Kristine Anne Heiney

• Heiney, Kristine; Józsa, Mónika; Rule, Michael E.; Sprekeler, Henning; Nichele, Stefano & O’Leary, Timothy (2026). Information theoretic measures of neural and behavioural coupling predict representational drift. PLoS Computational Biology. ISSN 1553-734X. 22(2), p. 1013130–1013130. doi: 10.1371/journal.pcbi.1013130. Full text in Research Archive Show summary

  In many parts of the brain, population tuning to stimuli and behaviour gradually changes over the course of days to weeks in a phenomenon known as representational drift. The tuning stability of individual cells varies over the population, and it remains unclear what drives this heterogeneity. We investigate how a neuron’s tuning stability relates to its shared variability with other neurons in the population using two published datasets from posterior parietal cortex and visual cortex. We quantified the contribution of pairwise interactions to behaviour or stimulus encoding by partial information decomposition, which breaks down the mutual information between the pairwise neural activity and the external variable into components uniquely provided by each neuron and by their interactions. Information shared by the two neurons is termed ‘redundant’, and information requiring knowledge of the state of both neurons is termed ‘synergistic’. We found that a neuron’s tuning stability is positively correlated with the strength of its average pairwise redundancy with the population. We hypothesize that subpopulations of neurons show greater stability because they are tuned to salient features common across multiple tasks. Regardless of the mechanistic implications of our work, the stability–redundancy relationship may support improved longitudinal neural decoding in technology that has to track population dynamics over time, such as brain–machine interfaces.

• Valderhaug, Vibeke Devold; Ramstad, Ola Huse; Wijdeven, Rosanne Francisca van de; Heiney, Kristine; Nichele, Stefano & Sandvig, Axel [Show all 7 contributors for this article] (2024). Micro-and mesoscale aspects of neurodegeneration in engineered human neural networks carrying the LRRK2 G2019S mutation. Frontiers in Cellular Neuroscience. 18, p. 1–15. doi: 10.3389/fncel.2024.1366098. Full text in Research Archive Show summary

  Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene have been widely linked to Parkinson’s disease, where the G2019S variant has been shown to contribute uniquely to both familial and sporadic forms of the disease. LRRK2-related mutations have been extensively studied, yet the wide variety of cellular and network events related to these mutations remain poorly understood. The advancement and availability of tools for neural engineering now enable modeling of selected pathological aspects of neurodegenerative disease in human neural networks in vitro. Our study revealed distinct pathology associated dynamics in engineered human cortical neural networks carrying the LRRK2 G2019S mutation compared to healthy isogenic control neural networks. The neurons carrying the LRRK2 G2019S mutation self-organized into networks with aberrant morphology and mitochondrial dynamics, affecting emerging structure–function relationships both at the micro-and mesoscale. Taken together, the findings of our study points towar

• Farner, Jørgen Jensen; Ramstad, Ola Huse; Nichele, Stefano & Heiney, Kristine (2024). Local Delay Plasticity Supports Generalized Learning in Spiking Neural Networks. In Villani, Marco; Cagnoni, Stefano & Serra, Roberto (Ed.), Artificial Life and Evolutionary Computation. WIVACE 2023.. Springer. ISSN 9783031574306. p. 241–255. doi: 10.1007/978-3-031-57430-6_19. Full text in Research Archive Show summary

  We propose a novel local learning rule for spiking neural networks in which spike propagation times undergo activity-dependent plasticity. Our plasticity rule aligns pre-synaptic spike times to produce a stronger and more rapid response. Inputs are encoded by latency coding and outputs decoded by matching similar patterns of output spiking activity. We demonstrate the use of this method in a three-layer feed-foward network with inputs from a database of handwritten digits. Networks consistently showed improved classification accuracy after training, and training with this method also allowed networks to generalize to an input class unseen during training. Our proposed method takes advantage of the ability of spiking neurons to support many different time-locked sequences of spikes, each of which can be activated by different input activations. The proof-of-concept shown here demonstrates the great potential for local delay learning to expand the memory capacity and generalizability of spiking neural networks and offers new perspectives on how to configure neuromorphic hardware.

• Pierro, Alessandro; Heiney, Kristine; Shrivastava, Shamit; Marcucci, Gulia & Nichele, Stefano (2023). Optimization of a Hydrodynamic Computational Reservoir through Evolution. In Silva, Sara & Paquete, Luis (Ed.), GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference. Association for Computing Machinery (ACM). ISSN 9798400701191. p. 202–210. doi: 10.1145/3583131.3590355. Full text in Research Archive Show summary

  As demand for computational resources reaches unprecedented levels, research is expanding into the use of complex material substrates for computing. In this study, we interface with a model of a hydrodynamic system, under development by a startup, as a computational reservoir and optimize its properties using an evolution in materio approach. Input data are encoded as waves applied to our shallow water reservoir, and the readout wave height is obtained at a fixed detection point. We optimized the readout times and how inputs are mapped to the wave amplitude or frequency using an evolutionary search algorithm, with the objective of maximizing the system’s ability to linearly separate observations in the training data by maximizing the readout matrix determinant. Applying evolutionary methods to this reservoir system substantially improved separability on an XNOR task, in comparison toimplementations with hand-selected parameters. We also applied our approach to a regression task and show that our approach improves out-of-sample accuracy. Results from this study will inform how we interface with the physical reservoir in future work, and we will use these methods to continue to optimize other aspects of the physical implementation of this system as a computational reservoir.

• Heiney, Kristine Anne; Ramstad, Ola Huse; Fiskum, Vegard; Sandvig, Axel; Sandvig, Ioanna & Nichele, Stefano (2022). Neuronal avalanche dynamics and functional connectivity elucidate information propagation in vitro. Frontiers in Neural Circuits. 16. doi: 10.3389/fncir.2022.980631. Full text in Research Archive Show summary

  Cascading activity is commonly observed in complex dynamical systems, including networks of biological neurons, and how these cascades spread through the system is reliant on how the elements of the system are connected and organized. In this work, we studied networks of neurons as they matured over 50 days in vitro and evaluated both their dynamics and their functional connectivity structures by observing their electrophysiological activity using microelectrode array recordings. Correlations were obtained between features of their activity propagation and functional connectivity characteristics to elucidate the interplay between dynamics and structure. The results indicate that in vitro networks maintain a slightly subcritical state by striking a balance between integration and segregation. Our work demonstrates the complementarity of these two approaches—functional connectivity and avalanche dynamics—in studying information propagation in neurons in vitro, which can in turn inform the design and optimization of engineered computational substrates.

• Farner, Jørgen Jensen; Weydahl, Håkon; Jahren, Christian Ruben; Ramstad, Ola Huse; Nichele, Stefano & Heiney, Kristine Anne (2021). Evolving spiking neuron cellular automata and networks to emulate in vitro neuronal activity. In Haddow, Pauline (Eds.), IEEE Symposium Series on Computational Intelligence 2021. IEEE Press. ISSN 9781728190488. doi: 10.1109/SSCI50451.2021.9660185. Full text in Research Archive Show summary

  Neuro-inspired models and systems have great potential for applications in unconventional computing. Often, the mechanisms of biological neurons are modeled or mimicked in simulated or physical systems in an attempt to harness some of the computational power of the brain. However, the biological mechanisms at play in neural systems are complicated and challenging to capture and engineer; thus, it can be simpler to turn to a data-driven approach to transfer features of neural behavior to artificial substrates. In the present study, we used an evolutionary algorithm to produce spiking neural systems that emulate the patterns of behavior of biological neurons in vitro. The aim of this approach was to develop a method of producing models capable of exhibiting complex behavior that may be suitable for use as computational substrates. Our models were able to produce a level of network-wide synchrony and showed a range of behaviors depending on the target data used for their evolution, which was from a range of neuronal culture densities and maturities. The genomes of the top-performing models indicate the excitability and density of connections in the model play an important role in determining the complexity of the produced activity.

• Valderhaug, Vibeke Devold; Heiney, Kristine; Ramstad, Ola Huse; Bråthen, Geir; Kuan, Wei-Li & Nichele, Stefano [Show all 8 contributors for this article] (2021). Early functional changes associated with alpha-synuclein proteinopathy in engineered human neural networks. American Journal of Physiology - Cell Physiology. 320(6), p. 1–12. doi: 10.1152/AJPCELL.00413.2020. Full text in Research Archive Show summary

  A patterned spread of proteinopathy represents a common characteristic of many neurodegenerative diseases. In Parkinson’s disease (PD), misfolded forms of α-synuclein proteins accumulate in hallmark pathological inclusions termed Lewy bodies and Lewy neurites. Such protein aggregates seem to affect selectively vulnerable neuronal populations in the substantia nigra and to propagate within interconnected neuronal networks. Research findings suggest that these proteinopathic inclusions are present at very early time points in disease development, even before clear behavioral symptoms of dysfunction arise. In this study, we investigate the early pathophysiology developing after induced formation of such PD-related α-synuclein inclusions in a physiologically relevant in vitro setup using engineered human neural networks. We monitor the neural network activity using multielectrode arrays (MEAs) for a period of 3 wk following proteinopathy induction to identify associated changes in network function, with a special emphasis on the measure of network criticality. Self-organized criticality represents the critical point between resilience against perturbation and adaptational flexibility, which appears to be a functional trait in self-organizing neural networks, both in vitro and in vivo. We show that although developing pathology at early onset is not clearly manifest in standard measurements of network function, it may be discerned by investigating differences in network criticality states.

• Heiney, Kristine Anne; Valderhaug, Vibeke Devold; Ramstad, Ola Huse; Sandvig, Ioanna; Sandvig, Axel & Nichele, Stefano (2021). Hallmarks of Criticality in Neuronal Networks Depend on Cell Type and the Temporal Resolution of Neuronal Avalanches. International Journal of Unconventional Computing. ISSN 1548-7199. 16(4), p. 303–326. Full text in Research Archive Show summary

  The human brain has a remarkable capacity for computation, and it has been theorized that this capacity arises from the brain self-organizing into the critical state, a dynamical state poised between ordered and dis- ordered behavior and widely considered to be well-suited for computation. Criticality is commonly identified in in vitro neuronal networks using an analytical approach based on the size distribution of cascades of activity called neuronal avalanches. In this study, criticality analysis was applied to different in vitro neuronal networks with two areas of focus: evaluating the effect of the size of the time bins used for neuronal avalanche detection and observation of the development of networks of neurons derived from human induced pluripotent stem cells. This pre- preliminary study is expected to aid in the construction of models capable of emulating neuronal behaviors identified as well-suited for computation and ultimately inform the development of brain-inspired computing substrates that are better able to keep pace with increased demand for data storage and processing power.

• Heiney, Kristine; Ramstad, Ola Huse; Fiskum, Vegard; Christiansen, Nicholas; Sandvig, Axel & Nichele, Stefano [Show all 7 contributors for this article] (2021). Criticality, connectivity, and neural disorder: A multifaceted approach to neural computation. Frontiers in Computational Neuroscience. 15:611183, p. 1–19. doi: 10.3389/fncom.2021.611183. Full text in Research Archive Show summary

  It has been hypothesized that the brain optimizes its capacity for computation by self-organizing to a critical point. The dynamical state of criticality is achieved by striking a balance such that activity can effectively spread through the network without overwhelming it and is commonly identified in neuronal networks by observing the behavior of cascades of network activity termed “neuronal avalanches.” The dynamic activity that occurs in neuronal networks is closely intertwined with how the elements of the network are connected and how they influence each other’s functional activity. In this review, we highlight how studying criticality with a broad perspective that integrates concepts from physics, experimental and theoretical neuroscience, and computer science can provide a greater understanding of the mechanisms that drive networks to criticality and how their disruption may manifest in different disorders. First, integrating graph theory into experimental studies on criticality, as is becoming more common in theoretical and modeling studies, would provide insight into the kinds of network structures that support criticality in networks of biological neurons. Furthermore, plasticity mechanisms play a crucial role in shaping these neural structures, both in terms of homeostatic maintenance and learning. Both network structures and plasticity have been studied fairly extensively in theoretical models, but much work remains to bridge the gap between theoretical and experimental findings. Finally, information theoretical approaches can tie in more concrete evidence of a network’s computational capabilities. Approaching neural dynamics with all these facets in mind has the potential to provide a greater understanding of what goes wrong in neural disorders. Criticality analysis therefore holds potential to identify disruptions to healthy dynamics, granted that robust methods and approaches are considered.

• Valderhaug, Vibeke Devold; Ramstad, Ola Huse; Wijdeven, Rosanne Francisca van de; Heiney, Kristine; Nichele, Stefano & Sandvig, Axel [Show all 7 contributors for this article] (2020). Structural and functional alterations associated with the LRRK2 G2019S mutation revealed in structured human neural networks. BioRxiv. ISSN 0362-4331. doi: 10.1101/2020.05.02.073726. Full text in Research Archive Show summary

  Mutations in the LRRK2 gene have been widely linked to Parkinson’s disease. The G2019S variant has been shown to contribute uniquely to both familial and sporadic forms of the disease. LRRK2-related mutations have been extensively studied, yet the wide variety of cellular and network events directly or indirectly related to these mutations remain poorly understood. In this study, we structured multi-nodal human neural networks carrying the G2019S mutation using custom-designed microfluidic chips coupled to microelectrode-arrays. By applying live imaging approaches, immunocytochemistry and computational modelling, we have revealed alterations in both the structure and function of the resulting neural networks when compared to controls. We provide first evidence of increased neuritic density associated with the G2019S LRRK2 mutation, while previous studies have found either a strong decrease, or no change, compared to controls. Additionally, we corroborate previous findings regarding increased baseline network activity compared to control neural networks. Furthermore, we can reveal additional network alterations attributable to the specific mutation by selectively inducing transient overexcitation to confined parts of the structured multi-nodal networks. These alterations, which we were able to capture both at the micro- and mesoscale manifested as differences in relative network activity and correlation, as well as in mitochondria activation, neuritic remodelling, and synaptic alterations. Our study thus provides important new insights into early signs of neural network pathology significantly expanding upon the current knowledge relating to the G2019S Parkinson’s disease mutation.

• Valderhaug, Vibeke Devold; Heiney, Kristine; Ramstad, Ola Huse; Bråthen, Geir; Kuan, Wei-Li & Nichele, Stefano [Show all 8 contributors for this article] (2020). Criticality as a measure of developing proteinopathy in engineered human neural networks. BioRxiv. ISSN 0362-4331. doi: 10.1101/2020.05.03.074666. Full text in Research Archive Show summary

  A patterned spread of proteinopathy represents a common characteristic of many neurodegenerative diseases. In Parkinson’s disease (PD), misfolded forms of alpha-synuclein proteins aggregate and accumulate in hallmark pathological inclusions termed Lewy bodies and Lewy neurites, which seems to affect selectively vulnerable neuronal populations and propagate within interconnected neuronal networks. Research findings suggest that these proteinopathic inclusions are present at very early timepoints in disease development, even before strong behavioural symptoms of dysfunction arise, but that these underlying pathologies might be masked by homeostatic processes working to maintain the function of the degenerating neural circuits. This study investigates whether inducing the PD-related alpha-synuclein pathology in engineered human neural networks can be associated with changes in network function, and particularly with network criticality states. Self-organised criticality represents the critical point between resilience against perturbation and adaptational flexibility, which appears to be a functional trait in self-organising neural networks, both in vitro and in vivo. By monitoring the developing neural network activity through the use of multielectrode arrays (MEAs) for a period of three weeks following proteinopathy induction, we show that although this developing pathology is not clearly manifest in standard measurements of network function, it may be discerned by differences in network criticality states.

• Heiney, Kristine; Ramstad, Ola Huse; Sandvig, Ioanna; Sandvig, Axel & Nichele, Stefano (2020). Assessment and manipulation of the computational capacity of in vitro neuronal networks through criticality in neuronal avalanches. In Huang, Tingwen (Eds.), Proceedings of IEEE Symposium Series on Computational Intelligence (SSCI2019). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728124858. p. 246–253. doi: 10.1109/SSCI44817.2019.9002693. Full text in Research Archive Show summary

  In this work, we report the preliminary analysis of the electrophysiological behavior of in vitro neuronal networks to identify when the networks are in a critical state based on the size distribution of network-wide avalanches of activity. The results presented here demonstrate the importance of selecting appropriate parameters in the evaluation of the size distribution and indicate that it is possible to perturb networks showing highly synchronized—or supercritical—behavior into the critical state by increasing the level of inhibition in the network. The classification of critical versus non-critical networks is valuable in identifying networks that can be expected to perform well on computational tasks, as criticality is widely considered to be the state in which a system is best suited for computation. In addition to enabling the identification of networks that are well-suited for computation, this analysis is expected to aid in the classification of networks as perturbed or healthy. This study is part of a larger research project, the overarching aim of which is to develop computational models that are able to reproduce target behaviors observed in in vitro neuronal networks. These models will ultimately be used to aid in the realization of these behaviors in nanomagnet arrays to be used in novel computing hardwares.

View all works in NVA

• Heiney, Kristine Anne; Ciceri, Simone; Sprekeler, Henning & O'Leary, Timothy (2026). Experience-driven representational drift in an excitatory–inhibitory circuit model. Full text in Research Archive Show summary

  When an animal performs a familiar task, the relationship between the neural activity and task variables changes gradually over time, without any accompanying measurable change in behavioural performance. Experimental evidence demonstrates that this phenomenon, known as representational drift, is influenced by an animal’s repeated experience of the task. Using circuit models with feedforward and feedback inhibition, we show how task experience may drive representational drift. Our model consists of populations of output excitatory neurons receiving excitatory signals from input neurons and feedback or feedforward inhibitory signals from inhibitory neurons. Each input neuron is assigned bump-like tuning to the task variable q, and the inhibitory neurons inherit their tuning from the output neurons. The weights between the E and I neurons are plastic and follow Hebbian and homeostatic rules, and the input weights Win are fixed. To model a single session in which the task is performed, only a subset of the input neurons are activated. We repeated this for multiple sessions by sampling different subsets and compared the resultant representations. Under certain conditions, our models can exhibit hallmarks of representational drift driven by this sampling. We show how the inhibitory population acts to guide the changing representations within a degenerate solution space.

• Heiney, Kristine Anne (2025). Relating representational drift to neural circuit interactions. Full text in Research Archive Show summary

  In many parts of the brain, population tuning to stimuli and behaviour gradually changes over the course of days to weeks in a phenomenon known as representational drift. The tuning stability of individual cells varies over the population, and it remains unclear what drives this heterogeneity. In this talk, I will first discuss how a neuron’s tuning stability relates to its shared variability with other neurons in the population using two published datasets from posterior parietal cortex and visual cortex. In this analysis, we quantified the contribution of pairwise interactions to behaviour or stimulus encoding by partial information decomposition, which breaks down the mutual information between the pairwise neural activity and the external variable into components uniquely provided by each neuron and by their interactions. Information shared by the two neurons is termed ‘redundant’, and information requiring knowledge of the state of both neurons is termed ‘synergistic’. We found that a neuron's tuning stability is positively correlated with the strength of its average pairwise redundancy with the population, and that these high-redundancy neurons also tend to show high average pairwise synergy. We hypothesise that subpopulations of neurons show greater stability because they are tuned to salient features common across multiple tasks. At the end of the talk, I will briefly discuss ongoing work on modelling representational drift in excitatory–inhibitory rate-based circuit models, building on our findings from this data analysis.

• Heiney, Kristine Anne (2024). Transitions: Moving within science. Full text in Research Archive
• Valderhaug, Vibeke Devold; Ramstad, Ola Huse; Wijdeven, Rosanne Francisca van de; Heiney, Kristine; Nichele, Stefano & Sandvig, Ioanna [Show all 7 contributors for this article] (2024). MICRO- AND MESOSCALE ASPECTS OF NEURODEGENERATION IN MULT-NODAL HUMAN NEURAL NETWORKS CARRYING THE LRRK2 G2019S MUTATION. Full text in Research Archive
• Ramstad, Ola Huse; Wijdeven, Rosanne Francisca van de; Heiney, Kristine; Nichele, Stefano; Sandvig, Axel & Sandvig, Ioanna (2024). MICRO- AND MESOSCALE ASPECTS OF NEURODEGENERATION IN MULT-NODAL HUMAN NEURAL NETWORKS CARRYING THE LRRK2 G2019S MUTATION. Full text in Research Archive
• Ramstad, Ola Huse; Wijdeven, Rosanne Francisca van de; Heiney, Kristine; Nichele, Stefano; Sandvig, Axel & Sandvig, Ioanna (2024). MICRO- AND MESOSCALE ASPECTS OF NEURODEGENERATION IN MULTI-NODAL HUMAN NEURAL NETWORKS CARRYING THE LRRK2 G2019S MUTATION. Full text in Research Archive
• Heiney, Kristine Anne; Jozsa, Monika; Rule, Michael E.; Nichele, Stefano & O'Leary, Timothy (2023). Rate of representational drift correlates with information theoretic measures of neural and behavioural coupling. Full text in Research Archive
• Farner, Jørgen Jensen; Ramstad, Ola Huse; Nichele, Stefano & Heiney, Kristine (2023). Beyond weight plasticity: Local learning with propagation delays in spiking neural networks. Full text in Research Archive
• Heiney, Kristine Anne; Józsa, Mónika; Rule, Michael; Nichele, Stefano & O'Leary, Timothy (2023). Encoding of behaviour by pairwise neuronal interactions correlates with representational drift. Full text in Research Archive
• Heiney, Kristine Anne; Józsa, Mónika; Rule, Michael; Nichele, Stefano & O'Leary, Timothy (2023). Neural correlations, information, and representational drift. Full text in Research Archive
• Heiney, Kristine Anne; Józsa, Mónika; Rule, Michael; Nichele, Stefano & O'Leary, Timothy (2022). Encoding of behaviour by pairwise neuronal interactions correlates with representational drift. Full text in Research Archive
• Heiney, Kristine Anne; Rule, Michael; Józsa, Mónika; Nichele, Stefano & O'Leary, Timothy (2022). Changing to stay the same. Full text in Research Archive
• Heiney, Kristine Anne; Ramstad, Ola Huse; Fiskum, Vegard; Sandvig, Axel; Sandvig, Ioanna & Nichele, Stefano (2022). Neuronal avalanche dynamics and functional connectivity as indicators of the computational capacity of in vitro neuronal networks. Full text in Research Archive Show summary

  The brain has long been a source of inspiration for the development of novel computational methods and architectures. Most notably, modern machine learning (ML) and deep learning (DL) algorithms in the field of artificial intelligence (AI) are based on an artificial neuron model, and such approaches have enabled us to process data on a previously unimaginable scale. However, current ML and DL approaches are computationally expensive and task-specific, suggesting we have much yet to learn from the efficiency and learning capabilities of the brain. In this work, we evaluated the dynamics and functional connectivity of networks of cortical neurons as they matured over approximately two months in vitro. The electrophysiological activity of the networks was captured daily using microelectrode arrays (MEAs). To assess the dynamics, we identified patterns of activity termed ‘neuronal avalanches’ and used this activity to compute the branching ratio and complexity of the observed activity. The branching ratio describes how activity is propagated through the network and allows the activity to be classified into different dynamical regimes, while the complexity provides a measure that is maximized when activity indicates a balance between integration and segregation in the network. Graphs representing the functional connectivity of the network before were extracted from their electrophysiological behavior, and these graphs were used to evaluate the relationship between the avalanche dynamics and the connectivity of the network. This work serves as a starting point for the data-driven development of biologically plausible models to inform AI algorithms. Future work will involve information theoretical analysis of the network behavior as well as investigations into the role of plasticity in determining the dynamical state. By evaluating the interplay between computation and connectivity, we hope to drive the development of novel powerful AI models.

• Heiney, Kristine Anne; Józsa, Mónika; Rule, Michael; Nichele, Stefano & O'Leary, Timothy (2022). Encoding of behaviour by pairwise neuronal interactions correlates with representational drift. Full text in Research Archive Show summary

  In some brain areas, populations of neurons can change their behavioural tuning gradually over days. At the single cell level, the rate of this drift varies considerably, raising the question: Does the amount of information contained in interactions between cells predict how much they drift? We reanalysed data from Driscoll et al. (2017), in which the activity of mouse posterior parietal cortex (PPC) neurons was tracked over weeks as mice performed a learned navigation task. We quantified the informativeness of pairwise interactions by measuring the mutual information between behaviour and pairwise neuronal responses, relative to a conditionally shuffled baseline. This conditional shuffling preserves the tuning of single cells while destroying noise correlations or higher-order dependencies in a pair. The amount of pairwise information relative to this baseline can be synergistic (positive) or redundant (negative). After thresholding these measures, we found that neurons with high average pairwise redundancy typically also showed greater average pairwise synergy. Furthermore, the average redundancy was positively correlated with tuning stability, more strongly than was single-cell informativeness. This suggests that redundant subpopulations of neurons form a more stable ‘backbone’ that is less susceptible to drift and more informative about behaviour on average. Thus, redundant coupling is more predictive of stable tuning than the information conveyed about behaviour at the single cell level.

• Farner, Jørgen Jensen; Weydahl, Håkon; Jahren, Ruben; Ramstad, Ola Huse; Nichele, Stefano & Heiney, Kristine Anne (2021). Evolving spiking neuron cellular automata and networks to emulate in vitro neuronal activity. Full text in Research Archive Show summary

  Neuro-inspired models and systems have great potential for applications in unconventional computing. Often, the mechanisms of biological neurons are modeled or mimicked in simulated or physical systems in an attempt to harness some of the computational power of the brain. However, the biological mechanisms at play in neural systems are complicated and challenging to capture and engineer; thus, it can be simpler to turn to a data-driven approach to transfer features of neural behavior to artificial substrates. In the present study, we used an evolutionary algorithm to produce spiking neural systems that emulate the patterns of behavior of biological neurons in vitro. The aim of this approach was to develop a method of producing models capable of exhibiting complex behavior that may be suitable for use as computational substrates. Our models were able to produce a level of network-wide synchrony and showed a range of behaviors depending on the target data used for their evolution, which was from a range of neuronal culture densities and maturities. The genomes of the top-performing models indicate the excitability and density of connections in the model play an important role in determining the complexity of the produced activity.

• Heiney, Kristine; Ramstad, Ola Huse; Fiskum, Vegard; Sandvig, Axel; Sandvig, Ioanna & Nichele, Stefano (2021). From in vitro to computational models of the brain: What can criticality tell us about neural computation? Full text in Research Archive Show summary

  It has been hypothesized that some biological systems, including parts of the brain, may operate near the critical point of a phase transition, delicately balanced between ordered and disordered behavior. Criticality maximizes a number of properties that are favorable for computation, such as dynamic range, information transmission, and the number of input–output mappings; put more simply, systems at criticality are well-suited to take inputs for some computational problem, perform transformations on the inputs as they propagate through the system, and give meaningful outputs. In this talk, I will present our ongoing work on evaluating the closeness of in vitro neuronal networks to criticality and discuss the implications of our findings for understanding neural computation and the development of bioinspired computing methods and hardware. To determine the closeness of networks to criticality, the spatiotemporal scaling behavior of neuronal avalanches is observed, and we compare this behavior across networks with different seeding densities as they mature. The functional connectivity is also obtained for these networks and the small-worldness assessed using the small-world propensity measure. Additionally, the excitation-to-inhibition ratio was chemically manipulated to evaluate its effect on the network dynamics. Although none of our networks tended to mature toward the critical state, we observed consistent differences across the two considered densities in the avalanche scaling and branching ratio indicative of a greater dominance of network bursts in the higher-density networks. Additionally, the lower-density networks tended to show small-world organization in their functional connectivity. Chemical perturbation to increase inhibition brought the networks closer to criticality, and the higher-density networks showed greater disruption to both their functional activity and avalanche behavior under chemical perturbation. From these findings, we aim to find connectivity and activity patterns correlated with closeness to criticality so that we may emulate these features in models for bioinspired computation. Future work will also involve evaluating measures of information processing in these networks as indicators of the suitability of these networks for computation.

• Heiney, Kristine; Tufte, Gunnar & Nichele, Stefano (2021). On Artificial Life and Emergent Computation in Physical Substrates. Full text in Research Archive Show summary

  In living systems, we often see the emergence of the ingredients necessary for computation---the capacity for information transmission, storage, and modification---begging the question of how we may exploit or imitate such biological systems in unconventional computing applications. What can we gain from artificial life in the advancement of computing technology? Artificial life provides us with powerful tools for understanding the dynamic behavior of biological systems and capturing this behavior in manmade substrates. With this approach, we can move towards a new computing paradigm concerned with harnessing emergent computation in physical substrates not governed by the constraints of Moore's law and ultimately realize massively parallel and distributed computing technology. In this paper, we argue that the lens of artificial life offers valuable perspectives for the advancement of high-performance computing technology. We first present a brief foundational background on artificial life and some relevant tools that may be applicable to unconventional computing. Two specific substrates are then discussed in detail: biological neurons and ensembles of nanomagnets. These substrates are the focus of the authors' ongoing work, and they are illustrative of the two sides of the approach outlined here---the close study of living systems and the construction of artificial systems to produce life-like behaviors. We conclude with a philosophical discussion on what we can learn from approaching computation with the curiosity inherent to the study of artificial life. The main contribution of this paper is to present the great potential of using artificial life methodologies to uncover and harness the inherent computational power of physical substrates toward applications in unconventional high-performance computing.

• Heiney, Kristine (2021). Intersections of Neuroscience and Artificial Intelligence: Do our computers have a mind of their own? Full text in Research Archive
• Heiney, Kristine; Ramstad, Ola Huse; Pontes-Filho, Sidney; Glover, Tom; Lindell, Trym & Farner, Jørgen Jensen [Show all 8 contributors for this article] (2021). Bridging the computational gap: From biological to artificial substrates. Full text in Research Archive
• Heiney, Kristine; Ramstad, Ola Huse; Sandvig, Axel; Sandvig, Ioanna & Nichele, Stefano (2020). Manipulating the dynamic state of in vitro neuronal networks. Full text in Research Archive Show summary

  The dynamic state of neuronal networks is influenced by a wide range of functional and structural factors, including network connectivity, synaptic function, and the balance of excitation and inhibition in the network. Additionally, it has been shown that only some dissociated networks self-organize into the critical state as they are left to mature in vitro, with many ultimately showing the tightly synchronized behavior indicative of a supercritical state. To explore the possibility of manipulating dissociated networks into the critical state by chemical intervention, we increased inhibition in the network by adding -aminobutyric acid (GABA) at day in vitro (DIV) 51. Prior to perturbation with GABA, the networks showed large network-wide bursts with close synchrony, and the size distribution of neuronal avalanches was bimodal. After perturbation, the synchrony was broken, and the avalanche distribution followed a power law, one of the hallmarks of criticality. This finding indicates that it is possible to use chemical perturbation in dissociated networks to manipulate the dynamic state. This will allow for future comparative experimental studies on the characteristics and computational capabilities of networks in different dynamical regimes, shedding light on the mechanisms and benefits of critical and near-critical behavior.

• Heiney, Kristine; Ramstad, Ola Huse; Fiskum, Vegard; Sandvig, Axel; Sandvig, Ioanna & Nichele, Stefano (2020). Manipulation of the scaling behavior of neuronal avalanches toward achieving criticality. Full text in Research Archive
• Heiney, Kristine; Ramstad, Ola Huse; Fiskum, Vegard; Sandvig, Ioanna; Sandvig, Axel & Nichele, Stefano (2020). Computational behavior of biological neural networks through the lens of criticality. Full text in Research Archive Show summary

  The human brain is a powerful computational machine, and harnessing some of this power in manmade computing systems would enable advances in terms of computational speed, power consumption, and fault tolerance. One interesting phenomenon theorized to occur in the brain is self-organized criticality. The critical state is a dynamical regime poised between ordered behavior and chaotic behavior and recognized as good for computation. Systems in the critical state have been shown to optimize performance by many measures, including dynamic range, complexity, and information transmission. It has been proposed that the brain self-organizes into this state by striking a balance between excitation and inhibition to optimize its computational capacity. Networks of neurons in vitro can be classified critical if the size of network-wide cascades of activity, termed “neuronal avalanches,” follows a power-law distribution. This method of classification provides a path to identifying networks that are likely suited for computation. Our lab has conducted experiments to assess the dynamical state of networks of dissociated cortical neurons as they mature in vitro. It was found that the networks progressed from a period of low activity to a subcritical state before settling into a supercritical state, characterized by tight network-wide synchronization. It has been reported that not all dissociated networks self-organize into the critical state, begging the question: what can be done to induce criticality in these cases? To adjust the excitation-to-inhibition ratio toward the balance expected at criticality, small volumes of an inhibitory neurotransmitter (GABA) were incrementally added to the networks. After perturbation with GABA, it was observed that the networks showed avalanche activity with power-law scaling, manifested in more complex patterns of activity in the network, indicating that it is possible to manipulate networks into the critical state. The ability to identify the dynamical state of networks and manipulate them into the critical state may enable the comparison of the architectures and features of networks in different states via different information and network theoretical measures. Identifying features correlated with networks at criticality will provide a foundation for the behaviors we wish to replicate as we develop novel neuroscience-inspired artificial neural network models and biologically plausible learning algorithms.

• Heiney, Kristine (2020). Learning to compute with the language of neurons. Full text in Research Archive Show summary

  The human brain has a remarkable capacity to receive, process, and store information with very little power consumption. Despite having 86 billion neurons forming over 100 trillion connections, the brain consumes only about 20 Watts of power—compare this to the 7 billion transistors in an iPhone 10 and the 60 Watts needed to power a lightbulb and you can begin to see the immense and efficient computational capacity of the brain. Modern microelectronics technology has given us the tools to observe the electrical signaling behavior of networks of neurons grown in the lab to better understand how they form connections with each other and propagate information through the network. With the insights we obtain by analyzing these signals, we are working to emulate targeted behaviors in models and physical substrates toward a new computing paradigm that will enable vast and scalable computing capabilities with a lower energy cost.

• Heiney, Kristine (2019). The winding road from Ithaca to Oslo: My career path as a woman in STEM. Full text in Research Archive
• Heiney, Kristine & Mello, Gustavo (2019). An Introduction to Artificial Intelligence and the OsloMet AI Lab. Full text in Research Archive
• Ramstad, Ola Huse; Wijdeven, Rosanne Francisca van de; Heiney, Kristine; Nichele, Stefano; Sandvig, Ioanna & Sandvig, Axel (2019). Mapping functional response to axotomy in a reductionist corticospinal network. Full text in Research Archive
• Heiney, Kristine; Valderhaug, Vibeke Devold; Sandvig, Ioanna; Tufte, Gunnar; Hammer, Hugo Lewi & Nichele, Stefano (2019). Evaluating the criticality of neuronal networks toward the identification of pathological conditions. Full text in Research Archive
• Heiney, Kristine; Valderhaug, Vibeke Devold; Ramstad, Ola Huse; Sandvig, Ioanna; Sandvig, Axel & Tufte, Gunnar [Show all 8 contributors for this article] (2019). Evaluation of the criticality of in vitro neuronal networks: Toward an assessment of computational capacity. Full text in Research Archive Show summary

  Novel computing hardwares are necessary to keep up with today's increasing demand for data storage and processing power. In this research project, we turn to the brain for inspiration to develop novel computing substrates that are self-learning, scalable, energy-efficient, and fault-tolerant. The overarching aim of this work is to develop computational models that are able to reproduce target behaviors observed in in vitro neuronal networks. These models will be ultimately be used to aid in the realization of these behaviors in a more engineerable substrate: an array of nanomagnets. The target behaviors will be identified by analyzing electrophysiological recordings of the neuronal networks. Preliminary analysis has been performed to identify when a network is in a critical state based on the size distribution of network-wide avalanches of activity, and the results of this analysis are reported here. This classification of critical versus non-critical networks is valuable in identifying networks that can be expected to perform well on computational tasks, as criticality is widely considered to be the state in which a system is best suited for computation. This type of analysis is expected to enable the identification of networks that are well-suited for computation and the classification of networks as perturbed or healthy.

• Heiney, Kristine (2019). Criticality and computation in neuronal networks. Full text in Research Archive
• Ramstad, Ola Huse; Wijdeven, Rosanne Francisca van de; Heiney, Kristine; Nichele, Stefano; Sandvig, Ioanna & Sandvig, Axel (2019). Mapping functional response to axotomy in a reductionist corticospinal circuit. Full text in Research Archive
• Heiney, Kristine; Ramstad, Ola Huse; Sandvig, Ioanna; Sandvig, Axel & Nichele, Stefano (2019). Assessment and manipulation of the computational capacity of in vitro neuronal networks through criticality in neuronal avalanches. Full text in Research Archive
• Heiney, Kristine; Ramstad, Ola Huse; Sandvig, Ioanna; Sandvig, Axel & Nichele, Stefano (2019). Assessment and manipulation of the computational capacity of in vitro neuronal networks through criticality in neuronal avalanches. Full text in Research Archive Show summary

  The brain is an effective and efficient computational machine, yet the precise mechanisms it uses to perform computations are poorly understood. As demand for technologies capable of storing and processing large amounts of data increases, it would be beneficial to harness the computational power of the brain in engineerable computing hardwares; however, to recapitulate the desired behaviors, we must first grasp the dynamics underlying the communication within networks of neurons. To this end, a preliminary analysis of the electrophysiological behavior of in vitro neuronal networks of primary rat cortical neurons was performed to identify when the networks are in a critical state based on the size distribution of network-wide avalanches of activity. The critical state is defined as a transitional state between static or cyclical behavior and highly disordered or hyperactive behavior, and systems in the critical state are thought to be in the optimal conditions to perform computational tasks. The neuronal networks were observed as they matured from day in vitro 7 to 51 and were chemically perturbed with GABA on day 51 to determine if networks that do not reach the critical state during normal maturation can be manipulated into the critical state by reducing the excitation-to-inhibition ratio. The results presented here demonstrate the importance of selecting appropriate parameters in the evaluation of the size distribution and indicate that it is possible to perturb networks showing highly synchronized—or supercritical—behavior into the critical state by increasing the level of inhibition in the network. The classification of critical versus non-critical networks is valuable in identifying networks that can be expected to perform well on computational tasks, and it is expected that perturbed networks or disease models may show different behaviors with regard to criticality during the course of maturation. This study is part of a larger research project, the overarching aim of which is to develop computational models that are able to reproduce target behaviors observed in in vitro neuronal networks. These models will ultimately be used to aid in the realization of these behaviors in nanomagnet arrays to be used in novel computing hardwares.

• Heiney, Kristine (2018). SOMA: Self-Organizing Models of Artificial learning, toward strategies to restore perturbed dynamics – PhD project plan. Full text in Research Archive
• Heiney, Kristine Anne & Nichele, Stefano (2023). Deciphering and emulating neuronal communication: Population-level computation from elemental interactions. Norges teknisk-naturvitenskapelige universitet. ISSN 9788232671397. Full text in Research Archive Show summary

  This thesis explores computation in neural systems and how we might draw inspiration from it in designing computational systems. The research conducted for this thesis encompasses both neural data analysis and computational modeling. In both of these research components, the overarching goal of this thesis is to elucidate how representations at the system level emerge from the communication of the system components, both to aid in expanding our understanding of neural computation and to support the development of novel computing substrates and algorithms. The first half of the thesis considers how to decipher the language of neurons. We focus on the level of populations of neurons—tens to thousands of interconnected cells, both in vivo and in vitro and evaluate how the interactions of the individual cells lead to computation at the population level. In our in vivo study, we evaluated how a neuron’s interactions with the population relate to the stability of its tuning to a task. In our in vitro work, we probed the dynamics and network organization of dissociated neuronal populations and assessed how these might be tuned to optimize their capacity of the populations to represent and process information. The second half of the thesis examines how the lessons we learned from studying neural systems can inform the development of engineered computational systems. We pursue this using both a bottom-up approach, where the system-level behavior emerges from the designed elements and their interactions, and a top-down approach, where the system behavior is explicitly targeted and the components are iteratively refined until the target is reached. In our bottom-up approach, we de-signed a novel learning rule to locally adjust the speed at which activity propagates in a spiking neural network and successfully used it to train networks for a classification task. In our top-down approach, we used evolutionary algorithms to design models targeting two different fitness goals: the first, to match activity patterns observed in neuronal populations in vitro, and the second, to maximize the separability of output waveforms in a hydrodynamic computational reservoir. Our findings highlight the relevance of understanding the nature of elemental interactions to make sense of computation at the system level and demonstrate the myriad lessons we can learn from neural computation in designing computational systems.

• Heiney, Kristine; Nichele, Stefano; Sandvig, Ioanna & Tufte, Gunnar (2023). Deciphering and emulating neuronal communication: Population-level computation from elemental interactions. NTNU. Full text in Research Archive

View all works in NVA