arXiv: Neurons and Cognition最新文献_第8页

Columnar Electromagnetic Influences on Short-Term Memory at Multiple Scales 柱状电磁对多尺度短期记忆的影响

arXiv: Neurons and Cognition Pub Date : 2011-05-11 DOI: 10.2139/SSRN.1838903

L. Ingber

{"title":"Columnar Electromagnetic Influences on Short-Term Memory at Multiple Scales","authors":"L. Ingber","doi":"10.2139/SSRN.1838903","DOIUrl":"https://doi.org/10.2139/SSRN.1838903","url":null,"abstract":"For several decades the stated Holy Grail of chemical, biological and biophysical research into neocortical information processing has been to reduce such neocortical phenomena into specific bottom-up molecular and smaller-scale processes. Over the past three decades, with regard to short-term memory (STM) and long-term memory (LTM) phenomena, which themselves are likely components of other phenomena like attention and consciousness, a statistical mechanics of neocortical interactions (SMNI) approach has yielded specific details of STM capacity, duration and stability not present in molecular approaches, but it is clear that most molecular approaches consider it inevitable that their reductionist approaches at molecular and possibly even quantum scales will yet prove to be causal explanations of such phenomena. The SMNI approach is a bottom-up aggregation from synaptic scales to columnar and regional scales of neocortex, and has been merged with larger non-invasive EEG scales with other colleagues – all at scales much coarser than molecular scales. As with many Crusades for some truths, other truths can be trampled. It is proposed that an SMNI vector potential (SMNI-VP) constructed from magnetic fields induced by neuronal electrical firings, at thresholds of collective minicolumnar activity with laminar specification, can give rise to causal top-down mechanisms that effect molecular excitatory and inhibitory processes in STM and LTM. A specific example might be causal influences on momentum $mathbf{p}$ of Ca$^{2 }$ ions by the SMNI-VP $mathbf{A}$, as calculated by the canonical momentum $mathbf{q}$, $mathbf{q} = mathbf{p} - e mathbf{A}$, where $e$ is the electron coulomb charge and $c$ is the speed of light, which may be applied either classically or quantum-mechanically. Such a smoking gun for top-down effects awaits forensic in vivo experimental verification, requiring appreciating the necessity and due diligence of including true multiple-scale interactions across orders of magnitude in the complex neocortical environment.","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123232512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Interactions between Intrinsic and Stimulus-Evoked Activity in Recurrent Neural Networks 递归神经网络内禀活动与刺激诱发活动的相互作用

arXiv: Neurons and Cognition Pub Date : 2009-12-18 DOI: 10.1093/acprof:oso/9780195393798.003.0004

L. Abbott, Kanaka Rajan, H. Sompolinsky

{"title":"Interactions between Intrinsic and Stimulus-Evoked Activity in Recurrent Neural Networks","authors":"L. Abbott, Kanaka Rajan, H. Sompolinsky","doi":"10.1093/acprof:oso/9780195393798.003.0004","DOIUrl":"https://doi.org/10.1093/acprof:oso/9780195393798.003.0004","url":null,"abstract":"Trial-to-trial variability is an essential feature of neural responses, but its source is a subject of active debate. Response variability (Mast and Victor, 1991; Arieli et al., 1995 & 1996; Anderson et al., 2000 & 2001; Kenet et al., 2003; Petersen et al., 2003a & b; Fiser, Chiu and Weliky, 2004; MacLean et al., 2005; Yuste et al., 2005; Vincent et al., 2007) is often treated as random noise, generated either by other brain areas, or by stochastic processes within the circuitry being studied. We call such sources of variability external to stress the independence of this form of noise from activity driven by the stimulus. Variability can also be generated internally by the same network dynamics that generates responses to a stimulus. How can we distinguish between external and internal sources of response variability? Here we show that internal sources of variability interact nonlinearly with stimulus-induced activity, and this interaction yields a suppression of noise in the evoked state. This provides a theoretical basis and potential mechanism for the experimental observation that, in many brain areas, stimuli cause significant suppression of neuronal variability (Werner and Mountcastle, 1963; Fortier, Smith and Kalaska, 1993; Anderson et al., 2000; Friedrich and Laurent, 2004; Churchland et al., 2006; Finn, Priebe and Ferster, 2007; Mitchell, Sundberg and Reynolds, 2007; Churchland et al., 2009). The combined theoretical and experimental results suggest that internally generated activity is a significant contributor to response variability in neural circuits.","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115308624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 77

The baseline for response latency distributions 响应延迟分布的基线

arXiv: Neurons and Cognition Pub Date : 2009-08-16 DOI: 10.1038/NPRE.2009.3622.1

F. Mart'in

引用次数: 2

Neuronal Coding of pacemaker neurons - A random dynamical systems approach 起搏器神经元的神经元编码-随机动力系统方法

arXiv: Neurons and Cognition Pub Date : 2009-03-26 DOI: 10.3934/CPAA.2011.10.995

T. Jaeger

引用次数: 2

Macro- and Microscopic Self-Similarity in Neuro- and Psycho-Dynamics 神经和心理动力学中的宏观和微观自相似性

arXiv: Neurons and Cognition Pub Date : 2008-05-23 DOI: 10.1142/S1793524509000698

V. Ivancevic, T. Ivancevic

引用次数: 1

Reliability of Layered Neural Oscillator Networks 层状神经振荡器网络的可靠性

arXiv: Neurons and Cognition Pub Date : 2008-05-22 DOI: 10.4310/CMS.2009.V7.N1.A12

Kevin K. Lin, E. Shea-Brown, L. Young

引用次数: 23

On the Fitzhugh-Nagumo model 在Fitzhugh-Nagumo模型上

arXiv: Neurons and Cognition Pub Date : 2008-04-01 DOI: 10.1142/9789812772350_0029

M. Angelis, P. Renno

引用次数: 7

Vortices in brain waves 脑电波中的漩涡

arXiv: Neurons and Cognition Pub Date : 2008-02-26 DOI: 10.1142/S0217979210056025

W. Freeman, G. Vitiello

引用次数: 43

Autonomous dynamics in neural networks: the dHAN concept and associative thought processes 神经网络中的自主动力学:dHAN概念和联想思维过程

arXiv: Neurons and Cognition Pub Date : 2007-02-22 DOI: 10.1063/1.2709594

C. Gros

引用次数: 2

The brain near the edge 靠近边缘的大脑

arXiv: Neurons and Cognition Pub Date : 2006-10-22 DOI: 10.1063/1.2709580

D. Chialvo

引用次数: 31