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Article ; Online: Structure of activity in multiregion recurrent neural networks.

Clark, David G / Beiran, Manuel

ArXiv

2024

Abstract: Neural circuits are composed of multiple regions, each with rich dynamics and engaging in communication with other regions. The combination of local, within-region dynamics and global, network-level dynamics is thought to provide computational ... ...

Abstract	Neural circuits are composed of multiple regions, each with rich dynamics and engaging in communication with other regions. The combination of local, within-region dynamics and global, network-level dynamics is thought to provide computational flexibility. However, the nature of such multiregion dynamics and the underlying synaptic connectivity patterns remain poorly understood. Here, we study the dynamics of recurrent neural networks with multiple interconnected regions. Within each region, neurons have a combination of random and structured recurrent connections. Motivated by experimental evidence of communication subspaces between cortical areas, these networks have low-rank connectivity between regions, enabling selective routing of activity. These networks exhibit two interacting forms of dynamics: high-dimensional fluctuations within regions and low-dimensional signal transmission between regions. To characterize this interaction, we develop a dynamical mean-field theory to analyze such networks in the limit where each region contains infinitely many neurons, with cross-region currents as key order parameters. Regions can act as both generators and transmitters of activity, roles that we show are in conflict. Specifically, taming the complexity of activity within a region is necessary for it to route signals to and from other regions. Unlike previous models of routing in neural circuits, which suppressed the activities of neuronal groups to control signal flow, routing in our model is achieved by exciting different high-dimensional activity patterns through a combination of connectivity structure and nonlinear recurrent dynamics. This theory provides insight into the interpretation of both multiregion neural data and trained neural networks.
Language	English
Publishing date	2024-02-20
Publishing country	United States
Document type	Preprint
ISSN	2331-8422
ISSN (online)	2331-8422
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Dimension of Activity in Random Neural Networks.

Clark, David G / Abbott, L F / Litwin-Kumar, Ashok

Physical review letters

2023 Volume 131, Issue 11, Page(s) 118401

Abstract: Neural networks are high-dimensional nonlinear dynamical systems that process information through the coordinated activity of many connected units. Understanding how biological and machine-learning networks function and learn requires knowledge of the ... ...

Abstract	Neural networks are high-dimensional nonlinear dynamical systems that process information through the coordinated activity of many connected units. Understanding how biological and machine-learning networks function and learn requires knowledge of the structure of this coordinated activity, information contained, for example, in cross covariances between units. Self-consistent dynamical mean field theory (DMFT) has elucidated several features of random neural networks-in particular, that they can generate chaotic activity-however, a calculation of cross covariances using this approach has not been provided. Here, we calculate cross covariances self-consistently via a two-site cavity DMFT. We use this theory to probe spatiotemporal features of activity coordination in a classic random-network model with independent and identically distributed (i.i.d.) couplings, showing an extensive but fractionally low effective dimension of activity and a long population-level timescale. Our formulas apply to a wide range of single-unit dynamics and generalize to non-i.i.d. couplings. As an example of the latter, we analyze the case of partially symmetric couplings.
Language	English
Publishing date	2023-09-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	208853-8
ISSN	1079-7114 ; 0031-9007
ISSN (online)	1079-7114
ISSN	0031-9007
DOI	10.1103/PhysRevLett.131.118401
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Book ; Online: Theory of coupled neuronal-synaptic dynamics

Clark, David G. / Abbott, L. F.

2023

Abstract: In neural circuits, synaptic strengths influence neuronal activity by shaping network dynamics, and neuronal activity influences synaptic strengths through activity-dependent plasticity. Motivated by this fact, we study a recurrent-network model in which ...

Abstract	In neural circuits, synaptic strengths influence neuronal activity by shaping network dynamics, and neuronal activity influences synaptic strengths through activity-dependent plasticity. Motivated by this fact, we study a recurrent-network model in which neuronal units and synaptic couplings are interacting dynamic variables, with couplings subject to Hebbian modification with decay around quenched random strengths. Rather than assigning a specific role to the plasticity, we use dynamical mean-field theory and other techniques to systematically characterize the neuronal-synaptic dynamics, revealing a rich phase diagram. Adding Hebbian plasticity slows activity in chaotic networks and can induce chaos in otherwise quiescent networks. Anti-Hebbian plasticity quickens activity and produces an oscillatory component. Analysis of the Jacobian shows that Hebbian and anti-Hebbian plasticity push locally unstable modes toward the real and imaginary axes, explaining these behaviors. Both random-matrix and Lyapunov analysis show that strong Hebbian plasticity segregates network timescales into two bands with a slow, synapse-dominated band driving the dynamics, suggesting a flipped view of the network as synapses connected by neurons. For increasing strength, Hebbian plasticity initially raises the complexity of the dynamics, measured by the maximum Lyapunov exponent and attractor dimension, but then decreases these metrics, likely due to the proliferation of stable fixed points. We compute the marginally stable spectra of such fixed points as well as their number, showing exponential growth with network size. In chaotic states with strong Hebbian plasticity, a stable fixed point of neuronal dynamics is destabilized by synaptic dynamics, allowing any neuronal state to be stored as a stable fixed point by halting the plasticity. This phase of freezable chaos offers a new mechanism for working memory. Comment: 20 pages, 9 figures
Keywords	Quantitative Biology - Neurons and Cognition ; Condensed Matter - Disordered Systems and Neural Networks ; Computer Science - Neural and Evolutionary Computing
Subject code	612
Publishing date	2023-02-17
Publishing country	us
Document type	Book ; Online
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Book ; Online: Dimension of activity in random neural networks

Clark, David G. / Abbott, L. F. / Litwin-Kumar, Ashok

2022

Abstract	Neural networks are high-dimensional nonlinear dynamical systems that process information through the coordinated activity of many connected units. Understanding how biological and machine-learning networks function and learn requires knowledge of the structure of this coordinated activity, information contained, for example, in cross covariances between units. Self-consistent dynamical mean field theory (DMFT) has elucidated several features of random neural networks -- in particular, that they can generate chaotic activity -- however, a calculation of cross covariances using this approach has not been provided. Here, we calculate cross covariances self-consistently via a two-site cavity DMFT. We use this theory to probe spatiotemporal features of activity coordination in a classic random-network model with independent and identically distributed (i.i.d.) couplings, showing an extensive but fractionally low effective dimension of activity and a long population-level timescale. Our formulae apply to a wide range of single-unit dynamics and generalize to non-i.i.d. couplings. As an example of the latter, we analyze the case of partially symmetric couplings. Comment: 8 pages, 6 figures; clarified derivation
Keywords	Quantitative Biology - Neurons and Cognition ; Condensed Matter - Disordered Systems and Neural Networks ; Computer Science - Neural and Evolutionary Computing
Subject code	612
Publishing date	2022-07-25
Publishing country	us
Document type	Book ; Online
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Article ; Online: Fluoroacetate distribution, response to fluoridation, and synthesis in juvenile Gastrolobium bilobum plants.

Leong, Bryan J / Folz, Jacob S / Bathe, Ulschan / Clark, David G / Fiehn, Oliver / Hanson, Andrew D

Phytochemistry

2022 Volume 202, Page(s) 113356

Abstract: Like angiosperms from several other families, the leguminous shrub Gastrolobium bilobum R.Br. produces and accumulates fluoroacetate, indicating that it performs the difficult chemistry needed to make a C-F bond. Bioinformatic analyses indicate that ... ...

Abstract	Like angiosperms from several other families, the leguminous shrub Gastrolobium bilobum R.Br. produces and accumulates fluoroacetate, indicating that it performs the difficult chemistry needed to make a C-F bond. Bioinformatic analyses indicate that plants lack homologs of the only enzymes known to make a C-F bond, i.e., the Actinomycete flurorinases that form 5'-fluoro-5'-deoxyadenosine from S-adenosylmethionine and fluoride ion. To probe the origin of fluoroacetate in G. bilobum we first showed that fluoroacetate accumulates to millimolar levels in young leaves but not older leaves, stems or roots, that leaf fluoroacetate levels vary >20-fold between individual plants and are not markedly raised by sodium fluoride treatment. Young leaves were fed adenosine-
MeSH term(s)	Fluoridation ; Fluoroacetates/chemistry ; Fluoroacetates/metabolism ; Plants/metabolism ; Ribose ; S-Adenosylmethionine ; Serine
Chemical Substances	Fluoroacetates ; Serine (452VLY9402) ; Ribose (681HV46001) ; S-Adenosylmethionine (7LP2MPO46S)
Language	English
Publishing date	2022-08-05
Publishing country	England
Document type	Journal Article
ZDB-ID	208884-8
ISSN	1873-3700 ; 0031-9422
ISSN (online)	1873-3700
ISSN	0031-9422
DOI	10.1016/j.phytochem.2022.113356
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Article: Construction of a genome-wide genetic linkage map and identification of quantitative trait loci for powdery mildew resistance in

Bhattarai, Krishna / Sharma, Sadikshya / Verma, Sujeet / Peres, Natalia A / Xiao, Shunyuan / Clark, David G / Deng, Zhanao

Frontiers in plant science

2023 Volume 13, Page(s) 1072717

Abstract: Powdery mildew (PM) is a common fungal disease in many important crops. The PM caused ... ...

Abstract	Powdery mildew (PM) is a common fungal disease in many important crops. The PM caused by
Language	English
Publishing date	2023-01-06
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2613694-6
ISSN	1664-462X
ISSN	1664-462X
DOI	10.3389/fpls.2022.1072717
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Article ; Online: Computerized Cognitive Training and 24-Month Mortality in Heart Failure.

Jung, Miyeon / Smith, Asa B / Giordani, Bruno / Clark, David G / Gradus-Pizlo, Irmina / Wierenga, Kelly L / Lake, Kittie Reid / Pressler, Susan J

The Journal of cardiovascular nursing

2023 Volume 39, Issue 2, Page(s) E51–E58

Abstract: Background: Cognitive dysfunction predicts mortality in heart failure (HF). Computerized cognitive training (CCT) has shown preliminary efficacy in improving cognitive function. However, the relationship between CCT and mortality is unclear. Aims were ... ...

Abstract	Background: Cognitive dysfunction predicts mortality in heart failure (HF). Computerized cognitive training (CCT) has shown preliminary efficacy in improving cognitive function. However, the relationship between CCT and mortality is unclear. Aims were to evaluate (1) long-term efficacy of CCT in reducing 24-month mortality and (2) age, HF severity, global cognition, memory, working memory, depressive symptoms, and health-related quality of life as predictors of 24-month mortality among patients with HF. Methods: In this prospective longitudinal study, 142 patients enrolled in a 3-arm randomized controlled trial were followed for 24 months. Logistic regression was used to achieve the aims. Results: Across 24 months, 16 patients died (CCT, 8.3%; control groups, 12.8%). Computerized cognitive training did not predict 24-month mortality (odds ratio [OR], 0.65). Older age (OR, 1.08), worse global cognition (OR, 0.73), memory (OR, 0.81), and depressive symptoms (OR, 1.10) at baseline predicted 24-month mortality. Conclusions: Efficacious interventions are needed to improve global cognition, memory, and depressive symptoms and reduce mortality in HF.
MeSH term(s)	Humans ; Quality of Life ; Prospective Studies ; Cognitive Training ; Longitudinal Studies ; Cognitive Dysfunction ; Cognition ; Heart Failure/psychology
Language	English
Publishing date	2023-07-25
Publishing country	United States
Document type	Randomized Controlled Trial ; Journal Article
ZDB-ID	639335-4
ISSN	1550-5049 ; 0889-4655
ISSN (online)	1550-5049
ISSN	0889-4655
DOI	10.1097/JCN.0000000000001023
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Article: Fluoroacetate distribution, response to fluoridation, and synthesis in juvenile Gastrolobium bilobum plants

Leong, Bryan J. / Folz, Jacob S. / Bathe, Ulschan / Clark, David G. / Fiehn, Oliver / Hanson, Andrew D.

Phytochemistry. 2022 Oct., v. 202

2022

Abstract: Like angiosperms from several other families, the leguminous shrub Gastrolobium bilobum R.Br. produces and accumulates fluoroacetate, indicating that it performs the difficult chemistry needed to make a C–F bond. Bioinformatic analyses indicate that ... ...

Abstract	Like angiosperms from several other families, the leguminous shrub Gastrolobium bilobum R.Br. produces and accumulates fluoroacetate, indicating that it performs the difficult chemistry needed to make a C–F bond. Bioinformatic analyses indicate that plants lack homologs of the only enzymes known to make a C–F bond, i.e., the Actinomycete flurorinases that form 5′-fluoro-5′-deoxyadenosine from S-adenosylmethionine and fluoride ion. To probe the origin of fluoroacetate in G. bilobum we first showed that fluoroacetate accumulates to millimolar levels in young leaves but not older leaves, stems or roots, that leaf fluoroacetate levels vary >20-fold between individual plants and are not markedly raised by sodium fluoride treatment. Young leaves were fed adenosine-¹³C-ribose, ¹³C-serine, or ¹³C-acetate to test plausible biosynthetic routes to fluoroacetate from S-adenosylmethionine, a C₃-pyridoxal phosphate complex, or acetyl-CoA, respectively. Incorporation of ¹³C into expected metabolites confirmed that all three precursors were taken up and metabolized. Consistent with the bioinformatic evidence against an Actinomycete-type pathway, no adenosine-¹³C-ribose was converted to ¹³C-fluoroacetate; nor was the characteristic 4-fluorothreonine product of the Actinomycete pathway detected. Similarly, no ¹³C from acetate or serine was incorporated into fluoroacetate. While not fully excluding the hypothetical pathways that were tested, these negative labeling data imply that G. bilobum creates the C–F bond by an unprecedented biochemical reaction. Enzyme(s) that mediate such a reaction could be of great value in pharmaceutical and agrochemical manufacturing.
Keywords	Gastrolobium ; S-adenosylmethionine ; acetates ; acetyl coenzyme A ; agrochemicals ; bioinformatics ; biosynthesis ; enzymes ; fluoridation ; juveniles ; metabolites ; phosphates ; plant biochemistry ; serine ; shrubs ; sodium fluoride
Language	English
Dates of publication	2022-10
Publishing place	Elsevier Ltd
Document type	Article
ZDB-ID	208884-8
ISSN	1873-3700 ; 0031-9422
ISSN (online)	1873-3700
ISSN	0031-9422
DOI	10.1016/j.phytochem.2022.113356
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: Credit Assignment Through Broadcasting a Global Error Vector

Clark, David G. / Abbott, L. F. / Chung, SueYeon

2021

Abstract: Backpropagation (BP) uses detailed, unit-specific feedback to train deep neural networks (DNNs) with remarkable success. That biological neural circuits appear to perform credit assignment, but cannot implement BP, implies the existence of other powerful ...

Abstract	Backpropagation (BP) uses detailed, unit-specific feedback to train deep neural networks (DNNs) with remarkable success. That biological neural circuits appear to perform credit assignment, but cannot implement BP, implies the existence of other powerful learning algorithms. Here, we explore the extent to which a globally broadcast learning signal, coupled with local weight updates, enables training of DNNs. We present both a learning rule, called global error-vector broadcasting (GEVB), and a class of DNNs, called vectorized nonnegative networks (VNNs), in which this learning rule operates. VNNs have vector-valued units and nonnegative weights past the first layer. The GEVB learning rule generalizes three-factor Hebbian learning, updating each weight by an amount proportional to the inner product of the presynaptic activation and a globally broadcast error vector when the postsynaptic unit is active. We prove that these weight updates are matched in sign to the gradient, enabling accurate credit assignment. Moreover, at initialization, these updates are exactly proportional to the gradient in the limit of infinite network width. GEVB matches the performance of BP in VNNs, and in some cases outperforms direct feedback alignment (DFA) applied in conventional networks. Unlike DFA, GEVB successfully trains convolutional layers. Altogether, our theoretical and empirical results point to a surprisingly powerful role for a global learning signal in training DNNs. Comment: 20 pages, 6 figures; expanded references and discussion
Keywords	Quantitative Biology - Neurons and Cognition ; Computer Science - Neural and Evolutionary Computing
Subject code	006
Publishing date	2021-06-08
Publishing country	us
Document type	Book ; Online
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Article ; Online: Responses to low phosphorus in high and low foliar anthocyanin coleus (Solenostemon scutellarioides) and maize (Zea mays).

Henry, Amelia / Chopra, Surinder / Clark, David G / Lynch, Jonathan P

Functional plant biology : FPB

2020 Volume 39, Issue 3, Page(s) 255–265

Abstract: Foliar anthocyanin production is frequently induced by phosphorus deficiency, but the adaptive significance of increased anthocyanin production under P stress, if any, remains unknown. In this study we hypothesised that if anthocyanin expression is an ... ...

Abstract	Foliar anthocyanin production is frequently induced by phosphorus deficiency, but the adaptive significance of increased anthocyanin production under P stress, if any, remains unknown. In this study we hypothesised that if anthocyanin expression is an adaptive response to mitigate the stress effects of P deficiency, genotypes with constitutive anthocyanin expression would have greater tolerance to P stress than low anthocyanin-producing genotypes. Four studies were conducted in greenhouse, outdoor chamber and field conditions to compare genetically similar maize and coleus plants with contrasting anthocyanin accumulation (i.e. 'red-leafed' vs 'green-leafed'). In low-P treatments, anthocyanin production did not consistently result in greater photosynthesis or biomass. In coleus, red-leafed phenotypes showed lower chlorophyll a/b ratios suggesting photoprotection by anthocyanins against degradation of light harvesting complex proteins. However, the opposite trend was observed in maize, where red-leafed phenotypes showed greater chlorophyll a/b ratios and lower qP (oxidation state of PSII). Based on results from the various treatments and growth conditions of this study, it could not be concluded that high foliar anthocyanin production confers a general functional advantage under low-P stress. More research comparing inducible vs constitutive production may help elucidate the role of anthocyanin biosynthesis in P deficiency responses.
Language	English
Publishing date	2020-06-02
Publishing country	Australia
Document type	Journal Article
ZDB-ID	2071582-1
ISSN	1445-4416 ; 1445-4408
ISSN (online)	1445-4416
ISSN	1445-4408
DOI	10.1071/FP11256
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