Studies have found that anterior temporal lobe is critical for detailed knowledge of object categories, suggesting that it has an important role in semantic memory. However, in addition to information about entities, such as people and objects, semantic memory also encompasses information about places. We tested predictions stemming from the PMAT model, which proposes there are distinct systems that support different kinds of semantic knowledge: an anterior temporal (AT) network that represents information about entities and a posterior medial (PM) network that represents information about places. We used representational similarity analysis to test for activation of semantic features when human participants viewed pictures of famous people and places, while controlling for visual similarity. We used machine learning techniques to quantify the semantic similarity of items based on encyclopedic knowledge in the Wikipedia page for each item and found that these similarity models accurately predict human simi...

Perception improves with repeated exposure. Evidence has shown object recognition can be improved by training for multiple days in adults. In particular, a study of Amar-Halpert et al. (2017) has compared the learning effect of repetitive and brief, at-threshold training on a discrimination task and reported similar improvement in both groups. The finding is interpreted as evidence that memory reactivation benefits discrimination learning. This raises the question how this process might influence different perceptual tasks, including tasks with more complex visual stimuli. Here, this preregistered study investigates whether reactivation induces improvements in a visual object learning task that includes more complex visual stimuli. Participants were trained to recognize a set of objects during five days of training. After the initial training, a group was trained with repeated practice, the other with brief, near-threshold reactivation trials. In both groups we found improved object recognition at brief ex...

Computer vision approaches have made significant inroads into offline tracking of behavior and estimating animal poses. In particular, because of their versatility, deep-learning approaches have been gaining attention in behavioral tracking without any markers. Here we developed an approach using DeepLabCut for real-time estimation of movement. We trained a deep neural network offline with high-speed video data of a mouse whisking, then transferred the trained network to work with the same mouse, whisking in real-time. With this approach, we tracked the tips of three whiskers in an arc and converted positions into a TTL output within behavioral time scales, i.e. 10.5 milliseconds. With this approach it is possible to trigger output based on movement of individual whiskers, or on the distance between adjacent whiskers. Flexible closed-loop systems like the one we have deployed here can complement optogenetic approaches and can be used to directly manipulate the relationship between movement and neural activ...

Resting-state functional connectivity has provided substantial insight into intrinsic brain network organization, yet the functional importance of task-related change from that intrinsic network organization remains unclear. Indeed, such task-related changes are known to be small, suggesting they may have only minimal functional relevance. Alternatively, despite their small amplitude, these task-related changes may be essential for the human brain’s ability to adaptively alter its functionality via rapid changes in inter-regional relationships. We utilized activity flow mapping – an approach for building empirically-derived network models – to quantify the functional importance of task-state functional connectivity (above and beyond resting-state functional connectivity) in shaping cognitive task activations in the (female and male) human brain. We found that task-state functional connectivity could be used to better predict independent fMRI activations across all 24 task conditions and all 360 cortical re...

Expected Utility Theory (EUT), the first axiomatic theory of risky choice, describes choices as a utility maximization process: decision makers assign a subjective value (utility) to each choice option and choose the one with the highest utility. The continuity axiom, central to EUT and its modifications, is a necessary and sufficient condition for the definition of numerical utilities. The axiom requires decision makers to be indifferent between a gamble and a specific probabilistic combination of a more preferred and a less preferred gamble. While previous studies demonstrated that monkeys choose according to combinations of objective reward magnitude and probability, a concept-driven experimental approach for assessing the axiomatically defined conditions for maximizing subjective utility by animals is missing. We experimentally tested the continuity axiom for a broad class of gamble types in four male rhesus macaque monkeys, showing that their choice behavior complied with the existence of a numerical ...

Animals engage in routine behavior in order to efficiently navigate their environments. This routine behavior may be influenced by the state of the environment, such as the location and size of rewards. The neural circuits tracking environmental information and how that information impacts decisions to deviate from routines remains unexplored. To investigate the representation of environmental information during routine foraging, we recorded the activity of single neurons in posterior cingulate cortex (PCC) in two male monkeys searching through an array of targets in which the location of rewards was unknown. Outside the laboratory, people and animals solve such traveling salesman problems by following routine traplines that connect nearest-neighbor locations. In our task, monkeys also deployed traplining routines, but as the environment became better known, they deviate from them despite the reduction in foraging efficiency. While foraging, PCC neurons tracked environmental information but not reward and ...

A fundamental, evolutionarily conserved biological mechanism required for long-term memory formation is rapid induction of gene transcription upon learning in relevant brain areas. For episodic types of memories, two regions undergoing this transcription are the dorsal hippocampus (dHC) and prelimbic (PL) cortex. Whether and to what extent these regions regulate similar or distinct transcriptomic profiles upon learning remains to be understood. Here, we used RNA sequencing in the dHC and PL cortex of male rats to profile their transcriptomes in untrained conditions (baseline) and at 1 hour and 6 days after inhibitory avoidance learning. We found that, out of 33,713 transcripts, over 14,000 were significantly expressed at baseline in both regions and approximately 3,000 were selectively enriched in each region. Gene Ontology biological pathway analyses indicated that commonly expressed pathways included synapse organization, regulation of membrane potential, and vesicle localization. The enriched pathways i...

Early life is a sensitive period in which enhanced neural plasticity allows the developing brain to adapt to its environment. This plasticity can also be a risk factor in which maladaptive development can lead to long-lasting behavioral deficits. Here, we test how early-life exposure to the selective-serotonin-reuptake-inhibitor, fluoxetine, affects motivation and dopaminergic signaling in adulthood. We show for the first time that mice exposed to fluoxetine in the early postnatal period exhibit a reduction in effort-related motivation. These mice also show blunted responses to amphetamine and reduced dopaminergic activation in a sucrose reward task. Interestingly, we find that the reduction in motivation can be rescued in the adult by administering bupropion, a dopamine-norepinephrine reuptake inhibitor used as an antidepressant and a smoke cessation aid, but not by fluoxetine. Taken together, our studies highlight the effects of early postnatal exposure of fluoxetine on motivation and demonstrate the in...

Perineuronal nets (PNNs) are an extracellular matrix structure rich in chondroitin sulfate proteoglycans (CSPGs), which preferentially encase parvalbumin-containing (PV+) interneurons. PNNs restrict cortical network plasticity but the molecular mechanisms involved are unclear. We found that reactivation of ocular dominance plasticity in the adult visual cortex induced by chondroitinase ABC (chABC)-mediated PNN removal requires intact signaling by the neurotrophin receptor TRKB in PV+ neurons. Additionally, we demonstrate that chABC increases TRKB phosphorylation (pTRKB), while PNN component aggrecan attenuates brain-derived neurotrophic factor (BDNF)-induced pTRKB in cortical neurons in culture. We further found that protein tyrosine phosphatase σ (PTPσ, PTPRS), receptor for CSPGs, interacts with TRKB and restricts TRKB phosphorylation. PTPσ deletion increases phosphorylation of TRKB in vitro and in vivo in male and female mice, and juvenile-like plasticity is retained in the visual cortex of adult PTPσ-de...

High digital connectivity and a focus on reproducibility are contributing to an open science revolution in neuroscience. Repositories and platforms have emerged across the whole spectrum of subdisciplines, paving the way for a paradigm shift in the way we share, analyze, and reuse vast amounts of data collected across many laboratories. Here, we describe how open access web-based tools are changing the landscape and culture of neuroscience, highlighting six free resources that span subdisciplines from behavior to whole-brain mapping, circuits, neurons, and gene variants.