Finding the Engram
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Deciphering Neural Codes of Memory During Sleep
Deciphering Neural Codes of Memory during Sleep The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Chen, Zhe and Matthew A. Wilson. "Deciphering Neural Codes of Memory during Sleep." Trends in Neurosciences 40, 5 (May 2017): 260-275 © 2017 Elsevier Ltd As Published http://dx.doi.org/10.1016/j.tins.2017.03.005 Publisher Elsevier BV Version Author's final manuscript Citable link https://hdl.handle.net/1721.1/122457 Terms of Use Creative Commons Attribution-NonCommercial-NoDerivs License Detailed Terms http://creativecommons.org/licenses/by-nc-nd/4.0/ HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Trends Neurosci Manuscript Author . Author Manuscript Author manuscript; available in PMC 2018 May 01. Published in final edited form as: Trends Neurosci. 2017 May ; 40(5): 260–275. doi:10.1016/j.tins.2017.03.005. Deciphering Neural Codes of Memory during Sleep Zhe Chen1,* and Matthew A. Wilson2,* 1Department of Psychiatry, Department of Neuroscience & Physiology, New York University School of Medicine, New York, NY 10016, USA 2Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA Abstract Memories of experiences are stored in the cerebral cortex. Sleep is critical for consolidating hippocampal memory of wake experiences into the neocortex. Understanding representations of neural codes of hippocampal-neocortical networks during sleep would reveal important circuit mechanisms on memory consolidation, and provide novel insights into memory and dreams. Although sleep-associated ensemble spike activity has been investigated, identifying the content of memory in sleep remains challenging. -
Specialized Coding Patterns Among Dorsomedial Prefrontal Neuronal Ensembles Predict Conditioned Reward Seeking
RESEARCH ARTICLE Specialized coding patterns among dorsomedial prefrontal neuronal ensembles predict conditioned reward seeking Roger I Grant1, Elizabeth M Doncheck1, Kelsey M Vollmer1, Kion T Winston1, Elizaveta V Romanova1, Preston N Siegler1, Heather Holman1, Christopher W Bowen1, James M Otis1,2* 1Department of Neuroscience, Medical University of South Carolina, Charleston, United States; 2Hollings Cancer Center, Medical University of South Carolina, Charleston, United States Abstract Non-overlapping cell populations within dorsomedial prefrontal cortex (dmPFC), defined by gene expression or projection target, control dissociable aspects of reward seeking through unique activity patterns. However, even within these defined cell populations, considerable cell-to-cell variability is found, suggesting that greater resolution is needed to understand information processing in dmPFC. Here, we use two-photon calcium imaging in awake, behaving mice to monitor the activity of dmPFC excitatory neurons throughout Pavlovian reward conditioning. We characterize five unique neuronal ensembles that each encodes specialized information related to a sucrose reward, reward-predictive cues, and behavioral responses to those cues. The ensembles differentially emerge across daily training sessions – and stabilize after learning – in a manner that improves the predictive validity of dmPFC activity dynamics for deciphering variables related to behavioral conditioning. Our results characterize the complex dmPFC neuronal ensemble dynamics that stably predict -
From the Agentic Perspective of Social Cognitive Theory
“free will” from the agentic perspective 87 of language and abstract and deliberative cognitive capacities provided the neuronal structure for supplanting aimless environmental selection with cog- nitive agency. Human forebears evolved into a sentient agentic species. Their advanced symbolizing capacity enabled humans to transcend the dictates of their immediate environment and made them unique in their power to shape their circumstances and life courses. Through cognitive self-guidance, humans can visualize futures that act on the present, order preferences rooted in per- sonal values, construct, evaluate, and modify alternative courses of action to secure valued outcomes, and override environmental influences. The present chapter addresses the issue of free will from the agentic per- spective of social cognitive theory (Bandura, 1986, 2006). To be an agent is to influence intentionally one’s functioning and the course of environmental events. People are contributors to their life circumstances not just products of them. In this view, personal influence is part of the determining conditions gov- erning self-development, adaptation, and change. There are four core properties of human agency. One such property is intentionality. People form intentions that include action plans and strategies for realizing them. Most human pursuits involve other participating agents so there is no absolute agency. They have to negotiate and accommodate their self-interests to achieve unity of effort within diversity. Collective endeavors require commitment to a shared intention and coordination of interdependent plans of action to realize it (Bratman, 1999). Effective group performance is guided by collective intentionally. The second feature involves the temporal extension of agency through fore- thought. -
Dynamics of Memory Engrams
G Model NSR-4275; No. of Pages 5 ARTICLE IN PRESS Neuroscience Research xxx (2019) xxx–xxx Contents lists available at ScienceDirect Neuroscience Research jo urnal homepage: www.elsevier.com/locate/neures Update Article Dynamics of memory engrams ∗ Shogo Takamiya, Shoko Yuki, Junya Hirokawa, Hiroyuki Manabe, Yoshio Sakurai Laboratory of Neural Information, Graduate School of Brain Science, Doshisha University, Kyotanabe 610-0394, Kyoto, Japan a r t i c l e i n f o a b s t r a c t Article history: In this update article, we focus on “memory engrams”, which are traces of long-term memory in the brain, Received 21 February 2019 and emphasizes that they are not static but dynamic. We first introduce the major findings in neuroscience Received in revised form 18 March 2019 and psychology reporting that memory engrams are sometimes diffuse and unstable, indicating that Accepted 27 March 2019 they are dynamically modified processes of consolidation and reconsolidation. Second, we introduce and Available online xxx discuss the concepts of cell assembly and engram cell, the former has been investigated by psychological experiments and behavioral electrophysiology and the latter is defined by recent combination of activity- Keywords: dependent cell labelling with optogenetics to show causal relationships between cell population activity Memory engram and behavioral changes. Third, we discuss the similarities and differences between the cell assembly and Memory reconsolidation engram cell concepts to reveal the dynamics of memory engrams. We also discuss the advantages and Cell assembly Engram cell problems of live-cell imaging, which has recently been developed to visualize multineuronal activities. -
A Single-Neuron: Current Trends and Future Prospects
cells Review A Single-Neuron: Current Trends and Future Prospects Pallavi Gupta 1, Nandhini Balasubramaniam 1, Hwan-You Chang 2, Fan-Gang Tseng 3 and Tuhin Subhra Santra 1,* 1 Department of Engineering Design, Indian Institute of Technology Madras, Tamil Nadu 600036, India; [email protected] (P.G.); [email protected] (N.B.) 2 Department of Medical Science, National Tsing Hua University, Hsinchu 30013, Taiwan; [email protected] 3 Department of Engineering and System Science, National Tsing Hua University, Hsinchu 30013, Taiwan; [email protected] * Correspondence: [email protected] or [email protected]; Tel.: +91-044-2257-4747 Received: 29 April 2020; Accepted: 19 June 2020; Published: 23 June 2020 Abstract: The brain is an intricate network with complex organizational principles facilitating a concerted communication between single-neurons, distinct neuron populations, and remote brain areas. The communication, technically referred to as connectivity, between single-neurons, is the center of many investigations aimed at elucidating pathophysiology, anatomical differences, and structural and functional features. In comparison with bulk analysis, single-neuron analysis can provide precise information about neurons or even sub-neuron level electrophysiology, anatomical differences, pathophysiology, structural and functional features, in addition to their communications with other neurons, and can promote essential information to understand the brain and its activity. This review highlights various single-neuron models and their behaviors, followed by different analysis methods. Again, to elucidate cellular dynamics in terms of electrophysiology at the single-neuron level, we emphasize in detail the role of single-neuron mapping and electrophysiological recording. We also elaborate on the recent development of single-neuron isolation, manipulation, and therapeutic progress using advanced micro/nanofluidic devices, as well as microinjection, electroporation, microelectrode array, optical transfection, optogenetic techniques. -
The Emergence of Synchrony in Networks of Mutually Inferring Neurons
www.nature.com/scientificreports OPEN The emergence of synchrony in networks of mutually inferring neurons Received: 6 November 2018 Ensor Rafael Palacios1, Takuya Isomura 2, Thomas Parr1 & Karl Friston 1 Accepted: 8 April 2019 This paper considers the emergence of a generalised synchrony in ensembles of coupled self-organising Published: xx xx xxxx systems, such as neurons. We start from the premise that any self-organising system complies with the free energy principle, in virtue of placing an upper bound on its entropy. Crucially, the free energy principle allows one to interpret biological systems as inferring the state of their environment or external milieu. An emergent property of this inference is synchronisation among an ensemble of systems that infer each other. Here, we investigate the implications of neuronal dynamics by simulating neuronal networks, where each neuron minimises its free energy. We cast the ensuing ensemble dynamics in terms of inference and show that cardinal behaviours of neuronal networks – both in vivo and in vitro – can be explained by this framework. In particular, we test the hypotheses that (i) generalised synchrony is an emergent property of free energy minimisation; thereby explaining synchronisation in the resting brain: (ii) desynchronisation is induced by exogenous input; thereby explaining event-related desynchronisation and (iii) structure learning emerges in response to causal structure in exogenous input; thereby explaining functional segregation in real neuronal systems. Any biological or self-organising system is characterised by the ability to maintain itself in a changing envi- ronment. Te separation of a system from its environment – and the implicit delineation of its boundaries – mandates autopoietic, autonomous behaviour1. -
On the Trail of the Hippocampal Engram
Physiological Psychology 1980, Vol. 8 (2),229-238 On the trail of the hippocampal engram JOHN O'KEEFE and DULCIE H. CONWAY Cerebral Functions Group, Anatomy Department and Centre/or Neuroscience University College London, London WCIE 6BT, England Recent ideas about hippocampal function in animals suggest that it may be involved in memory. A new test for rat memory is described (the despatch task). It has several properties which make it well suited for the study of one-trial, long-term memory. The information about the location of the goal is given to the rat when it is in the startbox. The memory formed is long lasting (> 30 min), and the animals are remembering the location of the goal and not the response to be made to reach that goal. The spatial relations of the cues within the environ ment can be manipulated to bias the animals towards selection of a place hypothesis or a guidance hypothesis. The latter does not seem to support long-term memory formation in the way that the former does. Fornix lesions selectively disrupt performance of the place-biased task, a finding that is predicted by the cognitive map theory but not by other ideas about hippocampal involvement in memory. Since 1957, when Scoville and Milner (1957) reported is little interference between different place representa that bilateral removal of the mesial temporal lobes in tions. Consequently, the mapping system should be man resulted in a profound global amnesia, it has gener capable of supporting high levels of performance in tests ally been accepted that the hippocampus plays an of animal memory, such as the delayed reaction task important role in human memory (but see Horel, 1978, of Hunter (1913)- for an important paper questioning this dogma). -
Searching for the Memory New Research Sheds Light—Literally—On Recall Mechanisms by Christof Koch
(consciousness redux) Searching for the Memory New research sheds light—literally—on recall mechanisms BY CHRISTOF KOCH The difference between false gion as far as learning is concerned. page 76], and you will not be able to memories and true ones is the The most singular feature of science form new explicit memories, whereas same as for jewels: it is always the that distinguishes it from other human losses of large swaths of visual cortex false ones that look the most real, activities, such as art or religion, and leave the subjects blind but without the most brilliant. gives it a dynamics of its own, is prog- memory impairments. Yet percepts and memo- THIS QUOTE by surrealist ries are not born of brain re- painter Salvador Dalí comes gions but arise within intri- to mind when pondering the cate networks of neurons, latest wizardry coming out of connected by synapses. Neu- two neurobiology laborato- rons, rather than chunks of ries. Before we come to that, brain, are the atoms of however, let us remember thoughts, consciousness and that ever since Plato and Aris- remembering. totle first likened memories to impressions made onto wax Implanting a False tablets, philosophers and nat- Memory in Mice ural scientists have searched If you have ever been the for the physical substrate of victim of a mugging in a des- memories. In the first half of olate parking garage, you the 20th-century psycholo- may carry that occurrence gists carried out carefully with you to the end of your controlled experiments to days. Worse, whenever you look for the so-called memo- walk into a parking struc- ry engram in the brain. -
Universal Memory Mechanism for Familiarity Recognition and Identification
The Journal of Neuroscience, January 2, 2008 • 28(1):239–248 • 239 Behavioral/Systems/Cognitive Universal Memory Mechanism for Familiarity Recognition and Identification Volodya Yakovlev,1 Daniel J. Amit,2,3† Sandro Romani,4 and Shaul Hochstein1 1Life Sciences Institute & Neural Computation Center and 2Racah Institute of Physics, Hebrew University, Jerusalem 91904, Israel, and 3Department of Physics and 4Doctoral Program in Neurophysiology, Department of Human Physiology, University of Rome “La Sapienza,” 00185 Rome, Italy Macaque monkeys were tested on a delayed-match-to-multiple-sample task, with either a limited set of well trained images (in random- ized sequence) or with never-before-seen images. They performed much better with novel images. False positives were mostly limited to catch-trial image repetitions from the preceding trial. This result implies extremely effective one-shot learning, resembling Standing’s finding that people detect familiarity for 10,000 once-seen pictures (with 80% accuracy) (Standing, 1973). Familiarity memory may differ essentially from identification, which embeds and generates contextual information. When encountering another person, we can say immediately whether his or her face is familiar. However, it may be difficult for us to identify the same person. To accompany the psychophysical findings, we present a generic neural network model reproducing these behaviors, based on the same conservative Hebbian synaptic plasticity that generates delay activity identification memory. Familiarity becomes the first step toward establishing identification. Adding an inter-trial reset mechanism limits false positives for previous-trial images. The model, unlike previous propos- als, relates repetition–recognition with enhanced neural activity, as recently observed experimentally in 92% of differential cells in prefrontal cortex, an area directly involved in familiarity recognition. -
Memory Engram Storage and Retrieval
Available online at www.sciencedirect.com ScienceDirect Memory engram storage and retrieval 1,2 1 1 Susumu Tonegawa , Michele Pignatelli , Dheeraj S Roy and 1,2 Toma´ s J Ryan A great deal of experimental investment is directed towards likely distributed nature of a memory engram throughout questions regarding the mechanisms of memory storage. Such the brain [6 ]. Here we review recent experimental stud- studies have traditionally been restricted to investigation of the ies on the identification of memory engram cells, with a anatomical structures, physiological processes, and molecular focus on the mechanisms of memory storage. A more pathways necessary for the capacity of memory storage, and comprehensive review of recent memory engram studies have avoided the question of how individual memories are is available elsewhere [7 ]. stored in the brain. Memory engram technology allows the labeling and subsequent manipulation of components of Memory function and the hippocampus specific memory engrams in particular brain regions, and it has The medial temporal lobe (MTL), in particular the hip- been established that cell ensembles labeled by this method are both sufficient and necessary for memory recall. Recent pocampus, was implicated in memory of events or episodes research has employed this technology to probe fundamental by neurological studies of human clinical patients, where questions of memory consolidation, differentiating between its direct electrophysiological stimulation evoked the recall mechanisms of memory retrieval from the -
The Brain in Motion: How Ensemble Fluidity Drives Memory-Updating and Flexibility William Mau1*, Michael E Hasselmo2, Denise J Cai1*
REVIEW ARTICLE The brain in motion: How ensemble fluidity drives memory-updating and flexibility William Mau1*, Michael E Hasselmo2, Denise J Cai1* 1Neuroscience Department, Icahn School of Medicine at Mount Sinai, New York, United States; 2Center for Systems Neuroscience, Boston University, Boston, United States Abstract While memories are often thought of as flashbacks to a previous experience, they do not simply conserve veridical representations of the past but must continually integrate new information to ensure survival in dynamic environments. Therefore, ‘drift’ in neural firing patterns, typically construed as disruptive ‘instability’ or an undesirable consequence of noise, may actually be useful for updating memories. In our view, continual modifications in memory representations reconcile classical theories of stable memory traces with neural drift. Here we review how memory representations are updated through dynamic recruitment of neuronal ensembles on the basis of excitability and functional connectivity at the time of learning. Overall, we emphasize the importance of considering memories not as static entities, but instead as flexible network states that reactivate and evolve across time and experience. Introduction Memories are neural patterns that guide behavior in familiar situations by preserving relevant infor- mation about the past. While this definition is simple in theory, in practice, environments are *For correspondence: [email protected] (WM); dynamic and probabilistic, leaving the brain with the difficult task of shaping memory representa- [email protected] (DJC) tions to address this challenge. Dynamic environments imply that whatever is learned from a single episode may not hold true for future related experiences and should therefore be updated over Competing interests: The time. -
Analysis of Neuronal Ensemble Activity Reveals the Pitfalls and Shortcomings of Rotation Dynamics Mikhail A
www.nature.com/scientificreports OPEN Analysis of neuronal ensemble activity reveals the pitfalls and shortcomings of rotation dynamics Mikhail A. Lebedev1, Alexei Ossadtchi2, Nil Adell Mill 3, Núria Armengol Urpí4, Maria R. Cervera3 & Miguel A. L. Nicolelis1,5,6,7,8,9* Back in 2012, Churchland and his colleagues proposed that “rotational dynamics”, uncovered through linear transformations of multidimensional neuronal data, represent a fundamental type of neuronal population processing in a variety of organisms, from the isolated leech central nervous system to the primate motor cortex. Here, we evaluated this claim using Churchland’s own data and simple simulations of neuronal responses. We observed that rotational patterns occurred in neuronal populations when (1) there was a temporal sequence in peak fring rates exhibited by individual neurons, and (2) this sequence remained consistent across diferent experimental conditions. Provided that such a temporal order of peak fring rates existed, rotational patterns could be easily obtained using a rather arbitrary computer simulation of neural activity; modeling of any realistic properties of motor cortical responses was not needed. Additionally, arbitrary traces, such as Lissajous curves, could be easily obtained from Churchland’s data with multiple linear regression. While these observations suggest that temporal sequences of neuronal responses could be visualized as rotations with various methods, we express doubt about Churchland et al.’s bold assessment that such rotations are related to “an unexpected yet surprisingly simple structure in the population response”, which “explains many of the confusing features of individual neural responses”. Instead, we argue that their approach provides little, if any, insight on the underlying neuronal mechanisms employed by neuronal ensembles to encode motor behaviors in any species.