement of word learning. This emphasizes the need to develop new neuromodulation methods influencing network interactions. Our study suggests that this may be achieved through behavioral activation of one area and concomitant activation of another area with HD-tDCS.
Journal Article
Distinct learning-induced changes in stimulus selectivity and interactions of GABAergic interneuron classes in visual cortex
How learning enhances neural representations for behaviorally relevant stimuli via activity changes of cortical cell types remains unclear. We simultaneously imaged responses of pyramidal cells (PYR) along with parvalbumin (PV), somatostatin (SOM), and vasoactive intestinal peptide (VIP) inhibitory interneurons in primary visual cortex while mice learned to discriminate visual patterns. Learning increased selectivity for task-relevant stimuli of PYR, PV and SOM subsets but not VIP cells. Strikingly, PV neurons became as selective as PYR cells, and their functional interactions reorganized, leading to the emergence of stimulus-selective PYR–PV ensembles. Conversely, SOM activity became strongly decorrelated from the network, and PYR–SOM coupling before learning predicted selectivity increases in individual PYR cells. Thus, learning differentially shapes the activity and interactions of multiple cell classes: while SOM inhibition may gate selectivity changes, PV interneurons become recruited into stimulus-specific ensembles and provide more selective inhibition as the network becomes better at discriminating behaviorally relevant stimuli.
Journal Article
Sequence learning in the human brain: A functional neuroanatomical meta-analysis of serial reaction time studies
Sequence learning underlies numerous motor, cognitive, and social skills. Previous models and empirical investigations of sequence learning in humans and non-human animals have implicated cortico-basal ganglia-cerebellar circuitry as well as other structures. To systematically examine the functional neuroanatomy of sequence learning in humans, we conducted a series of neuroanatomical meta-analyses. We focused on the serial reaction time (SRT) task. This task, which is the most widely used paradigm for probing sequence learning in humans, allows for the rigorous control of visual, motor, and other factors. Controlling for these factors (in sequence-random block contrasts), sequence learning yielded consistent activation only in the basal ganglia, across the striatum (anterior/mid caudate nucleus and putamen) and the globus pallidus. In contrast, when visual, motor, and other factors were not controlled for (in a global analysis with all sequence-baseline contrasts, not just sequence-random contrasts), premotor cortical and cerebellar activation were additionally observed. The study provides solid evidence that, at least as tested with the visuo-motor SRT task, sequence learning in humans relies on the basal ganglia, whereas cerebellar and premotor regions appear to contribute to aspects of the task not related to sequence learning itself. The findings have both basic research and translational implications.
•Using ALE, we synthesized the functional neuroanatomical data on sequence learning.•We focused on the widely used serial reaction time (SRT) task paradigm.•Sequence learning (sequence > random contrast) showed only basal ganglia activation.•This was found in the anterior/mid caudate and putamen, and in the globus pallidus.•Cerebellar/premotor activation was linked to other (visual/motor) SRT task factors.
Journal Article
Instructor-learner brain coupling discriminates between instructional approaches and predicts learning
The neural mechanisms that support naturalistic learning via effective pedagogical approaches remain elusive. Here we used functional near-infrared spectroscopy to measure brain activity from instructor-learner dyads simultaneously during dynamic conceptual learning. Results revealed that brain-to-brain coupling was correlated with learning outcomes, and, crucially, appeared to be driven by specific scaffolding behaviors on the part of the instructors (e.g., asking guiding questions or providing hints). Brain-to-brain coupling enhancement was absent when instructors used an explanation approach (e.g., providing definitions or clarifications). Finally, we found that machine-learning techniques were more successful when decoding instructional approaches (scaffolding vs. explanation) from brain-to-brain coupling data than when using a single-brain method. These findings suggest that brain-to-brain coupling as a pedagogically relevant measure tracks the naturalistic instructional process during instructor-learner interaction throughout constructive engagement, but not information clarification.
•We investigated instruction-based naturalistic learning using fNIRS hyperscanning.•Instructor-learner brain coupling was driven by specific scaffolding behaviors.•Instructor-learner brain coupling predicted learning outcomes.•Instructor-learner brain coupling was successfully used to decode instructional approaches.
Journal Article