Neural Coding
neural coding
Eugenio Piasini
A two-year postdoctoral position in computational neuroscience and neural coding is open to investigate the role of hippocampal-dependent memory function in visual perceptual learning. The postdoc will work in Eugenio Piasini's group at the International School for Advanced Studies (SISSA), in close collaboration with Manuela Allegra at the Italian National Research Council (CNR).
Matthew Chalk
A postdoctoral position is available for a project with Matthew Chalk (https://matthewjchalk.wixsite.com/mysite), at the Vision Institute (www.institut-vision.org/en/), within the Sorbonne Université, in Paris, France. The project will involve investigating principles of neural coding in the retina. Specifically, the project will investigate how different coding objectives, such as optimising efficiency or encoding predictive information, can explain the diverse ways that neurons in the retina respond to visual stimulation. The project will extend previous work by Chalk et al. to develop a general theory of optimal neural coding (Chalk et al. PNAS 2018, Chalk et al. 2022 biorxiv). For this, we will use a range of computational techniques including gaussian processes (Goldin et al. 2023 PNAS) and information theory. The project is part of an exciting interdisciplinary collaboration between theorists and experimentalists at the Vision Institute (Olivier Marre; http://oliviermarre.free.fr), and Thomas Euler (https://eulerlab.de) and Philip Berens (https://www.eye-tuebingen.de/berenslab/) at Tuebingen University. The Vision Institute is a stimulating environment for brain research. It brings together in a single building researchers, clinicians and industrial partners in order to discover, test and develop treatments and technological innovations for the benefit of visually impaired patients. The candidate will have a PhD with a strong, quantitative background (ideally in fields such as machine learning, theoretical neuroscience or physics). They will have a good grasp of oral and written English (French is not required). Most of all, they will enjoy tackling new problems with enthusiasm and as part of a team. The position is funded for three years. Applications should include a CV, a statement of research interests (~ 1 page), and two letters of recommendation. Electronic submissions in pdf-format are preferred and should be sent to Matthew Chalk (matthewjchalk@gmail.com). Feel free to ask any informal questions about the position if you are interested.
Prof Mario Dipoppa
We are looking for candidates, who are eager to solve fundamental questions with a creative mindset. Candidates should have a strong publication track record in Computational Neuroscience or a related quantitative field, including but not limited to Computer Science, Machine Learning, Engineering, Bioinformatics, Physics, Mathematics, and Statistics. Candidates holding a Ph.D. degree interested in joining the laboratory as postdoctoral researchers should submit a CV including a publication list, a copy of a first-authored publication, a research statement describing past research and career goals (max. two pages), and contact information for two academic referees. The selected candidates will be working on questions addressing how brain computations emerge from the dynamics of the underlying neural circuits and how the neural code is shaped by computational needs and biological constraints of the brain. To tackle these questions, we employ a multidisciplinary approach that combines state-of-the-art modeling techniques and theoretical frameworks, which include but are not limited to data-driven circuit models, biologically realistic deep learning models, abstract neural network models, machine learning methods, and analysis of the neural code. Our research team, the Theoretical and Computational Neuroscience Laboratory, is on the main UCLA campus and enjoys close collaborations with the world-class neuroscience community there. The lab, led by Mario Dipoppa, is a cooperative and vibrant environment where all members are offered excellent scientific training and career mentoring. We strongly encourage candidates to apply early as applications will be reviewed until the positions are filled. The positions are available immediately with a flexible starting date. Please submit the application material as a single PDF file with your full name in the file name to mdipoppa@g.ucla.edu. Informal inquiries are welcome. For more details visit www.dipoppalab.com.
Prof Mario Dipoppa
We are looking for candidates with a keen interest in gaining research experience in Computational Neuroscience, pursuing their own projects, and supporting those of other team members. Candidates should have a bachelor's or master's degree in a quantitative discipline and strong programming skills, ideally in Python. Candidates interested in joining the laboratory as research associates should send a CV, a research statement describing past research and career goals (max. one page), and contact information for two academic referees. The selected candidates will be working on questions addressing how brain computations emerge from the dynamics of the underlying neural circuits and how the neural code is shaped by computational needs and biological constraints of the brain. To tackle these questions, we employ a multidisciplinary approach that combines state-of-the-art modeling techniques and theoretical frameworks, which include but are not limited to data-driven circuit models, biologically realistic deep learning models, abstract neural network models, machine learning methods, and analysis of the neural code. Our research team, the Theoretical and Computational Neuroscience Laboratory, is on the main UCLA campus and enjoys close collaborations with the world-class neuroscience community there. The lab, led by Mario Dipoppa, is a cooperative and vibrant environment where all members are offered excellent scientific training and career mentoring. We strongly encourage candidates to apply early as applications will be reviewed until the positions are filled. The positions are available immediately with a flexible starting date. Please submit the application material as a single PDF file with your full name in the file name to mdipoppa@g.ucla.edu. Informal inquiries are welcome. For more details visit www.dipoppalab.com.
Prof Tim Gollisch
The work includes participation in recordings from the isolated retina (mostly mouse) with multielectrodes, using both wild-type retinas and optogenetic retina models of vision restoration therapy. Patch-clamp recordings are also a possibility. A strong focus will then be to combine these experiments with novel tools for data analysis and mathematical modeling, using cascade-type models (linear-nonlinear models and beyond), artificial neural networks, or machine-learning techniques to analyze the retinal network. See the announcement at https://www.retina.uni-goettingen.de/join-the-lab/ for more information as well as for contact information.
Prof. Wenhao Zhang
The Computational Neuroscience lab directed by Dr. Wenhao Zhang at the University of Texas Southwestern Medical Center (www.zhang-cnl.org) is currently seeking up to two postdoctoral fellows to study cutting edge problems in computational neuroscience. Research topics include: 1). The neural circuit implementation of normative computation, e.g., Bayesian (causal) inference. 2). Dynamical analysis of recurrent neural circuit models. 3). Modern deep learning methods to solve neuroscience problems. Successful candidates are expected to play an active and independent role in one of our research topics. All projects are strongly encouraged to collaborate with experimental neuroscientists both in UT Southwestern as well as abroad. The initial appointment is for one year with the expectation of extension given satisfactory performance. UT Southwestern provides competitive salary and benefits packages.
Prof Wenhao Zhang
The Computational Neuroscience lab directed by Dr. Wenhao Zhang at the University of Texas Southwestern Medical Center (www.zhang-cnl.org) is currently seeking up to two postdoctoral fellows to study cutting edge problems in computational neuroscience. Research topics include: 1). The neural circuit implementation of normative computation, e.g., Bayesian (causal) inference. 2). Dynamical analysis of recurrent neural circuit models. 3). Modern deep learning methods to solve neuroscience problems. Successful candidates are expected to play an active and independent role in one of our research topics. All projects are strongly encouraged to collaborate with experimental neuroscientists both in UT Southwestern as well as abroad. The initial appointment is for one year with the expectation of extension given satisfactory performance. UT Southwestern provides competitive salary and benefits packages.
Prof Chengcheng Huang
The neural dynamics and computation group of Dr. Chengcheng Huang at the University of Pittsburgh has a postdoctoral position available. The Huang group investigates the nervous system using computational modeling and mathematical analysis of neural networks. We develop theoretical approaches to understanding circuit dynamics and information processing in sensory systems. We are interested in how different task and stimulus contexts change neuronal responses and the implications on neural coding, with an emphasis on neural variability. Potential projects include 1) the functional roles of interneuron subtypes in sensory and associative cortex, 2) attentional modulation on information flow and 3) interareal communication with feedforward and feedback connections. The candidate will have the opportunity to work with our experimental collaborators at the University of Pittsburgh.
Prof. Ross Williamson
The Williamson Laboratory investigates the organization and function of auditory cortical projection systems in behaving mice. We use a variety of state-of-the-art tools to probe the neural circuits of awake mice – these include two-photon calcium imaging and high-channel count electrophysiology (both with single-cell optogenetic perturbations), head-fixed behaviors (including virtual reality), and statistical approaches for neural characterization. Details on the research focus and approaches of the laboratory can be found here: https://www.williamsonlaboratory.com/research/
Eugenio Piasini
A two-year postdoctoral position in computational neuroscience and neural coding is open to investigate the role of hippocampal-dependent memory function in visual perceptual learning. The postdoc will work in Eugenio Piasini's group at the International School for Advanced Studies (SISSA), in close collaboration with Manuela Allegra at the Italian National Research Council (CNR). Although this role is focused on a specific project, we are happy to support the development of the postdoc's individual research pursuits.
N/A
We are seeking a PhD candidate to join us at Imperial College London. This position offers a unique opportunity to explore the cutting-edge intersection of neuroscience and artificial intelligence, with the broad goal to investigate shared principles of computation within both artificial and biological intelligent systems.
Eleonora Russo
One Ph.D. position is available within the National Ph.D. Program in ‘Theoretical and Applied Neuroscience’. The Ph.D. will be held in the Brain Dynamics Lab at the Biorobotics Institute of Sant'Anna School of Advanced Studies, Pisa (Italy) in collaboration with the Kelsch Group at the University Medical Center, Johannes Gutenberg University, Mainz (Germany). Understanding the dynamical systems governing neuronal activity is crucial for unraveling how the brain performs cognitive functions. Historically, various forms of recurrent neural networks (RNNs) have been proposed as simplified models of the cortex. Recently, due to remarkable advancements in machine learning, RNNs' ability to capture temporal dependencies has been used to develop tools for approximating unknown dynamical systems by training them on observed time-series data. This approach allows us to use time series of electrophysiological multi-single unit recordings as well as whole brain ultra-high field functional imaging (fMRI) to parametrize neuronal population dynamics and build functional models of cognitive functions. The objective of this research project is to investigate the neuronal mechanisms underlying the reinforcement and depreciation of perceived stimuli in the extended network of the mouse forebrain regions. The PhD student will carry out his/her/their studies primarily at the BioRobotics Institute of Sant'Anna School of Advanced Studies. The project will expose the student to a highly international and interdisciplinary context, in tight collaboration with theoretical and experimental neuroscientists in Italy and abroad. At the BioRobotics Institute, the research groups involved will be the Brain Dynamics Lab, the Computational Neuroengineering Lab, and the Bioelectronics and Bioengineering Area. Moreover, the project will be carried out in tight collaboration with the experimental group of Prof. Wolfgang Kelsch, Johannes Gutenberg University, Mainz, Germany. During the PhD, the student will have the opportunity to spend a period abroad.
Lorenzo Fontolan
We are pleased to announce the opening of a PhD position at INMED (Aix-Marseille University) through the SCHADOC program, focused on the neural coding of social interactions and memory in the cortex of behaving mice. The project will investigate how social behaviors essential for cooperation, mating, and group dynamics are encoded in the brain, and how these processes are disrupted in neurodevelopmental disorders such as autism. This project uses longitudinal calcium imaging and population-level data analysis to study how cortical circuits encode social interactions in mice. Recordings from mPFC and S1 in wild-type and Neurod2 KO mice will be used to extract neural representations of social memory. The candidate will develop and apply computational models of neural dynamics and representational geometry to uncover how these codes evolve over time and are disrupted in social amnesia.
Maths, AI and Neuroscience Meeting Stockholm
To understand brain function and develop artificial general intelligence it has become abundantly clear that there should be a close interaction among Neuroscience, machine learning and mathematics. There is a general hope that understanding the brain function will provide us with more powerful machine learning algorithms. On the other hand advances in machine learning are now providing the much needed tools to not only analyse brain activity data but also to design better experiments to expose brain function. Both neuroscience and machine learning explicitly or implicitly deal with high dimensional data and systems. Mathematics can provide powerful new tools to understand and quantify the dynamics of biological and artificial systems as they generate behavior that may be perceived as intelligent.
Neural Coding for Flexible Behavior in Prefrontal Cortex
Retinal responses to natural inputs
The research in my lab focuses on sensory signal processing, particularly in cases where sensory systems perform at or near the limits imposed by physics. Photon counting in the visual system is a beautiful example. At its peak sensitivity, the performance of the visual system is limited largely by the division of light into discrete photons. This observation has several implications for phototransduction and signal processing in the retina: rod photoreceptors must transduce single photon absorptions with high fidelity, single photon signals in photoreceptors, which are only 0.03 – 0.1 mV, must be reliably transmitted to second-order cells in the retina, and absorption of a single photon by a single rod must produce a noticeable change in the pattern of action potentials sent from the eye to the brain. My approach is to combine quantitative physiological experiments and theory to understand photon counting in terms of basic biophysical mechanisms. Fortunately there is more to visual perception than counting photons. The visual system is very adept at operating over a wide range of light intensities (about 12 orders of magnitude). Over most of this range, vision is mediated by cone photoreceptors. Thus adaptation is paramount to cone vision. Again one would like to understand quantitatively how the biophysical mechanisms involved in phototransduction, synaptic transmission, and neural coding contribute to adaptation.
Probabilistic computation in natural vision
A central goal of vision science is to understand the principles underlying the perception and neural coding of the complex visual environment of our everyday experience. In the visual cortex, foundational work with artificial stimuli, and more recent work combining natural images and deep convolutional neural networks, have revealed much about the tuning of cortical neurons to specific image features. However, a major limitation of this existing work is its focus on single-neuron response strength to isolated images. First, during natural vision, the inputs to cortical neurons are not isolated but rather embedded in a rich spatial and temporal context. Second, the full structure of population activity—including the substantial trial-to-trial variability that is shared among neurons—determines encoded information and, ultimately, perception. In the first part of this talk, I will argue for a normative approach to study encoding of natural images in primary visual cortex (V1), which combines a detailed understanding of the sensory inputs with a theory of how those inputs should be represented. Specifically, we hypothesize that V1 response structure serves to approximate a probabilistic representation optimized to the statistics of natural visual inputs, and that contextual modulation is an integral aspect of achieving this goal. I will present a concrete computational framework that instantiates this hypothesis, and data recorded using multielectrode arrays in macaque V1 to test its predictions. In the second part, I will discuss how we are leveraging this framework to develop deep probabilistic algorithms for natural image and video segmentation.
Design principles of adaptable neural codes
Behavior relies on the ability of sensory systems to infer changing properties of the environment from incoming sensory stimuli. However, the demands that detecting and adjusting to changes in the environment place on a sensory system often differ from the demands associated with performing a specific behavioral task. This necessitates neural coding strategies that can dynamically balance these conflicting needs. I will discuss our ongoing theoretical work to understand how this balance can best be achieved. We connect ideas from efficient coding and Bayesian inference to ask how sensory systems should dynamically allocate limited resources when the goal is to optimally infer changing latent states of the environment, rather than reconstruct incoming stimuli. We use these ideas to explore dynamic tradeoffs between the efficiency and speed of sensory adaptation schemes, and the downstream computations that these schemes might support. Finally, we derive families of codes that balance these competing objectives, and we demonstrate their close match to experimentally-observed neural dynamics during sensory adaptation. These results provide a unifying perspective on adaptive neural dynamics across a range of sensory systems, environments, and sensory tasks.
Natural switches in sensory attention rapidly modulate hippocampal spatial codes
During natural behavior animals dynamically switch between different behaviors, yet little is known about how the brain performs behavioral-switches. Navigation is a complex dynamic behavior that enables testing these kind of behavioral switches: It requires the animal to know its own allocentric (world-centered) location within the environment, while also paying attention to incoming sudden events such as obstacles or other conspecifics – and therefore the animal may need to rapidly switch from representing its own allocentric position to egocentrically representing ‘things out-there’. Here we used an ethological task where two bats flew together in a very large environment (130 meters), and had to switch between two behaviors: (i) navigation, and (ii) obstacle-avoidance during ‘cross-over’ events with the other bat. Bats increased their echolocation click-rate before a cross-over, indicating spatial attention to the other bat. Hippocampal CA1 neurons represented the bat’s own position when flying alone (allocentric place-coding); surprisingly, when meeting the other bat, neurons switched very rapidly to jointly representing the inter-bat distance × position (egocentric × allocentric coding). This switching to a neuronal representation of the other bat was correlated on a trial-by-trial basis with the attention signal, as indexed by the bat’s echolocation calls – suggesting that sensory attention is controlling these major switches in neural coding. Interestingly, we found that in place-cells, the different place-fields of the same neuron could exhibit very different tuning to inter-bat distance – creating a non-separable coding of allocentric position × egocentric distance. Together, our results suggest that attentional switches during navigation – which in bats can be measured directly based on their echolocation signals – elicit rapid dynamics of hippocampal spatial coding. More broadly, this study demonstrates that during natural behavior, when animals often switch between different behaviors, neural circuits can rapidly and flexibly switch their core computations.
Design principles of adaptable neural codes
Behavior relies on the ability of sensory systems to infer changing properties of the environment from incoming sensory stimuli. However, the demands that detecting and adjusting to changes in the environment place on a sensory system often differ from the demands associated with performing a specific behavioral task. This necessitates neural coding strategies that can dynamically balance these conflicting needs. I will discuss our ongoing theoretical work to understand how this balance can best be achieved. We connect ideas from efficient coding and Bayesian inference to ask how sensory systems should dynamically allocate limited resources when the goal is to optimally infer changing latent states of the environment, rather than reconstruct incoming stimuli. We use these ideas to explore dynamic tradeoffs between the efficiency and speed of sensory adaptation schemes, and the downstream computations that these schemes might support. Finally, we derive families of codes that balance these competing objectives, and we demonstrate their close match to experimentally-observed neural dynamics during sensory adaptation. These results provide a unifying perspective on adaptive neural dynamics across a range of sensory systems, environments, and sensory tasks.
Herbert Jasper Lecture
There is a long-standing tension between the notion that the hippocampal formation is essentially a spatial mapping system, and the notion that it plays an essential role in the establishment of episodic memory and the consolidation of such memory into structured knowledge about the world. One theory that resolves this tension is the notion that the hippocampus generates rather arbitrary 'index' codes that serve initially to link attributes of episodic memories that are stored in widely dispersed and only weakly connected neocortical modules. I will show how an essentially 'spatial' coding mechanism, with some tweaks, provides an ideal indexing system and discuss the neural coding strategies that the hippocampus apparently uses to overcome some biological constraints affecting the possibility of shipping the index code out widely to the neocortex. Finally, I will present new data suggesting that the hippocampal index code is indeed transferred to layer II-III of the neocortex.
Restless engrams: the origin of continually reconfiguring neural representations
During learning, populations of neurons alter their connectivity and activity patterns, enabling the brain to construct a model of the external world. Conventional wisdom holds that the durability of a such a model is reflected in the stability of neural responses and the stability of synaptic connections that form memory engrams. However, recent experimental findings have challenged this idea, revealing that neural population activity in circuits involved in sensory perception, motor planning and spatial memory continually change over time during familiar behavioural tasks. This continual change suggests significant redundancy in neural representations, with many circuit configurations providing equivalent function. I will describe recent work that explores the consequences of such redundancy for learning and for task representation. Despite large changes in neural activity, we find cortical responses in sensorimotor tasks admit a relatively stable readout at the population level. Furthermore, we find that redundancy in circuit connectivity can make a task easier to learn and compensate for deficiencies in biological learning rules. Finally, if neuronal connections are subject to an unavoidable level of turnover, the level of plasticity required to optimally maintain a memory is generally lower than the total change due to turnover itself, predicting continual reconfiguration of an engram.
Inhibitory neural circuit mechanisms underlying neural coding of sensory information in the neocortex
Neural codes, such as temporal codes (precisely timed spikes) and rate codes (instantaneous spike firing rates), are believed to be used in encoding sensory information into spike trains of cortical neurons. Temporal and rate codes co-exist in the spike train and such multiplexed neural code-carrying spike trains have been shown to be spatially synchronized in multiple neurons across different cortical layers during sensory information processing. Inhibition is suggested to promote such synchronization, but it is unclear whether distinct subtypes of interneurons make different contributions in the synchronization of multiplexed neural codes. To test this, in vivo single-unit recordings from barrel cortex were combined with optogenetic manipulations to determine the contributions of parvalbumin (PV)- and somatostatin (SST)-positive interneurons to synchronization of precisely timed spike sequences. We found that PV interneurons preferentially promote the synchronization of spike times when instantaneous firing rates are low (<12 Hz), whereas SST interneurons preferentially promote the synchronization of spike times when instantaneous firing rates are high (>12 Hz). Furthermore, using a computational model, we demonstrate that these effects can be explained by PV and SST interneurons having preferential contribution to feedforward and feedback inhibition, respectively. Overall, these results show that PV and SST interneurons have distinct frequency (rate code)-selective roles in dynamically gating the synchronization of spike times (temporal code) through preferentially recruiting feedforward and feedback inhibitory circuit motifs. The inhibitory neural circuit mechanisms we uncovered here his may have critical roles in regulating neural code-based somatosensory information processing in the neocortex.
Multitask performance humans and deep neural networks
Humans and other primates exhibit rich and versatile behaviour, switching nimbly between tasks as the environmental context requires. I will discuss the neural coding patterns that make this possible in humans and deep networks. First, using deep network simulations, I will characterise two distinct solutions to task acquisition (“lazy” and “rich” learning) which trade off learning speed for robustness, and depend on the initial weights scale and network sparsity. I will chart the predictions of these two schemes for a context-dependent decision-making task, showing that the rich solution is to project task representations onto orthogonal planes on a low-dimensional embedding space. Using behavioural testing and functional neuroimaging in humans, we observe BOLD signals in human prefrontal cortex whose dimensionality and neural geometry are consistent with the rich learning regime. Next, I will discuss the problem of continual learning, showing that behaviourally, humans (unlike vanilla neural networks) learn more effectively when conditions are blocked than interleaved. I will show how this counterintuitive pattern of behaviour can be recreated in neural networks by assuming that information is normalised and temporally clustered (via Hebbian learning) alongside supervised training. Together, this work offers a picture of how humans learn to partition knowledge in the service of structured behaviour, and offers a roadmap for building neural networks that adopt similar principles in the service of multitask learning. This is work with Andrew Saxe, Timo Flesch, David Nagy, and others.
Functional and structural loci of individuality in the Drosophila olfactory circuit
Behavior varies even among genetically identical animals raised in the same environment. However, little is known about the circuit or anatomical underpinnings of this individuality, though previous work implicates sensory periphery. Drosophila olfaction presents an ideal model to study the biological basis of behavioral individuality, because while the neural circuit underlying olfactory behavior is well-described and highly stereotyped, persistent idiosyncrasy in behavior, neural coding, and neural wiring have also been described. Projection neurons (PNs), which relay odor signals sensed by olfactory receptor neurons (ORNs) to deeper brain structures, exhibit variable calcium responses to identical odor stimuli across individuals, but how these idiosyncrasies relate to individual behavioral responses remains unknown. Here, using paired behavior and two-photon imaging measurements, we show that idiosyncratic calcium dynamics in both ORNs and PNs predict individual preferences for an aversive monomolecular odorant versus air, suggesting that variation at the periphery of the olfactory system determines individual preference for an odor’s presence. In contrast, PN, but not ORN, calcium responses predict individual preferences in a two-odor choice assay. Furthermore, paired behavior and immunohistochemistry measurements reveal that variation in ORN presynaptic density also predicts two-odor preference, suggesting this site is a locus of individuality where microscale circuit variation gives rise to idiosyncrasy in behavior. Our results demonstrate how a neural circuit may vary functionally and structurally to produce variable behavior among individuals.
On the purpose and origin of spontaneous neural activity
Spontaneous firing, observed in many neurons, is often attributed to ion channel or network level noise. Cortical cells during slow wave sleep exhibit transitions between so called Up and Down states. In this sleep state, with limited sensory stimuli, neurons fire in the Up state. Spontaneous firing is also observed in slices of cholinergic interneurons, cerebellar Purkinje cells and even brainstem inspiratory neurons. In such in vitro preparations, where the functional relevance is long lost, neurons continue to display a rich repertoire of firing properties. It is perplexing that these neurons, instead of saving their energy during information downtime and functional irrelevance, are eager to fire. We propose that spontaneous firing is not a chance event but instead, a vital activity for the well-being of a neuron. We postulate that neurons, in anticipation of synaptic inputs, keep their ATP levels at maximum. As recovery from inputs requires most of the energy resources, neurons are ATP surplus and ADP scarce during synaptic quiescence. With ADP as the rate-limiting step, ATP production stalls in the mitochondria when ADP is low. This leads to toxic Reactive Oxygen Species (ROS) formation, which are known to disrupt many cellular processes. We hypothesize that spontaneous firing occurs at these conditions - as a release valve to spend energy and to restore ATP production, shielding the neuron against ROS. By linking a mitochondrial metabolism model to a conductance-based neuron model, we show that spontaneous firing depends on baseline ATP usage and on ATP-cost-per-spike. From our model, emerges a mitochondrial mediated homeostatic mechanism that provides a recipe for different firing patterns. Our findings, though mostly affecting intracellular dynamics, may have large knock-on effects on the nature of neural coding. Hitherto it has been thought that the neural code is optimised for energy minimisation, but this may be true only when neurons do not experience synaptic quiescence.
Neural coding in the auditory cortex - "Emergent Scientists Seminar Series
Dr Jennifer Lawlor Title: Tracking changes in complex auditory scenes along the cortical pathway Complex acoustic environments, such as a busy street, are characterised by their everchanging dynamics. Despite their complexity, listeners can readily tease apart relevant changes from irrelevant variations. This requires continuously tracking the appropriate sensory evidence while discarding noisy acoustic variations. Despite the apparent simplicity of this perceptual phenomenon, the neural basis of the extraction of relevant information in complex continuous streams for goal-directed behavior is currently not well understood. As a minimalistic model for change detection in complex auditory environments, we designed broad-range tone clouds whose first-order statistics change at a random time. Subjects (humans or ferrets) were trained to detect these changes.They were faced with the dual-task of estimating the baseline statistics and detecting a potential change in those statistics at any moment. To characterize the extraction and encoding of relevant sensory information along the cortical hierarchy, we first recorded the brain electrical activity of human subjects engaged in this task using electroencephalography. Human performance and reaction times improved with longer pre-change exposure, consistent with improved estimation of baseline statistics. Change-locked and decision-related EEG responses were found in a centro-parietal scalp location, whose slope depended on change size, consistent with sensory evidence accumulation. To further this investigation, we performed a series of electrophysiological recordings in the primary auditory cortex (A1), secondary auditory cortex (PEG) and frontal cortex (FC) of the fully trained behaving ferret. A1 neurons exhibited strong onset responses and change-related discharges specific to neuronal tuning. PEG population showed reduced onset-related responses, but more categorical change-related modulations. Finally, a subset of FC neurons (dlPFC/premotor) presented a generalized response to all change-related events only during behavior. We show using a Generalized Linear Model (GLM) that the same subpopulation in FC encodes sensory and decision signals, suggesting that FC neurons could operate conversion of sensory evidence to perceptual decision. All together, these area-specific responses suggest a behavior-dependent mechanism of sensory extraction and generalization of task-relevant event. Aleksandar Ivanov Title: How does the auditory system adapt to different environments: A song of echoes and adaptation
Cholinergic regulation of learning in the olfactory system
In the olfactory system, cholinergic modulation has been associated with contrast modulation and changes in receptive fields in the olfactory bulb, as well the learning of odor associations in the olfactory cortex. Computational modeling and behavioral studies suggest that cholinergic modulation could improve sensory processing and learning while preventing pro-active interference when task demands are high. However, how sensory inputs and/or learning regulate incoming modulation has not yet been elucidated. We here use a computational model of the olfactory bulb, piriform cortex (PC) and horizontal limb of the diagonal band of Broca (HDB) to explore how olfactory learning could regulate cholinergic inputs to the system in a closed feedback loop. In our model, the novelty of an odor is reflected in firing rates and sparseness of cortical neurons in response to that odor and these firing rates can directly regulate learning in the system by modifying cholinergic inputs to the system.
Functional and structural loci of individuality in the Drosophila olfactory circuit
behaviour varies even among genetically identical animals raised in the same environment. However, little is known about the circuit or anatomical underpinnings of this individuality, though previous work implicates sensory periphery. Drosophila olfaction presents an ideal model to study the biological basis of behavioural individuality, because while the neural circuit underlying olfactory behaviour is well-described and highly stereotyped, persistent idiosyncrasy in behaviour, neural coding, and neural wiring have also been described. Projection neurons (PNs), which relay odor signals sensed by olfactory receptor neurons (ORNs) to deeper brain structures, exhibit variable calcium responses to identical odor stimuli across individuals, but how these idiosyncrasies relate to individual behavioural responses remains unknown. Here, using paired behaviour and two-photon imaging measurements, we show that idiosyncratic calcium dynamics in both ORNs and PNs predict individual preferences for an aversive monomolecular odorant versus air, suggesting that variation at the periphery of the olfactory system determines individual preference for an odor’s presence. In contrast, PN, but not ORN, calcium responses predict individual preferences in a two-odor choice assay. Furthermore, paired behaviour and immunohistochemistry measurements reveal that variation in ORN presynaptic density also predicts two-odor preference, suggesting this site is a locus of individuality where microscale circuit variation gives rise to idiosyncrasy in behaviour. Our results demonstrate how a neural circuit may vary functionally and structurally to produce variable behaviour among individuals.
Rational thoughts in neural codes
First, we describe a new method for inferring the mental model of an animal performing a natural task. We use probabilistic methods to compute the most likely mental model based on an animal’s sensory observations and actions. This also reveals dynamic beliefs that would be optimal according to the animal’s internal model, and thus provides a practical notion of “rational thoughts.” Second, we construct a neural coding framework by which these rational thoughts, their computational dynamics, and actions can be identified within the manifold of neural activity. We illustrate the value of this approach by training an artificial neural network to perform a generalization of a widely used foraging task. We analyze the network’s behaviour to find rational thoughts, and successfully recover the neural properties that implemented those thoughts, providing a way of interpreting the complex neural dynamics of the artificial brain. Joint work with Zhengwei Wu, Minhae Kwon, Saurabh Daptardar, and Paul Schrater.
Goal-directed remapping of enthorhinal cortex neural coding
COSYNE 2022
Goal-directed remapping of enthorhinal cortex neural coding
COSYNE 2022
Novelty modulates neural coding and reveals functional diversity within excitatory and inhibitory populations in the visual cortex
COSYNE 2022
Novelty modulates neural coding and reveals functional diversity within excitatory and inhibitory populations in the visual cortex
COSYNE 2022
Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders
COSYNE 2023
Context-dependent neural coding of utility in the frontal cortex of rats
COSYNE 2025
Dopamine controls neural coding of anxiety and valence in the mouse anterior insula
COSYNE 2025
The tilt illusion arises from an efficient reallocation of neural coding resources at the contextual boundary
COSYNE 2025
Neural coding of space and goals: Dynamics of egocentric boundary tuning during bait-chasing
FENS Forum 2024
Reduced dynamic range and impaired neural coding in 5xFAD mice
FENS Forum 2024