I present a theory of schizophrenia (SZ) that mechanistically explains its etiology, symptoms, pathophysiology, and treatment. SZ involves the chronic release of membrane polyunsaturated fatty acids (PUFAs) and their utilization for the synthesis of stress-induced plasticity agents such as endocannabinoids (ECBs). The causal event in SZ is prolonged stress during a sensitive period, which can induce prolonged and heritable changes. The physiological effect of the released PUFAs and their products is to disconnect neurons from their inputs and promote intrinsic excitability. I show that these effects can explain the positive, negative, cognitive, and mood symptoms of SZ, as well as the mechanisms of many known triggers of psychosis. The theory is supported by overwhelming evidence addressing lipids, immunity, ECBs, neuromodulators, hormones, neurotransmitters, and cortical parameters in SZ. It explains why antipsychotic drugs are effective against positive symptoms, and why they do not affect the other symptoms. Finally, I present promising treatment directions implied by the theory, including some that are immediately available.
{"title":"A Polyunsaturated Fatty Acid (PUFA) Theory of Schizophrenia","authors":"Ari Rappoport","doi":"arxiv-2408.06794","DOIUrl":"https://doi.org/arxiv-2408.06794","url":null,"abstract":"I present a theory of schizophrenia (SZ) that mechanistically explains its\u0000etiology, symptoms, pathophysiology, and treatment. SZ involves the chronic\u0000release of membrane polyunsaturated fatty acids (PUFAs) and their utilization\u0000for the synthesis of stress-induced plasticity agents such as endocannabinoids\u0000(ECBs). The causal event in SZ is prolonged stress during a sensitive period,\u0000which can induce prolonged and heritable changes. The physiological effect of\u0000the released PUFAs and their products is to disconnect neurons from their\u0000inputs and promote intrinsic excitability. I show that these effects can\u0000explain the positive, negative, cognitive, and mood symptoms of SZ, as well as\u0000the mechanisms of many known triggers of psychosis. The theory is supported by\u0000overwhelming evidence addressing lipids, immunity, ECBs, neuromodulators,\u0000hormones, neurotransmitters, and cortical parameters in SZ. It explains why\u0000antipsychotic drugs are effective against positive symptoms, and why they do\u0000not affect the other symptoms. Finally, I present promising treatment\u0000directions implied by the theory, including some that are immediately\u0000available.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211890","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}
Migraine (MGR) ranks first among diseases in terms of years of lost healthy life in young adult and adult women. Currently, there is no theory of MGR. This paper presents a complete theory of migraine that explains its etiology, symptoms, pathology, and treatment. Migraine involves partially saturated (usually chronically high) sympathetic nervous system (SNS) activity, mainly due to higher sensitivity of the metabolic sensors that recruit it. MGR headache occurs when SNS activity is desensitized or excessive, resulting in hyperexcitability of baroreceptors, oxidative stress, and activation of pain pathways via TRPV1 channels and CGRP. The theory is supported by overwhelming evidence, and explains the properties of current MGR treatments.
{"title":"A Sympathetic Nervous System Theory of Migraine","authors":"Ari Rappoport","doi":"arxiv-2408.06780","DOIUrl":"https://doi.org/arxiv-2408.06780","url":null,"abstract":"Migraine (MGR) ranks first among diseases in terms of years of lost healthy\u0000life in young adult and adult women. Currently, there is no theory of MGR. This\u0000paper presents a complete theory of migraine that explains its etiology,\u0000symptoms, pathology, and treatment. Migraine involves partially saturated\u0000(usually chronically high) sympathetic nervous system (SNS) activity, mainly\u0000due to higher sensitivity of the metabolic sensors that recruit it. MGR\u0000headache occurs when SNS activity is desensitized or excessive, resulting in\u0000hyperexcitability of baroreceptors, oxidative stress, and activation of pain\u0000pathways via TRPV1 channels and CGRP. The theory is supported by overwhelming\u0000evidence, and explains the properties of current MGR treatments.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211891","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}
Manuel Baum, Theresa Roessler, Antonio J. Osuna-Mascaró, Alice Auersperg, Oliver Brock
Research continues to accumulate evidence that Goffin's cockatoos (Cacatua goffiniana) can solve wide sets of mechanical problems, such as tool use, tool manufacture, and solving mechanical puzzles. However, the proximate mechanisms underlying this adaptive behavior are largely unknown. In this study, we analyze how three Goffin's cockatoos learn to solve a specific mechanical puzzle, a lockbox. The observed behavior results from the interaction between a complex environment (the lockbox) and different processes that jointly govern the animals' behavior. We thus jointly analyze the parrots' (1) engagement, (2) sensorimotor skill learning, and (3) action selection. We find that neither of these aspects could solely explain the animals' behavioral adaptation and that a plausible model of proximate mechanisms (including adaptation) should thus also jointly address these aspects. We accompany this analysis with a discussion of methods that may be used to identify such mechanisms. A major point we want to make is, that it is implausible to reliably identify a detailed model from the limited data of one or a few studies. Instead, we advocate for a more coarse approach that first establishes constraints on proximate mechanisms before specific, detailed models are formulated. We exercise this idea on the data we present in this study.
{"title":"Mechanical problem solving in Goffin's cockatoos -- Towards modeling complex behavior","authors":"Manuel Baum, Theresa Roessler, Antonio J. Osuna-Mascaró, Alice Auersperg, Oliver Brock","doi":"arxiv-2408.05967","DOIUrl":"https://doi.org/arxiv-2408.05967","url":null,"abstract":"Research continues to accumulate evidence that Goffin's cockatoos (Cacatua\u0000goffiniana) can solve wide sets of mechanical problems, such as tool use, tool\u0000manufacture, and solving mechanical puzzles. However, the proximate mechanisms\u0000underlying this adaptive behavior are largely unknown. In this study, we\u0000analyze how three Goffin's cockatoos learn to solve a specific mechanical\u0000puzzle, a lockbox. The observed behavior results from the interaction between a\u0000complex environment (the lockbox) and different processes that jointly govern\u0000the animals' behavior. We thus jointly analyze the parrots' (1) engagement, (2)\u0000sensorimotor skill learning, and (3) action selection. We find that neither of\u0000these aspects could solely explain the animals' behavioral adaptation and that\u0000a plausible model of proximate mechanisms (including adaptation) should thus\u0000also jointly address these aspects. We accompany this analysis with a\u0000discussion of methods that may be used to identify such mechanisms. A major\u0000point we want to make is, that it is implausible to reliably identify a\u0000detailed model from the limited data of one or a few studies. Instead, we\u0000advocate for a more coarse approach that first establishes constraints on\u0000proximate mechanisms before specific, detailed models are formulated. We\u0000exercise this idea on the data we present in this study.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211896","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}
Zhaoze Wang, Ronald W. Di Tullio, Spencer Rooke, Vijay Balasubramanian
The vertebrate hippocampus is believed to use recurrent connectivity in area CA3 to support episodic memory recall from partial cues. This brain area also contains place cells, whose location-selective firing fields implement maps supporting spatial memory. Here we show that place cells emerge in networks trained to remember temporally continuous sensory episodes. We model CA3 as a recurrent autoencoder that recalls and reconstructs sensory experiences from noisy and partially occluded observations by agents traversing simulated rooms. The agents move in realistic trajectories modeled from rodents and environments are modeled as high-dimensional sensory experience maps. Training our autoencoder to pattern-complete and reconstruct experiences with a constraint on total activity causes spatially localized firing fields, i.e., place cells, to emerge in the encoding layer. The emergent place fields reproduce key aspects of hippocampal phenomenology: a) remapping (maintenance of and reversion to distinct learned maps in different environments), implemented via repositioning of experience manifolds in the network's hidden layer, b) orthogonality of spatial representations in different arenas, c) robust place field emergence in differently shaped rooms, with single units showing multiple place fields in large or complex spaces, and d) slow representational drift of place fields. We argue that these results arise because continuous traversal of space makes sensory experience temporally continuous. We make testable predictions: a) rapidly changing sensory context will disrupt place fields, b) place fields will form even if recurrent connections are blocked, but reversion to previously learned representations upon remapping will be abolished, c) the dimension of temporally smooth experience sets the dimensionality of place fields, including during virtual navigation of abstract spaces.
{"title":"Time Makes Space: Emergence of Place Fields in Networks Encoding Temporally Continuous Sensory Experiences","authors":"Zhaoze Wang, Ronald W. Di Tullio, Spencer Rooke, Vijay Balasubramanian","doi":"arxiv-2408.05798","DOIUrl":"https://doi.org/arxiv-2408.05798","url":null,"abstract":"The vertebrate hippocampus is believed to use recurrent connectivity in area\u0000CA3 to support episodic memory recall from partial cues. This brain area also\u0000contains place cells, whose location-selective firing fields implement maps\u0000supporting spatial memory. Here we show that place cells emerge in networks\u0000trained to remember temporally continuous sensory episodes. We model CA3 as a\u0000recurrent autoencoder that recalls and reconstructs sensory experiences from\u0000noisy and partially occluded observations by agents traversing simulated rooms.\u0000The agents move in realistic trajectories modeled from rodents and environments\u0000are modeled as high-dimensional sensory experience maps. Training our\u0000autoencoder to pattern-complete and reconstruct experiences with a constraint\u0000on total activity causes spatially localized firing fields, i.e., place cells,\u0000to emerge in the encoding layer. The emergent place fields reproduce key\u0000aspects of hippocampal phenomenology: a) remapping (maintenance of and\u0000reversion to distinct learned maps in different environments), implemented via\u0000repositioning of experience manifolds in the network's hidden layer, b)\u0000orthogonality of spatial representations in different arenas, c) robust place\u0000field emergence in differently shaped rooms, with single units showing multiple\u0000place fields in large or complex spaces, and d) slow representational drift of\u0000place fields. We argue that these results arise because continuous traversal of\u0000space makes sensory experience temporally continuous. We make testable\u0000predictions: a) rapidly changing sensory context will disrupt place fields, b)\u0000place fields will form even if recurrent connections are blocked, but reversion\u0000to previously learned representations upon remapping will be abolished, c) the\u0000dimension of temporally smooth experience sets the dimensionality of place\u0000fields, including during virtual navigation of abstract spaces.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211899","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}
Jonathan Horsley, Yujiang Wang, Callum Simpson, Vyte Janiukstyte, Karoline Leiberg, Beth Little, Jane de Tisi, John Duncan, Peter N. Taylor
Status epilepticus (SE) carries risks of morbidity and mortality. Experimental studies have implicated the entorhinal cortex in prolonged seizures; however, studies in large human cohorts are limited. We hypothesised that individuals with temporal lobe epilepsy (TLE) and a history of SE would have more severe entorhinal atrophy compared to others with TLE and no history of SE. 357 individuals with drug resistant temporal lobe epilepsy (TLE) and 100 healthy controls were scanned on a 3T MRI. For all subjects the cortex was segmented, parcellated, and the thickness calculated from the T1-weighted anatomical scan. Subcortical volumes were derived similarly. Cohen's d and Wilcoxon rank-sum tests respectively were used to capture effect sizes and significance. Individuals with TLE and SE had reduced entorhinal thickness compared to those with TLE and no history of SE. The entorhinal cortex was more atrophic ipsilaterally (d=0.51, p<0.001) than contralaterally (d=0.37, p=0.01). Reductions in ipsilateral entorhinal thickness were present in both left TLE (n=22:176, d=0.78, p<0.001), and right TLE (n=19:140, d=0.31, p=0.04), albeit with a smaller effect size in right TLE. Several other regions exhibited atrophy in individuals with TLE, but these did not relate to a history of SE. These findings suggest potential involvement or susceptibility of the entorhinal cortex in prolonged seizures.
实验研究表明，内黑质皮层与癫痫发作时间延长有关；然而，对大型人类群体的研究却很有限。我们假设颞叶癫痫（TLE）患者和有 SE 病史的人与其他颞叶癫痫患者和没有 SE 病史的人相比，会有更严重的内叶萎缩。357 名耐药性颞叶癫痫（TLE）患者和 100 名健康对照者接受了 3T 磁共振成像扫描。对所有受试者的皮层进行了分割、切片，并通过 T1 加权解剖扫描计算出厚度。皮层下容积的计算方法与此类似。分别使用Cohen's d和Wilcoxon秩和检验来确定效应大小和显著性。与患有TLE且无SE史的患者相比，患有TLE且有SE史的患者的内叶厚度减少。左侧TLE（n=22:176, d=0.78, p<0.001）和右侧TLE（n=19:140, d=0.31, p=0.04）的同侧内侧皮层厚度均有减少，但右侧TLE的影响较小。其他几个区域在TLE患者中也表现出萎缩，但与SE病史无关。这些研究结果表明，脑皮质可能参与或易受癫痫长期发作的影响。
{"title":"Status epilepticus and thinning of the entorhinal cortex","authors":"Jonathan Horsley, Yujiang Wang, Callum Simpson, Vyte Janiukstyte, Karoline Leiberg, Beth Little, Jane de Tisi, John Duncan, Peter N. Taylor","doi":"arxiv-2408.05789","DOIUrl":"https://doi.org/arxiv-2408.05789","url":null,"abstract":"Status epilepticus (SE) carries risks of morbidity and mortality.\u0000Experimental studies have implicated the entorhinal cortex in prolonged\u0000seizures; however, studies in large human cohorts are limited. We hypothesised\u0000that individuals with temporal lobe epilepsy (TLE) and a history of SE would\u0000have more severe entorhinal atrophy compared to others with TLE and no history\u0000of SE. 357 individuals with drug resistant temporal lobe epilepsy (TLE) and 100\u0000healthy controls were scanned on a 3T MRI. For all subjects the cortex was\u0000segmented, parcellated, and the thickness calculated from the T1-weighted\u0000anatomical scan. Subcortical volumes were derived similarly. Cohen's d and\u0000Wilcoxon rank-sum tests respectively were used to capture effect sizes and\u0000significance. Individuals with TLE and SE had reduced entorhinal thickness compared to\u0000those with TLE and no history of SE. The entorhinal cortex was more atrophic\u0000ipsilaterally (d=0.51, p<0.001) than contralaterally (d=0.37, p=0.01).\u0000Reductions in ipsilateral entorhinal thickness were present in both left TLE\u0000(n=22:176, d=0.78, p<0.001), and right TLE (n=19:140, d=0.31, p=0.04), albeit\u0000with a smaller effect size in right TLE. Several other regions exhibited\u0000atrophy in individuals with TLE, but these did not relate to a history of SE. These findings suggest potential involvement or susceptibility of the\u0000entorhinal cortex in prolonged seizures.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211898","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}
Misunderstandings in dyadic interactions often persist despite our best efforts, particularly between native and non-native speakers, resembling a broken duet that refuses to harmonise. This paper delves into the computational mechanisms underpinning these misunderstandings through the lens of the broken Lorenz system -- a continuous dynamical model. By manipulating a specific parameter regime, we induce bistability within the Lorenz equations, thereby confining trajectories to distinct attractors based on initial conditions. This mirrors the persistence of divergent interpretations that often result in misunderstandings. Our simulations reveal that differing prior beliefs between interlocutors result in misaligned generative models, leading to stable yet divergent states of understanding when exposed to the same percept. Specifically, native speakers equipped with precise (i.e., overconfident) priors expect inputs to align closely with their internal models, thus struggling with unexpected variations. Conversely, non-native speakers with imprecise (i.e., less confident) priors exhibit a greater capacity to adjust and accommodate unforeseen inputs. Our results underscore the important role of generative models in facilitating mutual understanding (i.e., establishing a shared narrative) and highlight the necessity of accounting for multistable dynamics in dyadic interactions.
{"title":"A broken duet: multistable dynamics of dyadic interactions","authors":"Johan Medrano, Noor Sajid","doi":"arxiv-2408.03809","DOIUrl":"https://doi.org/arxiv-2408.03809","url":null,"abstract":"Misunderstandings in dyadic interactions often persist despite our best\u0000efforts, particularly between native and non-native speakers, resembling a\u0000broken duet that refuses to harmonise. This paper delves into the computational\u0000mechanisms underpinning these misunderstandings through the lens of the broken\u0000Lorenz system -- a continuous dynamical model. By manipulating a specific\u0000parameter regime, we induce bistability within the Lorenz equations, thereby\u0000confining trajectories to distinct attractors based on initial conditions. This\u0000mirrors the persistence of divergent interpretations that often result in\u0000misunderstandings. Our simulations reveal that differing prior beliefs between\u0000interlocutors result in misaligned generative models, leading to stable yet\u0000divergent states of understanding when exposed to the same percept.\u0000Specifically, native speakers equipped with precise (i.e., overconfident)\u0000priors expect inputs to align closely with their internal models, thus\u0000struggling with unexpected variations. Conversely, non-native speakers with\u0000imprecise (i.e., less confident) priors exhibit a greater capacity to adjust\u0000and accommodate unforeseen inputs. Our results underscore the important role of\u0000generative models in facilitating mutual understanding (i.e., establishing a\u0000shared narrative) and highlight the necessity of accounting for multistable\u0000dynamics in dyadic interactions.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141938805","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}
The brain is immensely complex, with diverse components and dynamic interactions building upon one another to orchestrate a wide range of functions and behaviors. Understanding patterns of these complex interactions and how they are coordinated to support collective neural activity and function is critical for parsing human and animal behavior, treating mental illness, and developing artificial intelligence. Rapid experimental advances in imaging, recording, and perturbing neural systems across various species now provide opportunities and challenges to distill underlying principles of brain organization and function. Here, we take stock of recent progresses and review methods used in the statistical analysis of brain networks, drawing from fields of statistical physics, network theory and information theory. Our discussion is organized by scale, starting with models of individual neurons and extending to large-scale networks mapped across brain regions. We then examine the organizing principles and constraints that shape the biological structure and function of neural circuits. Finally, we describe current opportunities aimed at improving models in light of recent developments and at bridging across scales to contribute to a better understanding of brain networks.
{"title":"Towards principles of brain network organization and function","authors":"Suman Kulkarni, Dani S. Bassett","doi":"arxiv-2408.02640","DOIUrl":"https://doi.org/arxiv-2408.02640","url":null,"abstract":"The brain is immensely complex, with diverse components and dynamic\u0000interactions building upon one another to orchestrate a wide range of functions\u0000and behaviors. Understanding patterns of these complex interactions and how\u0000they are coordinated to support collective neural activity and function is\u0000critical for parsing human and animal behavior, treating mental illness, and\u0000developing artificial intelligence. Rapid experimental advances in imaging,\u0000recording, and perturbing neural systems across various species now provide\u0000opportunities and challenges to distill underlying principles of brain\u0000organization and function. Here, we take stock of recent progresses and review\u0000methods used in the statistical analysis of brain networks, drawing from fields\u0000of statistical physics, network theory and information theory. Our discussion\u0000is organized by scale, starting with models of individual neurons and extending\u0000to large-scale networks mapped across brain regions. We then examine the\u0000organizing principles and constraints that shape the biological structure and\u0000function of neural circuits. Finally, we describe current opportunities aimed\u0000at improving models in light of recent developments and at bridging across\u0000scales to contribute to a better understanding of brain networks.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141938806","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}
This essay, derived from a lecture at "The Physics Modeling of Thought" workshop in Berlin in winter 2023, explores the mutually beneficial relationship between theoretical neuroscience and statistical physics through the lens of efficient coding and computation in cortical circuits. It highlights how the study of neural networks has enhanced our understanding of complex, nonequilibrium, and disordered systems, while also demonstrating how neuroscientific challenges have spurred novel developments in physics. The paper traces the evolution of ideas from seminal work on chaos in random neural networks to recent developments in efficient coding and the partial suppression of chaotic fluctuations. It emphasizes how concepts from statistical physics, such as phase transitions and critical phenomena, have been instrumental in elucidating the computational capabilities of neural networks. By examining the interplay between order and disorder in neural computation, the essay illustrates the deep connection between theoretical neuroscience and the statistical physics of nonequilibrium systems. This synthesis underscores the ongoing importance of interdisciplinary approaches in advancing both fields, offering fresh perspectives on the fundamental principles governing information processing in biological and artificial systems. This multidisciplinary approach not only advances our understanding of neural computation and complex systems but also points toward future challenges at the intersection of neuroscience and physics.
{"title":"Efficient coding with chaotic neural networks: A journey from neuroscience to physics and back","authors":"Jonathan Kadmon","doi":"arxiv-2408.01949","DOIUrl":"https://doi.org/arxiv-2408.01949","url":null,"abstract":"This essay, derived from a lecture at \"The Physics Modeling of Thought\"\u0000workshop in Berlin in winter 2023, explores the mutually beneficial\u0000relationship between theoretical neuroscience and statistical physics through\u0000the lens of efficient coding and computation in cortical circuits. It\u0000highlights how the study of neural networks has enhanced our understanding of\u0000complex, nonequilibrium, and disordered systems, while also demonstrating how\u0000neuroscientific challenges have spurred novel developments in physics. The\u0000paper traces the evolution of ideas from seminal work on chaos in random neural\u0000networks to recent developments in efficient coding and the partial suppression\u0000of chaotic fluctuations. It emphasizes how concepts from statistical physics,\u0000such as phase transitions and critical phenomena, have been instrumental in\u0000elucidating the computational capabilities of neural networks. By examining the interplay between order and disorder in neural computation,\u0000the essay illustrates the deep connection between theoretical neuroscience and\u0000the statistical physics of nonequilibrium systems. This synthesis underscores\u0000the ongoing importance of interdisciplinary approaches in advancing both\u0000fields, offering fresh perspectives on the fundamental principles governing\u0000information processing in biological and artificial systems. This\u0000multidisciplinary approach not only advances our understanding of neural\u0000computation and complex systems but also points toward future challenges at the\u0000intersection of neuroscience and physics.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141938807","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}
Robustness is a measure of functional reliability of a system against perturbations. To achieve a good and robust performance, a system must filter out external perturbations by its internal priors. These priors are usually distilled in the structure and the states of the system. Biophysical neural network are known to be robust but the exact mechanisms are still elusive. In this paper, we probe how orientation-selective neurons organized on a 1-D ring network respond to perturbations in the hope of gaining some insights on the robustness of visual system in brain. We analyze the steady-state of the rate-based network and prove that the activation state of neurons, rather than their firing rates, determines how the model respond to perturbations. We then identify specific perturbation patterns that induce the largest responses for different configurations of activation states, and find them to be sinusoidal or sinusoidal-like while other patterns are largely attenuated. Similar results are observed in a spiking ring model. Finally, we remap the perturbations in orientation back into the 2-D image space using Gabor functions. The resulted optimal perturbation patterns mirror adversarial attacks in deep learning that exploit the priors of the system. Our results suggest that based on different state configurations, these priors could underlie some of the illusionary experiences as the cost of visual robustness.
{"title":"State-dependent Filtering of the Ring Model","authors":"Jing Yan, Yunxuan Feng, Wei Dai, Yaoyu Zhang","doi":"arxiv-2408.01817","DOIUrl":"https://doi.org/arxiv-2408.01817","url":null,"abstract":"Robustness is a measure of functional reliability of a system against\u0000perturbations. To achieve a good and robust performance, a system must filter\u0000out external perturbations by its internal priors. These priors are usually\u0000distilled in the structure and the states of the system. Biophysical neural\u0000network are known to be robust but the exact mechanisms are still elusive. In\u0000this paper, we probe how orientation-selective neurons organized on a 1-D ring\u0000network respond to perturbations in the hope of gaining some insights on the\u0000robustness of visual system in brain. We analyze the steady-state of the\u0000rate-based network and prove that the activation state of neurons, rather than\u0000their firing rates, determines how the model respond to perturbations. We then\u0000identify specific perturbation patterns that induce the largest responses for\u0000different configurations of activation states, and find them to be sinusoidal\u0000or sinusoidal-like while other patterns are largely attenuated. Similar results\u0000are observed in a spiking ring model. Finally, we remap the perturbations in\u0000orientation back into the 2-D image space using Gabor functions. The resulted\u0000optimal perturbation patterns mirror adversarial attacks in deep learning that\u0000exploit the priors of the system. Our results suggest that based on different\u0000state configurations, these priors could underlie some of the illusionary\u0000experiences as the cost of visual robustness.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141938734","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}
Sarah Josephine Stednitz, Andrew Lesak, Adeline L Fecker, Peregrine Painter, Phil Washbourne, Luca Mazzucato, Ethan K Scott
Social behavior across animal species ranges from simple pairwise interactions to thousands of individuals coordinating goal-directed movements. Regardless of the scale, these interactions are governed by the interplay between multimodal sensory information and the internal state of each animal. Here, we investigate how animals use multiple sensory modalities to guide social behavior in the highly social zebrafish (Danio rerio) and uncover the complex features of pairwise interactions early in development. To identify distinct behaviors and understand how they vary over time, we developed a new hidden Markov model with constrained linear-model emissions to automatically classify states of coordinated interaction, using the movements of one animal to predict those of another. We discovered that social behaviors alternate between two interaction states within a single experimental session, distinguished by unique movements and timescales. Long-range interactions, akin to shoaling, rely on vision, while mechanosensation underlies rapid synchronized movements and parallel swimming, precursors of schooling. Altogether, we observe spontaneous interactions in pairs of fish, develop novel hidden Markov modeling to reveal two fundamental interaction modes, and identify the sensory systems involved in each. Our modeling approach to pairwise social interactions has broad applicability to a wide variety of naturalistic behaviors and species and solves the challenge of detecting transient couplings between quasi-periodic time series.
{"title":"Probabilistic modeling reveals coordinated social interaction states and their multisensory bases","authors":"Sarah Josephine Stednitz, Andrew Lesak, Adeline L Fecker, Peregrine Painter, Phil Washbourne, Luca Mazzucato, Ethan K Scott","doi":"arxiv-2408.01683","DOIUrl":"https://doi.org/arxiv-2408.01683","url":null,"abstract":"Social behavior across animal species ranges from simple pairwise\u0000interactions to thousands of individuals coordinating goal-directed movements.\u0000Regardless of the scale, these interactions are governed by the interplay\u0000between multimodal sensory information and the internal state of each animal.\u0000Here, we investigate how animals use multiple sensory modalities to guide\u0000social behavior in the highly social zebrafish (Danio rerio) and uncover the\u0000complex features of pairwise interactions early in development. To identify\u0000distinct behaviors and understand how they vary over time, we developed a new\u0000hidden Markov model with constrained linear-model emissions to automatically\u0000classify states of coordinated interaction, using the movements of one animal\u0000to predict those of another. We discovered that social behaviors alternate\u0000between two interaction states within a single experimental session,\u0000distinguished by unique movements and timescales. Long-range interactions, akin\u0000to shoaling, rely on vision, while mechanosensation underlies rapid\u0000synchronized movements and parallel swimming, precursors of schooling.\u0000Altogether, we observe spontaneous interactions in pairs of fish, develop novel\u0000hidden Markov modeling to reveal two fundamental interaction modes, and\u0000identify the sensory systems involved in each. Our modeling approach to\u0000pairwise social interactions has broad applicability to a wide variety of\u0000naturalistic behaviors and species and solves the challenge of detecting\u0000transient couplings between quasi-periodic time series.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141938809","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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