Pub Date : 2013-04-15DOI: 10.14293/S2199-1006.1.SOR-LIFE.AEKZPZ.v1
Karin Vadovivcov'a, R. Gasparotti
We propose that dACC, AI and caudolateral OFC(clOFC) project to lateral habenula (LHb) and D2 loop of ventral striatum (VS), forming a functional adversity processing circuit, directed towards inhibitory avoidance and self-control. This circuit learns what is bad or harmful to us and predicts risks, to stop us from going/moving for bad or suboptimal choices that decrease our well-being and survival chances. Proposed dACC role is to generate a WARNING signal when things are going (or might end) bad or wrong to prevent negative consequences: pain, harm, loss or failure. The AI signals about bad low aversive qualities, which make us sick or cause discomfort. These cortical inputs activate directly and indirectly (via D2 loop of VS) the LHb, which inhibits dopamine and serotonin release (and is reciprocally inhibited by VTA, DRN) to avoid choosing and doing things leading to harm or loss, but also to make us feel worse, down when overstimulated. We propose that dopamine attenuates the output of the adversity processing circuit, thus decreasing inhibitory avoidance and self-control, while serotonin attenuates dACC, AI, clOFC, D1 loop of VS, LHb, amygdala and pain pathway. Thus, by reciprocal inhibition, by causing dopamine and serotonin suppression - and by being suppressed by them, the adversity processing circuit competes with reward processing circuit for control of choice behaviour and affective states. We propose stimulating effect of dopamine and calming inhibitory effect of serotonin on the active avoidance circuit involving amygdala, linked to threat processing, anger, fear, self-defense and violences. We describe causes and roles of dopamine and serotonin signaling, and mental dysfunctions. We add new idea on vACC role in signaling that we are doing well and in inducing serotonin, when we gain/reach safety, comfort, valuable resources, social/biological rewards, affection or goals.
{"title":"Reward and adversity processing circuits, their competition and interactions with dopamine and serotonin signaling","authors":"Karin Vadovivcov'a, R. Gasparotti","doi":"10.14293/S2199-1006.1.SOR-LIFE.AEKZPZ.v1","DOIUrl":"https://doi.org/10.14293/S2199-1006.1.SOR-LIFE.AEKZPZ.v1","url":null,"abstract":"We propose that dACC, AI and caudolateral OFC(clOFC) project to lateral habenula (LHb) and D2 loop of ventral striatum (VS), forming a functional adversity processing circuit, directed towards inhibitory avoidance and self-control. This circuit learns what is bad or harmful to us and predicts risks, to stop us from going/moving for bad or suboptimal choices that decrease our well-being and survival chances. Proposed dACC role is to generate a WARNING signal when things are going (or might end) bad or wrong to prevent negative consequences: pain, harm, loss or failure. The AI signals about bad low aversive qualities, which make us sick or cause discomfort. These cortical inputs activate directly and indirectly (via D2 loop of VS) the LHb, which inhibits dopamine and serotonin release (and is reciprocally inhibited by VTA, DRN) to avoid choosing and doing things leading to harm or loss, but also to make us feel worse, down when overstimulated. We propose that dopamine attenuates the output of the adversity processing circuit, thus decreasing inhibitory avoidance and self-control, while serotonin attenuates dACC, AI, clOFC, D1 loop of VS, LHb, amygdala and pain pathway. Thus, by reciprocal inhibition, by causing dopamine and serotonin suppression - and by being suppressed by them, the adversity processing circuit competes with reward processing circuit for control of choice behaviour and affective states. We propose stimulating effect of dopamine and calming inhibitory effect of serotonin on the active avoidance circuit involving amygdala, linked to threat processing, anger, fear, self-defense and violences. We describe causes and roles of dopamine and serotonin signaling, and mental dysfunctions. We add new idea on vACC role in signaling that we are doing well and in inducing serotonin, when we gain/reach safety, comfort, valuable resources, social/biological rewards, affection or goals.","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123742579","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}
Pub Date : 2013-02-14DOI: 10.1007/978-3-319-27635-9_26
Vesna Vuksanovi'c, P. Hovel
{"title":"Large-scale neural network model for functional networks of the human cortex","authors":"Vesna Vuksanovi'c, P. Hovel","doi":"10.1007/978-3-319-27635-9_26","DOIUrl":"https://doi.org/10.1007/978-3-319-27635-9_26","url":null,"abstract":"","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117169736","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}
In this essay we elaborate on recent evidence demonstrating the presence of a second order phase transition in human brain dynamics and discuss its consequences for theoretical approaches to brain function. We review early evidence of criticality in brain dynamics at different spatial and temporal scales, and we stress how it was necessary to unify concepts and analysis techniques across scales to introduce the adequate order and control parameters which define the transition. A discussion on the relation between structural vs. dynamical complexity exposes future steps to understand the dynamics of the connectome (structure) from which emerges the cognitome (function).
{"title":"Brain complexity born out of criticality","authors":"E. Tagliazucchi, D. Chialvo","doi":"10.1063/1.4776495","DOIUrl":"https://doi.org/10.1063/1.4776495","url":null,"abstract":"In this essay we elaborate on recent evidence demonstrating the presence of a second order phase transition in human brain dynamics and discuss its consequences for theoretical approaches to brain function. We review early evidence of criticality in brain dynamics at different spatial and temporal scales, and we stress how it was necessary to unify concepts and analysis techniques across scales to introduce the adequate order and control parameters which define the transition. A discussion on the relation between structural vs. dynamical complexity exposes future steps to understand the dynamics of the connectome (structure) from which emerges the cognitome (function).","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132251723","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}
Pub Date : 2012-10-12DOI: 10.1002/9783527651009.CH3
D. Chialvo
Highly correlated brain dynamics produces synchronized states with no behavioral value, while weakly correlated dynamics prevent information flow. In between these states, the unique dynamical features of the critical state endow the brain with properties which are fundamental for adaptive behavior. We discuss the idea put forward two decades ago by Per Bak that the working brain stays at an intermediate (critical) regime characterized by power-law correlations. This proposal is now supported by a wide body of empirical evidence at different scales demonstrating that the spatiotemporal brain dynamics exhibit key signatures of critical dynamics, previously recognized in other complex systems. The rationale behind this program is discussed in these notes, followed by an account of the most recent results.
{"title":"Critical Brain Dynamics at Large Scale","authors":"D. Chialvo","doi":"10.1002/9783527651009.CH3","DOIUrl":"https://doi.org/10.1002/9783527651009.CH3","url":null,"abstract":"Highly correlated brain dynamics produces synchronized states with no behavioral value, while weakly correlated dynamics prevent information flow. In between these states, the unique dynamical features of the critical state endow the brain with properties which are fundamental for adaptive behavior. We discuss the idea put forward two decades ago by Per Bak that the working brain stays at an intermediate (critical) regime characterized by power-law correlations. This proposal is now supported by a wide body of empirical evidence at different scales demonstrating that the spatiotemporal brain dynamics exhibit key signatures of critical dynamics, previously recognized in other complex systems. The rationale behind this program is discussed in these notes, followed by an account of the most recent results.","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131711145","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}
In this chapter we first outline some of the popular computing environments used for analysing neural data, followed by a brief discussion of 'software carpentry', basic tools and skills from software engineering that can be of great use to computational scientists. We then introduce the concept of open-source software and explain some of its potential benefits for the academic community before giving a brief directory of some freely available open source software packages that address various aspects of the study of neural codes. While there are many commercial offerings that provide similar functionality, we concentrate here on open source packages, which in addition to being available free of charge, also have the source code available for study and modification.
{"title":"Open-Source Software for Studying Neural Codes","authors":"Robin A. A. Ince","doi":"10.1201/b14756-35","DOIUrl":"https://doi.org/10.1201/b14756-35","url":null,"abstract":"In this chapter we first outline some of the popular computing environments used for analysing neural data, followed by a brief discussion of 'software carpentry', basic tools and skills from software engineering that can be of great use to computational scientists. We then introduce the concept of open-source software and explain some of its potential benefits for the academic community before giving a brief directory of some freely available open source software packages that address various aspects of the study of neural codes. While there are many commercial offerings that provide similar functionality, we concentrate here on open source packages, which in addition to being available free of charge, also have the source code available for study and modification.","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115024977","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}
Pub Date : 2012-06-06DOI: 10.1002/9783527651009.CH7
R. Kozma, M. Puljic, W. Freeman
Criticality in the cortex emerges from the seemingly random interaction of microscopic components and produces higher cognitive functions at mesoscopic and macroscopic scales. Random graphs and percolation theory provide natural means to de- scribe critical regions in the behavior of the cortex and they are proposed here as novel mathematical tools helping us deciphering the language of the brain.
{"title":"Thermodynamic Model of Criticality in the Cortex Based On EEG/ECOG Data","authors":"R. Kozma, M. Puljic, W. Freeman","doi":"10.1002/9783527651009.CH7","DOIUrl":"https://doi.org/10.1002/9783527651009.CH7","url":null,"abstract":"Criticality in the cortex emerges from the seemingly random interaction of microscopic components and produces higher cognitive functions at mesoscopic and macroscopic scales. Random graphs and percolation theory provide natural means to de- scribe critical regions in the behavior of the cortex and they are proposed here as novel mathematical tools helping us deciphering the language of the brain.","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125300337","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}
R. Stoop, Victor Saase, C. Wagner, Britta Stoop, R. Stoop
It is widely believed that the particular wiring observed within cortical columns boosts neural computation. We use rewiring of neural networks performing real-world cognitive tasks to study the validity of this argument. In a vast survey of wirings within the column we detect, however, no traces of the proposed effect. It is on the mesoscopic inter-columnar scale that the existence of columns - largely irrespective of their inner organization - enhances the speed of information transfer and minimizes the total wiring length required to bind the distributed columnar computations towards spatio-temporally coherent results.
{"title":"Cortical columns for quick brains","authors":"R. Stoop, Victor Saase, C. Wagner, Britta Stoop, R. Stoop","doi":"10.15248/proc.1.852","DOIUrl":"https://doi.org/10.15248/proc.1.852","url":null,"abstract":"It is widely believed that the particular wiring observed within cortical columns boosts neural computation. We use rewiring of neural networks performing real-world cognitive tasks to study the validity of this argument. In a vast survey of wirings within the column we detect, however, no traces of the proposed effect. It is on the mesoscopic inter-columnar scale that the existence of columns - largely irrespective of their inner organization - enhances the speed of information transfer and minimizes the total wiring length required to bind the distributed columnar computations towards spatio-temporally coherent results.","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128731800","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}
Pub Date : 2011-12-21DOI: 10.1007/978-94-007-4491-2_3
M. Ventra, Y. Pershin
{"title":"Biologically-Inspired Electronics with Memory Circuit Elements","authors":"M. Ventra, Y. Pershin","doi":"10.1007/978-94-007-4491-2_3","DOIUrl":"https://doi.org/10.1007/978-94-007-4491-2_3","url":null,"abstract":"","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122830390","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}
Correlations among spikes, both on the same neuron and across neurons, are ubiquitous in the brain. For example cross-correlograms can have large peaks, at least in the periphery, and smaller -- but still non-negligible -- ones in cortex, and auto-correlograms almost always exhibit non-trivial temporal structure at a range of timescales. Although this has been known for over forty years, it's still not clear what role these correlations play in the brain -- and, indeed, whether they play any role at all. The goal of this chapter is to shed light on this issue by reviewing some of the work on this subject.
{"title":"Role of correlations in population coding","authors":"P. Latham, Y. Roudi","doi":"10.1201/b14756-9","DOIUrl":"https://doi.org/10.1201/b14756-9","url":null,"abstract":"Correlations among spikes, both on the same neuron and across neurons, are ubiquitous in the brain. For example cross-correlograms can have large peaks, at least in the periphery, and smaller -- but still non-negligible -- ones in cortex, and auto-correlograms almost always exhibit non-trivial temporal structure at a range of timescales. Although this has been known for over forty years, it's still not clear what role these correlations play in the brain -- and, indeed, whether they play any role at all. The goal of this chapter is to shed light on this issue by reviewing some of the work on this subject.","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133459538","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}
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