On the Limits of Time in the Brain

KronoScope ◽  
2013 ◽  
Vol 13 (2) ◽  
pp. 228-239
Author(s):  
Rémy Lestienne

Abstract J.T. Fraser used to emphasize the uniqueness of the human brain in its capacity for apprehending the various dimensions of “nootemporality” (Fraser 1982 and 1987). Indeed, our brain allows us to sense the flow of time, to measure delays, to remember past events or to predict future outcomes. In these achievements, the human brain reveals itself far superior to its animal counterpart. Women and men are the only beings, I believe, who are able to think about what they will do the next day. This is because such a thought implies three intellectual abilities that are proper to mankind: the capacity to take their own thoughts as objects of their thinking, the ability of mental time travels—to the past thanks to their episodic memory or to the future—and the possibility to project very far into the future, as a consequence of their enlarged and complexified forebrain. But there are severe limits to our timing abilities of which we are often unaware. Our sensibility to the passing time, like other of our intellectual abilities, is often competing with other brain functions, because they use at least in part the same neural networks. This is particularly the case regarding attention. The deeper the level of attention required, the looser is our perception of the flow of time. When we pay attention to something, when we fix our attention, then our inner sense of the flux of time freezes. This limitation should not sound too unfamiliar to the reader of J.T. Fraser who wrote in his book Time, Conflict, and Human Values (1999) about “time as a nested hierarchy of unresolvable conflicts.”

2018 ◽  
Author(s):  
Hyojeong Kim ◽  
Margaret L. Schlichting ◽  
Alison R. Preston ◽  
Jarrod A. Lewis-Peacock

AbstractThe human brain constantly anticipates the future based on memories of the past. Encountering a familiar situation reactivates memory of previous encounters which can trigger a prediction of what comes next to facilitate responsiveness. However, a prediction error can lead to pruning of the offending memory, a process that weakens its representation in the brain and leads to forgetting. Our goal in this study was to evaluate whether memories are spared from pruning in situations that allow for more abstract yet reliable predictions. We hypothesized that when the category, but not the identity, of a new stimulus can be anticipated, this will reduce pruning of existing memories and also reduce encoding of the specifics of new memories. Participants viewed a sequence of objects, some of which reappeared multiple times (“cues”), followed always by novel items. Half of the cues were followed by new items from different (unpredictable) categories, while others were followed by new items from a single (predictable) category. Pattern classification of fMRI data was used to identify category-specific predictions after each cue. Pruning was observed only in unpredictable contexts, while encoding of new items suffered more in predictable contexts. These findings demonstrate that how episodic memories are updated is influenced by the reliability of abstract-level predictions in familiar contexts.


2021 ◽  
Vol 8 (4) ◽  
pp. 1
Author(s):  
Saman Tauqir

Since the birth of science, the most fascinating structure of the human body is the human brain.  Over the past centuries’ researchers have been developing the latest technologies to imitate and explore how the human brain functions. However, to develop a machine that thinks like a human brain is still a dream for researchers. Aristotle’s early efforts to devise logical thinking via his syllogisms (a three-part deductive reasoning) were a source of inspiration for modern computers and technologies1. In the1950, Alan Turing designed a machine to decode encrypted messages, which was a breakthrough of super computers in the days of yore. He designed the “Turing Test” which was coined to assess whether a computer could exhibit intelligence better known as “artificial intelligence” (AI) today2. AI is “a field of science and engineering concerned with the computational understanding of what is commonly called intelligent behavior, and with the creation of artifacts that exhibit such behaviour”3. Since 1980, AI has come a long way, virtual reality is being used in dental education these days to create real life situations and promote clinical work on simulators to eliminate risk factors associated with training on live patients. Recently artificial intelligence has been integrated with tutoring systems like “Unified Medical Language System” (UMLS), which have resulted in a better quality of feedback, which the preclinical virtual patients provide to the students4,5. This interactive phase helps students to evaluate their clinical skills and compare their skills with the standard ones, thus creating an ideal and high-quality training environment. Studies have been carried out regarding the efficacy of AI systems, which have stipulated that preclinical students build higher competencies than with the use of traditional simulator units6-8. Currently AI inbuilt virtual dental assistants are present in the market. They can execute various chair side tasks with greater accuracy and less manpower ensuring minimum error during the procedures. In the world of implantology and maxillofacial surgery AI helps plan and prepare surgeries with smallest details forgoing actual surgery. Some exceptional uses of AI include robotic surgeries in the field of maxillofacial surgery and bioprinting (where tissues and organs can be reconstructed in thin layers)9. The field of AI has flourished to great extent in the past decade; AI systems are an aid to the field of dentistry and dental education.  This narrative attempts to explain possible AI-based applications in the future, it can be used for dental diagnosis, planning out treatments, conducting image analysis, and record keeping. AI-based technologies streamline and reduce laborious workforce to routine tasks, it ensures dental procedures are possible at a lower cost and ultimately makes predictive, preventive, and participatory dentistry possible. The use of AI in dental procedures needs to be guaranteed; its application with human oversight and evidence-based dentistry shall be expected. Dental education needs to be introduced to clinical AI solutions by promoting digital literacy in the future dental liveware.


2021 ◽  
pp. 147612702110120
Author(s):  
Siavash Alimadadi ◽  
Andrew Davies ◽  
Fredrik Tell

Research on the strategic organization of time often assumes that collective efforts are motivated by and oriented toward achieving desirable, although not necessarily well-defined, future states. In situations surrounded by uncertainty where work has to proceed urgently to avoid an impending disaster, however, temporal work is guided by engaging with both desirable and undesirable future outcomes. Drawing on a real-time, in-depth study of the inception of the Restoration and Renewal program of the Palace of Westminster, we investigate how organizational actors develop a strategy for an uncertain and highly contested future while safeguarding ongoing operations in the present and preserving the heritage of the past. Anticipation of undesirable future events played a crucial role in mobilizing collective efforts to move forward. We develop a model of future desirability in temporal work to identify how actors construct, link, and navigate interpretations of desirable and undesirable futures in their attempts to create a viable path of action. By conceptualizing temporal work based on the phenomenological quality of the future, we advance understanding of the strategic organization of time in pluralistic contexts characterized by uncertainty and urgency.


2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Yalan Xu ◽  
Xiuyue Song ◽  
Dong Wang ◽  
Yin Wang ◽  
Peifeng Li ◽  
...  

AbstractChemical synapses in the brain connect neurons to form neural circuits, providing the structural and functional bases for neural communication. Disrupted synaptic signaling is closely related to a variety of neurological and psychiatric disorders. In the past two decades, proteomics has blossomed as a versatile tool in biological and biomedical research, rendering a wealth of information toward decoding the molecular machinery of life. There is enormous interest in employing proteomic approaches for the study of synapses, and substantial progress has been made. Here, we review the findings of proteomic studies of chemical synapses in the brain, with special attention paid to the key players in synaptic signaling, i.e., the synaptic protein complexes and their post-translational modifications. Looking toward the future, we discuss the technological advances in proteomics such as data-independent acquisition mass spectrometry (DIA-MS), cross-linking in combination with mass spectrometry (CXMS), and proximity proteomics, along with their potential to untangle the mystery of how the brain functions at the molecular level. Last but not least, we introduce the newly developed synaptomic methods. These methods and their successful applications marked the beginnings of the synaptomics era.


Brain ◽  
2019 ◽  
Vol 142 (12) ◽  
pp. 3991-4002 ◽  
Author(s):  
Martijn P van den Heuvel ◽  
Lianne H Scholtens ◽  
Siemon C de Lange ◽  
Rory Pijnenburg ◽  
Wiepke Cahn ◽  
...  

See Vértes and Seidlitz (doi:10.1093/brain/awz353) for a scientific commentary on this article. Is schizophrenia a by-product of human brain evolution? By comparing the human and chimpanzee connectomes, van den Heuvel et al. demonstrate that connections unique to the human brain show greater involvement in schizophrenia pathology. Modifications in service of higher-order brain functions may have rendered the brain more vulnerable to dysfunction.


2020 ◽  
Vol 49 (D1) ◽  
pp. D1029-D1037
Author(s):  
Liting Song ◽  
Shaojun Pan ◽  
Zichao Zhang ◽  
Longhao Jia ◽  
Wei-Hua Chen ◽  
...  

Abstract The human brain is the most complex organ consisting of billions of neuronal and non-neuronal cells that are organized into distinct anatomical and functional regions. Elucidating the cellular and transcriptome architecture underlying the brain is crucial for understanding brain functions and brain disorders. Thanks to the single-cell RNA sequencing technologies, it is becoming possible to dissect the cellular compositions of the brain. Although great effort has been made to explore the transcriptome architecture of the human brain, a comprehensive database with dynamic cellular compositions and molecular characteristics of the human brain during the lifespan is still not available. Here, we present STAB (a Spatio-Temporal cell Atlas of the human Brain), a database consists of single-cell transcriptomes across multiple brain regions and developmental periods. Right now, STAB contains single-cell gene expression profiling of 42 cell subtypes across 20 brain regions and 11 developmental periods. With STAB, the landscape of cell types and their regional heterogeneity and temporal dynamics across the human brain can be clearly seen, which can help to understand both the development of the normal human brain and the etiology of neuropsychiatric disorders. STAB is available at http://stab.comp-sysbio.org.


Author(s):  
Jack M. Gorman

Some scientists now argue that humans are really not superior to other species, including our nearest genetic neighbors, chimpanzees and bonobos. Indeed, those animals seem capable of many things previously thought to be uniquely human, including a sense of the future, empathy, depression, and theory of mind. However, it is clear that humans alone produce speech, dominate the globe, and have several brain diseases like schizophrenia. There are three possible sources within the brain for these differences in brain function: in the structure of the brain, in genes coding for proteins in the brain, and in the level of expression of genes in the brain. There is evidence that all three are the case, giving us a place to look for the intersection of the human mind and brain: the expression of genes within neurons of the prefrontal cortex.


2020 ◽  
Author(s):  
Sreejan Kumar ◽  
Cameron T. Ellis ◽  
Thomas O’Connell ◽  
Marvin M Chun ◽  
Nicholas B. Turk-Browne

AbstractThe extent to which brain functions are localized or distributed is a foundational question in neuroscience. In the human brain, common fMRI methods such as cluster correction, atlas parcellation, and anatomical searchlight are biased by design toward finding localized representations. Here we introduce the functional searchlight approach as an alternative to anatomical searchlight analysis, the most commonly used exploratory multivariate fMRI technique. Functional searchlight removes any anatomical bias by grouping voxels based only on functional similarity and ignoring anatomical proximity. We report evidence that visual and auditory features from deep neural networks and semantic features from a natural language processing model are more widely distributed across the brain than previously acknowledged. This approach provides a new way to evaluate and constrain computational models with brain activity and pushes our understanding of human brain function further along the spectrum from strict modularity toward distributed representation.


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