2-year 100% research associate position in developmental cognitive neuroscience and EEG research at TU Dresden, Germany At the Faculty of Psychology, the Chair of Lifespan Developmental Neuroscience offers a position as Research Associate (m/f/x) (subject to personal qualification employees are remunerated according to salary group E 13 TV-L), starting as soon as possible. The position is initially limited until September 30, 2025 with the option for extension. The period of employment is governed by the Fixed Term Research Contracts Act (Wissenschaftszeitvertragsgesetz - WissZeitVG). The position offers the chance to obtain further academic qualification. The Chair of Lifespan Developmental Neuroscience investigates neurocognitive mechanisms underlying perceptual, cognitive, and motivational development across the lifespan. The main themes of our research are neurofunctional mechanisms underlying lifespan development of memory, cognitive control, reward processing, decision making, and multisensory perception. We also pursue applied research to study effects of behavioral intervention, non-invasive brain stimulation, or digital technologies in enhancing functional plasticity for individuals of difference ages. We utilize a broad range of neurocognitive (e.g., EEG, fNIRs, fMRI, tDCS) and computational methods. The lab has several testing rooms and is equipped with multiple EEG (64-channel and 32-channel) and fNIRs systems, as well as eye-tracking and virtual-reality devices. The MRI scanner (3T) and TMS-device can be accessed through the university’s NeuroImaging Center. TUD is a university of excellence supported by the DFG, which offers outstanding research opportunities. Researchers in this chair are involved in large research consortium and cluster, such as the DFG SFB 940 „Volition and Cognitive Control“ and DFG EXC 2050 „Tactile Internet with Human-in-the-Loop“. Tasks: research in the field of lifespan developmental cognitive neuroscience. The research topics are subject to the fits between the candidate’s research interests, expertise, and ongoing projects in the chair, particularly the DFG-funded research project Tec4Tic; scientific teaching (1 bachelor- or master-level seminar per semester for students majoring psychology). Topics for the seminars should cover neurocognitive mechanism of cognitive, motivation, or perceptual development.

We are looking for a motivated and talented scientist to join the Neuronal Circuits for Memory and Perception - Pardi lab, at the Institute of Psychiatry and Neuroscience of Paris (IPNP) as a funded postdoc from ~mid 2023. The Pardi lab aims to understand how brain circuits use internal information to transform sensations into perceptual representations, and how this process is affected in psychiatric disorders. The successful applicant will investigate the function of auditory thalamo-cortical loops in these processes, by combining mouse behavior with in vivo 2-photon calcium imaging, in vitro electrophysiology and optogenetics.

We are looking for a motivated and talented scientist to join the Neuronal Circuits for Memory and Perception - Pardi lab, at the Institute of Psychiatry and Neuroscience of Paris (IPNP), as a 2-year funded postdoc, from ~mid 2023, with the possibility of extension. The Pardi lab aims to understand how brain circuits use internal information to transform sensations into perceptual representations, and how this process is affected in psychiatric disorders. The successful applicant will investigate the function of auditory thalamo-cortical loops in these processes, by combining mouse behavior with in vivo 2-photon calcium imaging, in vitro electrophysiology and optogenetics.

The Frank Lab at the University of California, San Francisco is looking for a Junior Specialist Technician to begin work January 2021 or later. This is a full-time paid position with a two-year minimum commitment required. During this time, the technician will work directly with a postdoctoral fellow and may also contribute to other lab projects as time allows. The lab investigates the neural underpinnings of learning and memory by collecting in vivo electrophysiological recordings from the hippocampus of rats while they learn and perform complex, memory-dependent behaviors. We have developed cutting-edge decoding algorithms to capture neural representations of spatial location as rats navigate an environment. The specific project aims to measure how such spatial representations are altered in aged rats compared to young rats and assess whether changes in spatial representation might drive changes in performance of a memory-dependent task. Please reach out to Anna Gillespie (postdoc) if interested. Responsibilities include: Handling and behavioral training of rats Construction of microelectrode drives Participation in rat implant surgeries Development of behavioral and neural data analyses Collection of large scale electrophysiological and behavioral datasets

Projects in the lab aim to discover biomarkers of sleep oscillations that correlate with memory consolidation and sleep quality. Sleep disruption is a common symptom of neurodegenerative disorders and is thought to be linked to their progression. Thalamocortical activity during sleep is critical for the contribution of sleep to memory consolidation, but it is not clear what oscillatory and cellular activity patterns relate to sleep quality and memory consolidation. The candidate will assist with administrative and scientific aspects of this project, using rats to investigate the patterns of thalamic activity that promote healthy sleep function. More generally, the lab uses state-of-the-art techniques to investigate the neural network mechanisms of cognitive behavior, with a focus on learning and memory and on the role of the neuronal circuits formed by the thalamus.

The goal of this project is to investigate biomarkers of cellular activity in the thalamus that correlate with sleep depth and stability. Sleep disruption is a common symptom of neurodegenerative disorders and is thought to be linked to their progression. The thalamus is a critical structure in the maintenance and microarchitecture of sleep, but it is unclear how cellular activity in the thalamus relates to sleep structure. We use rats to investigate the patterns of thalamic cell activity that promote healthy sleep function. More generally, the lab uses state-of-the-art techniques to investigate the mechanisms of cognitive behavior, with a focus on learning and memory and on the role of the neuronal circuits formed by the thalamus.

We are recruiting lab personnel. If systems neuroscience at the intersection of olfaction and memory excites you, now is an excellent time to get in touch. Our goal is to discover fundamental rules and mechanisms that govern information storage and retrieval in neural systems. Our primary focus will be establishing the changes in neural circuit and population dynamics that correspond to odor recognition memory. To bring our understanding of this process to a new level of rigor we will apply quantitative statistical approaches to relate behavioral signatures of odor recognition to activity and plasticity in olfactory circuits. We will use in vivo electrophysiology and calcium imaging to capture the activity of large neural populations during olfactory experience, and we will apply cell-type specific perturbations of activity and plasticity to piece apart how specific circuit connections contribute.

An exciting opportunity has arisen for an experienced Researcher to make a leading contribution to a project on “Modulating sleep with learning to enhance learning”, joining the laboratory of Professor Edwin M. Robertson within the Institute of Neuroscience & Psychology. This group examines the architecture of human memory. We integrate together a variety of cutting edge techniques including behavioural analysis, functional imaging and brain stimulation. Together, these are used to provide a picture of how the content and structure of a memory determines its fate (retained or enhanced) across different brain states (sleep vs. wakefulness). Currently, there is an opening in our group funded by the Leverhulme Trust (UK). It would suit a bright, enthusiastic, aspiring researcher willing to think carefully, creatively, critically and collaboratively (with the Principal Investigator) about their work in this project on human neuroscience. The group provides a superb training environment, with many using it as a foundation to secure independent fellowships, and faculty positions. The laboratory is housed within the Institute of Neuroscience & Psychology (INP), which is home to several Wellcome Trust Investigators, and national academy members (Royal Society, Edinburgh).

The mission of the Donato lab is to understand the underlying principles that drive the assembly and function of neuronal circuits for navigation and memory. To reach our aims, we use a vast array of cutting-edge techniques, like the ultrasound-guided injection of viral vectors for neural circuit tracing, calcium imaging, single-unit recordings, opto and chemogenetics, coupled to a quantitative approach for the study of mouse behavior and advanced computational approaches for the analysis of big datasets. By these means, we are able to follow the activity of large populations of neurons longitudinally, from infancy to adulthood, to understand how cognition arises in the mammalian brain. For more information, please visit our lab websites at www.donatolab.com , and https://www.biozentrum.unibas.ch/research/researchgroups/overview/unit/donato.

The mission of the Donato lab is to understand the underlying principles that drive the assembly and function of neuronal circuits for navigation and memory. To reach our aims, we use a vast array of cutting-edge techniques, like the ultrasound-guided injection of viral vectors for neural circuit tracing, calcium imaging, single-unit recordings, opto and chemogenetics, coupled to a quantitative approach for the study of mouse behavior and advanced computational approaches for the analysis of big datasets. By these means, we are able to follow the activity of large populations of neurons longitudinally, from infancy to adulthood, to understand how cognition arises in the mammalian brain. For more information, please visit our lab websites at www.donatolab.com , and https://www.biozentrum.unibas.ch/research/researchgroups/overview/unit/donato.

Three PhD students funded by BBSRC MIBTP. Please find more information on https://sites.google.com/site/jiankliu/join-us 1. Towards a functional model for associative learning and memory formation Drs Jian Liu and Rodrigo Quian Quiroga, CSN/NPB, University of Leicester 2. Neuronal coupling across spatiotemporal scales and dimensions of cortical population activity Drs Michael Okun and Jian Liu, CSN/NPB, University of Leicester 3. Decoding movement from single neurons in motor cortex and their subcortical targets Drs Todor Gerdjikov and Jian Liu, CSN/NPB, University of Leicester

Several postdoctoral openings in the lab of Rava Azeredo da Silveira (Paris & Basel) The lab of Rava Azeredo da Silveira invites applications for Postdoctoral Researcher positions at ENS, Paris, and IOB, an associated institute of the University of Basel. Research questions will be chosen from a broad range of topics in theoretical/computational neuroscience and cognitive science (see the description of the lab’s activity, below). One of the postdoc positions to be filled in Basel will be part of a collaborative framework with Michael Woodford (Columbia University) and will involve projects relating the study of decision making to models of perception and memory. Candidates with backgrounds in mathematics, statistics, artificial intelligence, physics, computer science, engineering, biology, and psychology are welcome. Experience with data analysis and proficiency with numerical methods, in addition to familiarity with neuroscience topics and mathematical and statistical methods, are desirable. Equally desirable are a spirit of intellectual adventure, eagerness, and drive. The positions will come with highly competitive work conditions and salaries. Application deadline: Applications will be reviewed starting on 1 November 2020. How to apply: Please send the following information in one single PDF, to silveira@iob.ch: 1. letter of motivation; 2. statement of research interests, limited to two pages; 3. curriculum vitæ including a list of publications; 4. any relevant publications that you wish to showcase. In addition, please arrange for three letters of recommendations to be sent to the same email address. In all email correspondence, please include the mention “APPLICATION-POSTDOC” in the subject header, otherwise the application will not be considered. * ENS, together with a number of neighboring institutions (College de France, Institut Curie, ESPCI, Sorbonne Université, and Institut Pasteur), offers a rich scientific and intellectual environment, with a strong representation in computational neuroscience and related fields. * IOB is a research institute combining basic and clinical research. Its mission is to drive innovations in understanding vision and its diseases and develop new therapies for vision loss. IOB is an equal-opportunity employer with family-friendly work policies. * The Silveira Lab focuses on a range of topics, which, however, are tied together through a central question: How does the brain represent and manipulate information? Among the more concrete approaches to this question, the lab analyses and models neural activity in circuits that can be identified, recorded from, and perturbed experimentally, such as visual neural circuits in the retina and the cortex. Establishing links between physiological specificity and the structure of neural activity yields an understanding of circuits as building blocks of cerebral information processing. On a more abstract level, the lab investigates the representation of information in populations of neurons, from a statistical and algorithmic—rather than mechanistic—point of view, through theories of coding and data analyses. These studies aim at understanding the statistical nature of high-dimensional neural activity in different conditions, and how this serves to encode and process information from the sensory world. In the context of cognitive studies, the lab investigates mental processes such as inference, learning, and decision-making, through both theoretical developments and behavioral experiments. A particular focus is the study of neural constraints and limitations and, further, their impact on mental processes. Neural limitations impinge on the structure and variability of mental representations, which in turn inform the cognitive algorithms that produce behavior. The lab explores the nature of neural limitations, mental representations, and cognitive algorithms, and their interrelations.

Gain expertise in rodent electrophysiology and behavior studying thalamic cellular and network mechanisms of sleep and memory consolidation. We have several openings to study the mechanisms of synaptic plasticity and cellular spike dynamics that contribute to episodic memory consolidation during sleep. Trainees will gain expertise in systems neuroscience using electrophysiology (cell ensemble and LFP recording) and behavior in rats, as well as expertise on the thalamic molecular and cellular mechanisms underlying normal and disrupted sleep-dependent memory consolidation and the use of non-invasive technologies to regulate them. Some of the projects are part of collaborations with Harvard University and the Scripps Florida Institute.

The research group uses diverse computational modeling approaches, including biological neural networks, cognitive modeling, and machine learning/artificial intelligence, to study learning and memory. The selected candidate will expand the computational modeling framework Cobel-RL and use it to study how episodic memory might be used to learn to navigate.

We are seeking an outstanding researcher with expertise in computational or mathematical psychology to join the Complex Human Data Hub and contribute to the school’s research and teaching program. The CHDH has areas of strength in memory, perception, categorization, decision-making, language, cultural evolution, and social network analysis. We welcome applicants from all areas of mathematical psychology, computational cognitive science, computational behavioural science and computational social science and are especially interested in applicants who can build upon or complement our existing strengths. We particularly encourage applicants whose theoretical approaches and methodologies connect with social network processes and/or culture and cognition, or whose work links individual psychological processes to broader societal processes. We especially encourage women and other minorities to apply.

The project will focus on the computational investigation of the role of neural reactivations in memory. Since their discovery neural reactivations happening during sleep have emerged as an exceptional tool to investigate the process of memory formation in the brain. This phenomenon has been mostly associated with the hippocampus, an area known for its role in the processing of new memories and their initial storage. Continuous advancements in data acquisition techniques are giving us an unprecedented access to the activity of large-scale networks during sleep, in the hippocampus and in other cortical regions. At the same time, our theoretical understanding of the computations underlying neural reactivations and more in general memory representations, has only began to take shape. Combining mathematical modeling of neural networks and analysis of existing dataset, we will address some key aspects of this phenomenon such as: 1) The role of different sleep phases in regulating the reactivation process and in modulating the evolution of a memory trace. 2) The relationship of hippocampal reactivations to the process of (semantic) learning and knowledge generalization. 3) The relevance of reactivation statistical properties for learning in cortico-hippocampal networks.

Doctoral Position in Computational Neuroscience. Are you curious about how the human brain stores memories? Have you wondered how we manage to navigate through space? Our dynamic research group uses diverse computational modeling approaches, including biological neural networks, cognitive modeling, and machine learning/artificial intelligence, to study learning and memory. Currently, we are actively seeking a talented graduate student to join our team, someone who will expand our computational modeling framework Cobel-Spike and use it to study how spiking neural networks can learn to navigate. This position is 65% at TV-L E13, starts as soon as possible, and is funded for 3 years.

The primary focus of this position is to work on an exciting collaborative project of decoding spontaneous thoughts. The intended project focuses on understanding the contents and dynamics of spontaneous thoughts using fMRI decoding and natural tasks, their interaction with memory and emotion, and rumination in mental disorders. The candidate will have the opportunity to analyze a rich fMRI dataset of healthy and clinical participants during spontaneous thoughts, and conduct new experiments.

The Cognitive and Behavioral Neuroscience Division at Department of Psychology, University of Miami seeks highly motivated and creative Ph.D. students in our efforts to understand the brain and mind. Applications for entry in the Fall of 2025 are now being accepted, with a deadline of December 1st. For details, including contact information, please visit https://www.psy.miami.edu/graduate/how-to-apply/index.html. The Cognitive and Behavioral Neuroscience Division at Department of Psychology, University of Miami offers a unique program of study spanning neurobiology, behavior, computational and brain imaging research on topics of emotion, mindfulness, learning and memory, mental disorders and health. A listing of faculty affiliated with the division can be found online at https://www.psy.miami.edu/research/faculty-research/index.html and below.

The Department of Psychology at The Pennsylvania State University, University Park, PA, invites applications for a full-time Assistant or Associate Professor of Cognitive Psychology with anticipated start date of August, 2025. Areas of specialization within cognitive psychology are open and may include (but are not limited to) such topics as cognitive control, creativity, computational approaches and modelling, motor control, language science, memory, attention, perception, and decision making. A record of collaboration is desirable for both ranks. Substantial collaboration opportunities exist within the department that align with the department’s cross-cutting research themes and across campus. Current faculty in the cognitive area are active in units including the Center for Language Sciences, the Social Life and Engineering Sciences Imaging Center, the Center for Healthy Aging, the Center for Brain, Behavior, and Cognition and the Applied Research Lab. Responsibilities of the Assistant or Associate Professor of Cognitive Psychology include maintaining a strong record of publications in top outlets. This position will include resident instruction at the undergraduate and graduate level and normal university service, based on the candidate’s qualifications. A Ph.D. in Psychology or related field is required by the appointment date for both ranks. Candidates for the tenure-track Assistant Professor of Cognitive Psychology position must have demonstrated ability as a researcher, scholar, and teacher in a relevant field and have evidence of growth in scholarly achievement. Duties will involve a combination of teaching, research, and service, based on the candidate’s qualifications. Candidates for the tenure-track Associate Professor of Cognitive Psychology position must have demonstrated excellence as a researcher, scholar, and teacher in a relevant field and have an established reputation in scholarly achievement. Duties will involve a combination of teaching, research, and service, based on the candidate’s qualifications. The ideal candidate will have a strong record of publications in top outlets and have a history of or potential for external funding. In addition, successful candidates must either have demonstrated a commitment to building an inclusive, equitable, and diverse campus community, or describe one or more ways they would envision doing so, given the opportunity. Review of applications will begin immediately and will continue until the position is filled. Interested candidates should submit an online application at Penn State’s Job Posting Board, and should upload the following application materials electronically: (1) a Cover letter of application, (2) Concise statements of research and teaching interests, (3) a CV and (4) three selected (re)prints. System limitations allow for a total of 5 documents (5mb per document) as part of your application. Please combine materials to meet the 5-document limit. In addition, please arrange to have three letters of recommendation sent electronically to PsychApplications@psu.edu with the subject line: “Cognitive Psychology” Questions regarding the application process can be emailed to PsychApplications@psu.edu and questions regarding the position can be sent to the search chair: cogsearch@psu.edu. The Pennsylvania State University is committed to and accountable for advancing diversity, equity, and inclusion in all of its forms. We embrace individual uniqueness, foster a culture of inclusion that supports both broad and specific diversity initiatives, leverage the educational and institutional benefits of diversity, and engage all individuals to help them thrive. We value inclusion as a core strength and an essential element of our public service mission. Penn State offers competitive benefits to full-time employees, including medical, dental, vision, and retirement plans, in addition to 75% tuition discounts (including for a spouse and dependent children up to the age of 26) and paid holidays.

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.

Accurate perception of the environment is a constructive process that requires integration of external bottom-up sensory signals with internally-generated top-down information reflecting past experiences and current aims. Decades of work have elucidated how sensory neocortex processes physical stimulus features. In contrast, examining how memory-related-top-down information is encoded and integrated with bottom-up signals has long been challenging. Here, I will discuss our recent work pinpointing the outermost layer 1 of neocortex as a central hotspot for processing of experience-dependent top-down information threat during perception, one of the most fundamentally important forms of sensation.

Thalamic networks, at the core of thalamocortical and thalamosubcortical communications, underlie processes of perception, attention, memory, emotions, and the sleep-wake cycle, and are disrupted in mental disorders, including schizophrenia and autism. However, the underlying mechanisms of pathology are unknown. I will present novel evidence on key organizational principles, structural, and molecular features of thalamocortical networks, as well as critical thalamic pathway interactions that are likely affected in disorders. This data can facilitate modeling typical and abnormal brain function and can provide the foundation to understand heterogeneous disruption of these networks in sleep disorders, attention deficits, and cognitive and affective impairments in schizophrenia and autism, with important implications for the design of targeted therapeutic interventions

A combinatorial neural code for long-term motor memory

In this talk, I will discuss the OpenNeuro Fitlins GLM package and provide an illustration of the analytic workflow. OpenNeuro FitLins GLM is a semi-automated pipeline that reduces barriers to analyzing task-based fMRI data from OpenNeuro's 600+ task datasets. Created for psychology, psychiatry and cognitive neuroscience researchers without extensive computational expertise, this tool automates what is largely a manual process and compilation of in-house scripts for data retrieval, validation, quality control, statistical modeling and reporting that, in some cases, may require weeks of effort. The workflow abides by open-science practices, enhancing reproducibility and incorporates community feedback for model improvement. The pipeline integrates BIDS-compliant datasets and fMRIPrep preprocessed derivatives, and dynamically creates BIDS Statistical Model specifications (with Fitlins) to perform common mass univariate [GLM] analyses. To enhance and standardize reporting, it generates comprehensive reports which includes design matrices, statistical maps and COBIDAS-aligned reporting that is fully reproducible from the model specifications and derivatives. OpenNeuro Fitlins GLM has been tested on over 30 datasets spanning 50+ unique fMRI tasks (e.g., working memory, social processing, emotion regulation, decision-making, motor paradigms), reducing analysis times from weeks to hours when using high-performance computers, thereby enabling researchers to conduct robust single-study, meta- and mega-analyses of task fMRI data with significantly improved accessibility, standardized reporting and reproducibility.

Sleep is an active state critical for processing emotional memories encoded during waking in both humans and animals. There is a remarkable overlap between the brain structures and circuits active during sleep, particularly rapid eye-movement (REM) sleep, and the those encoding emotions. Accordingly, disruptions in sleep quality or quantity, including REM sleep, are often associated with, and precede the onset of, nearly all affective psychiatric and mood disorders. In this context, a major biomedical challenge is to better understand the underlying mechanisms of the relationship between (REM) sleep and emotion encoding to improve treatments for mental health. This lecture will summarize our investigation of the cellular and circuit mechanisms underlying sleep architecture, sleep oscillations, and local brain dynamics across sleep-wake states using electrophysiological recordings combined with single-cell calcium imaging or optogenetics. The presentation will detail the discovery of a 'somato-dendritic decoupling'in prefrontal cortex pyramidal neurons underlying REM sleep-dependent stabilization of optimal emotional memory traces. This decoupling reflects a tonic inhibition at the somas of pyramidal cells, occurring simultaneously with a selective disinhibition of their dendritic arbors selectively during REM sleep. Recent findings on REM sleep-dependent subcortical inputs and neuromodulation of this decoupling will be discussed in the context of synaptic plasticity and the optimization of emotional responses in the maintenance of mental health.

Synaptic architecture of a memory engram in the mouse hippocampus

The human medial temporal lobe contains neurons that respond selectively to the semantic contents of a presented stimulus. These "concept cells" may respond to very different pictures of a given person and even to their written or spoken name. Their response latency is far longer than necessary for object recognition, they follow subjective, conscious perception, and they are found in brain regions that are crucial for declarative memory formation. It has thus been hypothesized that they may represent the semantic "building blocks" of episodic memories. In this talk I will present data from single unit recordings in the hippocampus, entorhinal cortex, parahippocampal cortex, and amygdala during paradigms involving object recognition and conscious perception as well as encoding of episodic memories in order to characterize the role of concept cells in these cognitive functions.

Fast periodic visual stimulation (FPVS) has emerged as a promising tool for assessing cognitive function in individuals with dementia. This technique leverages electroencephalography (EEG) to measure brain responses to rapidly presented visual stimuli, offering a non-invasive and objective method for evaluating a range of cognitive functions. Unlike traditional cognitive assessments, FPVS does not rely on behavioural responses, making it particularly suitable for individuals with cognitive impairment. In this talk I will highlight a series of studies that have demonstrated its ability to detect subtle deficits in recognition memory, visual processing and attention in dementia patients using EEG in the lab, at home and in clinic. The method is quick, cost-effective, and scalable, utilizing widely available EEG technology. FPVS holds significant potential as a functional biomarker for early diagnosis and monitoring of dementia, paving the way for timely interventions and improved patient outcomes.

Join Us for the Memory Decoding Journal Club! A collaboration of the Carboncopies Foundation and BPF Aspirational Neuroscience. This time, we’re diving into a groundbreaking paper: "Motor learning selectively strengthens cortical and striatal synapses of motor engram neurons

Fear learning induces synaptic potentiation between engram neurons in the rat lateral amygdala. This study by Marios Abatis et al. demonstrates how fear conditioning strengthens synaptic connections between engram cells in the lateral amygdala, revealed through optogenetic identification of neuronal ensembles and electrophysiological measurements. The work provides crucial insights into memory formation mechanisms at the synaptic level, with implications for understanding anxiety disorders and developing targeted interventions. Presented by Dr. Kenneth Hayworth, this journal club will explore the paper's methodology linking engram cell reactivation with synaptic plasticity measurements, and discuss implications for memory decoding research.

Join us for the Memory Decoding Journal Club, a collaboration between the Carboncopies Foundation and BPF Aspirational Neuroscience. This month, we're diving into a groundbreaking paper: 'Reconstructing a new hippocampal engram for systems reconsolidation and remote memory updating' by Bo Lei, Bilin Kang, Yuejun Hao, Haoyu Yang, Zihan Zhong, Zihan Zhai, and Yi Zhong from Tsinghua University, Beijing Academy of Artificial Intelligence, IDG/McGovern Institute of Brain Research, and Peking Union Medical College. Dr. Randal Koene will guide us through an engaging discussion on these exciting findings and their implications for neuroscience and memory research.

How can the hippocampal system transition from episodic one-shot learning to a multi-shot learning regime and what is the utility of the resultant neural representations? This talk will explore the role of the dentate gyrus (DG) anatomy in this context. The canonical DG model suggests it performs pattern separation. More recent experimental results challenge this standard model, suggesting DG function is more complex and also supports the precise binding of objects and events to space and the integration of information across episodes. Very recent studies attribute pattern separation and pattern integration to anatomically distinct parts of the DG (the suprapyramidal blade vs the infrapyramidal blade). We propose a computational model that investigates this distinction. In the model the two processing streams (potentially localized in separate blades) contribute to the storage of distinct episodic memories, and the integration of information across episodes, respectively. The latter forms generalized expectations across episodes, eventually forming a cognitive map. We train the model with two data sets, MNIST and plausible entorhinal cortex inputs. The comparison between the two streams allows for the calculation of a prediction error, which can drive the storage of poorly predicted memories and the forgetting of well-predicted memories. We suggest that differential processing across the DG aids in the iterative construction of spatial cognitive maps to serve the generation of location-dependent expectations, while at the same time preserving episodic memory traces of idiosyncratic events.

Memories of emotionally-salient events are long-lasting, guiding behavior from minutes to years after learning. The prelimbic cortex (PL) is required for fear memory retrieval across time and is densely interconnected with many subcortical and cortical areas involved in recent and remote memory recall, including the temporal association area (TeA). While the behavioral expression of a memory may remain constant over time, the neural activity mediating memory-guided behavior is dynamic. In PL, different neurons underlie recent and remote memory retrieval and remote memory-encoding neurons have preferential functional connectivity with cortical association areas, including TeA. TeA plays a preferential role in remote compared to recent memory retrieval, yet how TeA circuits drive remote memory retrieval remains poorly understood. Here we used a combination of activity-dependent neuronal tagging, viral circuit mapping and miniscope imaging to investigate the role of the PL-TeA circuit in fear memory retrieval across time in mice. We show that PL memory ensembles recruit PL-TeA neurons across time, and that PL-TeA neurons have enhanced encoding of salient cues and behaviors at remote timepoints. This recruitment depends upon ongoing synaptic activity in the learning-activated PL ensemble. Our results reveal a novel circuit encoding remote memory and provide insight into the principles of memory circuit reorganization across time.

The centre of memory is the medial temporal lobe (MTL) and especially the hippocampus. In our research, a more flexible brain-inspired computational microcircuit of the CA1 region of the mammalian hippocampus was upgraded and used to examine how information retrieval could be affected under different conditions. Six models (1-6) were created by modulating different excitatory and inhibitory pathways. The results showed that the increase in the strength of the feedforward excitation was the most effective way to recall memories. In other words, that allows the system to access stored memories more accurately.

Memory is essential for shaping how we interpret the world, plan for the future, and understand ourselves, yet effective cognitive interventions for real-world episodic memory loss remain scarce. This talk introduces HippoCamera, a smartphone-based intervention inspired by how the brain supports memory, designed to enhance real-world episodic recollection by replaying high-fidelity autobiographical cues. It will showcase how our approach improves memory, mood, and hippocampal activity while uncovering links between memory distinctiveness, well-being, and the perception of time.

This webinar brings together three leaders in theoretical and computational neuroscience—Larry Abbott, Haim Sompolinsky, and Terry Sejnowski—to discuss how neural circuits generate fundamental aspects of the mind. Abbott illustrates mechanisms in electric fish that differentiate self-generated electric signals from external sensory cues, showing how predictive plasticity and two-stage signal cancellation mediate a sense of self. Sompolinsky explores attractor networks, revealing how discrete and continuous attractors can stabilize activity patterns, enable working memory, and incorporate chaotic dynamics underlying spontaneous behaviors. He further highlights the concept of object manifolds in high-level sensory representations and raises open questions on integrating connectomics with theoretical frameworks. Sejnowski bridges these motifs with modern artificial intelligence, demonstrating how large-scale neural networks capture language structures through distributed representations that parallel biological coding. Together, their presentations emphasize the synergy between empirical data, computational modeling, and connectomics in explaining the neural basis of cognition—offering insights into perception, memory, language, and the emergence of mind-like processes.

This webinar on learning and memory features three experts—Nicolas Brunel, Ashok Litwin-Kumar, and Julijana Gjorgieva—who present theoretical and computational approaches to understanding how neural circuits acquire and store information across different scales. Brunel discusses calcium-based plasticity and how standard “Hebbian-like” plasticity rules inferred from in vitro or in vivo datasets constrain synaptic dynamics, aligning with classical observations (e.g., STDP) and explaining how synaptic connectivity shapes memory. Litwin-Kumar explores insights from the fruit fly connectome, emphasizing how the mushroom body—a key site for associative learning—implements a high-dimensional, random representation of sensory features. Convergent dopaminergic inputs gate plasticity, reflecting a high-dimensional “critic” that refines behavior. Feedback loops within the mushroom body further reveal sophisticated interactions between learning signals and action selection. Gjorgieva examines how activity-dependent plasticity rules shape circuitry from the subcellular (e.g., synaptic clustering on dendrites) to the cortical network level. She demonstrates how spontaneous activity during development, Hebbian competition, and inhibitory-excitatory balance collectively establish connectivity motifs responsible for key computations such as response normalization.

The nervous system orchestrates adaptive behaviors by intricately coordinating responses to internal cues and environmental stimuli. This involves integrating sensory input, managing competing motivational states, and drawing on past experiences to anticipate future outcomes. While traditional models attribute this complexity to interactions between the mesocorticolimbic system and hypothalamic centers, the specific nodes of integration have remained elusive. Recent research, including our own, sheds light on the midline thalamus's overlooked role in this process. We propose that the midline thalamus integrates internal states with memory and emotional signals to guide adaptive behaviors. Our investigations into midline thalamic neuronal circuits have provided crucial insights into the neural mechanisms behind flexibility and adaptability. Understanding these processes is essential for deciphering human behavior and conditions marked by impaired motivation and emotional processing. Our research aims to contribute to this understanding, paving the way for targeted interventions and therapies to address such impairments.

Comprehending speech under acoustically challenging conditions is an everyday task that we can often execute with ease. However, accomplishing this requires the engagement of cognitive resources, such as auditory attention and working memory. The mechanisms that contribute to the robustness of speech comprehension are of substantial interest in the context of hearing mild to moderate hearing impairment, in which affected individuals typically report specific difficulties in understanding speech in background noise. Although hearing aids can help to mitigate this, they do not represent a universal solution, thus, finding alternative interventions is necessary. Given that age-related hearing loss (“presbycusis”) is inevitable, developing new approaches is all the more important in the context of aging populations. Moreover, untreated hearing loss in middle age has been identified as the most significant potentially modifiable predictor of dementia in later life. I will present research that has used a multi-methodological approach (fMRI, EEG, MEG and non-invasive brain stimulation) to try to elucidate the mechanisms that comprise the cognitive “last mile” in speech acousticallychallenging speech comprehension and to find ways to enhance them.

Understanding and potentially reversing memory decline necessitates a comprehensive examination of memory's evolution throughout life. Traditional memory assessments, however, suffer from a lack of comparability across different age groups due to the diverse nature of the tests employed. Addressing this gap, our study introduces a novel, ACT-R model-based memory assessment designed to provide a consistent metric for evaluating memory function across a lifespan, from 5 to 85-year-olds. This approach allows for direct comparison across various tasks and materials tailored to specific age groups. Our findings reveal a pronounced U-shaped trajectory of long-term memory function, with performance at age 5 mirroring those observed in elderly individuals with impairments, highlighting critical periods of memory development and decline. Leveraging this unified assessment method, we further investigate the therapeutic potential of rs-fMRI-guided TBS targeting area 8AV in individuals with early-onset Alzheimer’s Disease—a region implicated in memory deterioration and mood disturbances in this population. This research not only advances our understanding of memory's lifespan dynamics but also opens new avenues for targeted interventions in Alzheimer’s Disease, marking a significant step forward in the quest to mitigate memory decay.

There is a fundamental tension between storing discrete traces of individual experiences, which allows recall of particular moments in our past without interference, and extracting regularities across these experiences, which supports generalization and prediction in similar situations in the future. One influential proposal for how the brain resolves this tension is that it separates the processes anatomically into Complementary Learning Systems, with the hippocampus rapidly encoding individual episodes and the neocortex slowly extracting regularities over days, months, and years. But this does not explain our ability to learn and generalize from new regularities in our environment quickly, often within minutes. We have put forward a neural network model of the hippocampus that suggests that the hippocampus itself may contain complementary learning systems, with one pathway specializing in the rapid learning of regularities and a separate pathway handling the region’s classic episodic memory functions. This proposal has broad implications for how we learn and represent novel information of specific and generalized types, which we test across statistical learning, inference, and category learning paradigms. We also explore how this system interacts with slower-learning neocortical memory systems, with empirical and modeling investigations into how the hippocampus shapes neocortical representations during sleep. Together, the work helps us understand how structured information in our environment is initially encoded and how it then transforms over time.

Executive functions are cognitive processes that allow us to plan, monitor and execute our goals. Using fMRI, we investigated how early deafness influences crossmodal plasticity and the organisation of executive functions in the adult human brain. Results from a range of visual executive function tasks (working memory, task switching, planning, inhibition) show that deaf individuals specifically recruit superior temporal “auditory” regions during task switching. Neural activity in auditory regions predicts behavioural performance during task switching in deaf individuals, highlighting the functional relevance of the observed cortical reorganisation. Furthermore, language grammatical skills were correlated with the level of activation and functional connectivity of fronto-parietal networks. Together, these findings show the interplay between sensory and language experience in the organisation of executive processing in the brain.

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal, and abstract relationships that can be used to shape thought, planning, and behavior. Cognitive maps have been observed in the hippocampus, but their algorithmic form and the processes by which they are learned remain obscure. Here, we employed large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different versions of linear tracks in virtual reality. The results provide a detailed view of the formation of a cognitive map in the hippocampus. Throughout learning, both the animal behavior and hippocampal neural activity progressed through multiple intermediate stages, gradually revealing improved task representation that mirrored improved behavioral efficiency. The learning process led to progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent struture of the task. We show that a Hidden Markov Model (HMM) and a biologically plausible recurrent neural network trained using Hebbian learning can both capture core aspects of the learning dynamics and the orthogonalized representational structure in neural activity. In contrast, we show that gradient-based learning of sequence models such as Long Short-Term Memory networks (LSTMs) and Transformers do not naturally produce such orthogonalized representations. We further demonstrate that mice exhibited adaptive behavior in novel task settings, with neural activity reflecting flexible deployment of the state machine. These findings shed light on the mathematical form of cognitive maps, the learning rules that sculpt them, and the algorithms that promote adaptive behavior in animals. The work thus charts a course toward a deeper understanding of biological intelligence and offers insights toward developing more robust learning algorithms in artificial intelligence.

Remembering events in the past is crucial to intelligent behaviour. Flexible memory retrieval, beyond simple recall, requires a model of how events relate to one another. Two key brain systems are implicated in this process: the hippocampal episodic memory (EM) system and the prefrontal working memory (WM) system. While an understanding of the hippocampal system, from computation to algorithm and representation, is emerging, less is understood about how the prefrontal WM system can give rise to flexible computations beyond simple memory retrieval, and even less is understood about how the two systems relate to each other. Here we develop a mathematical theory relating the algorithms and representations of EM and WM by showing a duality between storing memories in synapses versus neural activity. In doing so, we develop a formal theory of the algorithm and representation of prefrontal WM as structured, and controllable, neural subspaces (termed activity slots). By building models using this formalism, we elucidate the differences, similarities, and trade-offs between the hippocampal and prefrontal algorithms. Lastly, we show that several prefrontal representations in tasks ranging from list learning to cue dependent recall are unified as controllable activity slots. Our results unify frontal and temporal representations of memory, and offer a new basis for understanding the prefrontal representation of WM

Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). Title: SwiFT: Swin 4D fMRI Transformer Abstract: Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches for fMRI analysis utilize hand-crafted features, but the process of feature extraction risks losing essential information in fMRI scans. To address this challenge, we present SwiFT (Swin 4D fMRI Transformer), a Swin Transformer architecture that can learn brain dynamics directly from fMRI volumes in a memory and computation-efficient manner. SwiFT achieves this by implementing a 4D window multi-head self-attention mechanism and absolute positional embeddings. We evaluate SwiFT using multiple large-scale resting-state fMRI datasets, including the Human Connectome Project (HCP), Adolescent Brain Cognitive Development (ABCD), and UK Biobank (UKB) datasets, to predict sex, age, and cognitive intelligence. Our experimental outcomes reveal that SwiFT consistently outperforms recent state-of-the-art models. Furthermore, by leveraging its end-to-end learning capability, we show that contrastive loss-based self-supervised pre-training of SwiFT can enhance performance on downstream tasks. Additionally, we employ an explainable AI method to identify the brain regions associated with sex classification. To our knowledge, SwiFT is the first Swin Transformer architecture to process dimensional spatiotemporal brain functional data in an end-to-end fashion. Our work holds substantial potential in facilitating scalable learning of functional brain imaging in neuroscience research by reducing the hurdles associated with applying Transformer models to high-dimensional fMRI. Speaker: Junbeom Kwon is a research associate working in Prof. Jiook Cha’s lab at Seoul National University. Paper link: https://arxiv.org/abs/2307.05916

Working memory (WM) is a cognitive function that allows the short-term maintenance and manipulation of information when no longer accessible to the senses. It relies on temporarily storing stimulus features in the activity of neuronal populations. To preserve these dynamics from distraction it has been proposed that pre and post-distraction population activity decomposes into orthogonal subspaces. If orthogonalization is necessary to avoid WM distraction, it should emerge as performance in the task improves. We sought evidence of WM orthogonalization learning and the underlying mechanisms by analyzing calcium imaging data from the prelimbic (PrL) and anterior cingulate (ACC) cortices of mice as they learned to perform an olfactory dual task. The dual task combines an outer Delayed Paired-Association task (DPA) with an inner Go-NoGo task. We examined how neuronal activity reflected the process of protecting the DPA sample information against Go/NoGo distractors. As mice learned the task, we measured the overlap between the neural activity onto the low-dimensional subspaces that encode sample or distractor odors. Early in the training, pre-distraction activity overlapped with both sample and distractor subspaces. Later in the training, pre-distraction activity was strictly confined to the sample subspace, resulting in a more robust sample code. To gain mechanistic insight into how these low-dimensional WM representations evolve with learning we built a recurrent spiking network model of excitatory and inhibitory neurons with low-rank connections. The model links learning to (1) the orthogonalization of sample and distractor WM subspaces and (2) the orthogonalization of each subspace with irrelevant inputs. We validated (1) by measuring the angular distance between the sample and distractor subspaces through learning in the data. Prediction (2) was validated in PrL through the photoinhibition of ACC to PrL inputs, which induced early-training neural dynamics in well-trained animals. In the model, learning drives the network from a double-well attractor toward a more continuous ring attractor regime. We tested signatures for this dynamical evolution in the experimental data by estimating the energy landscape of the dynamics on a one-dimensional ring. In sum, our study defines network dynamics underlying the process of learning to shield WM representations from distracting tasks.

Recent studies identified memory engram neurons, a neuronal population that is recruited by initial learning and is reactivated during memory recall.  Memory engram neurons are connected to one another through memory engram synapses in a distributed network of brain areas.  Our central hypothesis is that an associative memory is encoded and consolidated by selective strengthening of engram synapses.  We are testing this hypothesis, using a combination of engram cell labeling, optogenetic/chemogenetic, electrophysiological, and virus tracing approaches in rodent models of contextual fear conditioning.  In this talk, I will discuss our findings on how synaptic plasticity in memory engram synapses contributes to the acquisition and consolidation of contextual fear memory in a distributed network of the amygdala, hippocampus, and neocortex.

People largely differ with respect to how well they can learn, memorize, and perceive faces. In this talk, I address two potential sources of variation. One factor might be people’s ability to adapt their perception to the kind of faces they are currently exposed to. For instance, some studies report that those who show larger adaptation effects are also better at performing face learning and memory tasks. Another factor might be people’s sensitivity to perceive fine differences between similar-looking faces. In fact, one study shows that the brain of good performers in a face memory task shows larger neural differences between similar-looking faces. Capitalizing on this body of evidence, I present a behavioural study where I explore the relationship between people’s perceptual adaptability and sensitivity and their individual face processing performance.

Working memory is about the past but for the future. Adopting such a future-focused perspective shifts the narrative of working memory as a limited-capacity storage system to working memory as an anticipatory buffer that helps us prepare for potential and sequential upcoming behaviour. In my talk, I will present a series of our recent studies that have started to reveal emerging principles of a working memory that looks forward – highlighting, amongst others, how selective attention plays a vital role in prioritising internal contents for behaviour, and the bi-directional links between visual working memory and action. These studies show how studying the dynamics of working memory, selective attention, and action together paves way for an integrated understanding of how mind serves behaviour.