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.

Deep learning provides new data-driven tools to relate neural activity to perception and cognition, aiding scientists in developing theories of neural computation that increasingly resemble biological systems both at the level of behavior and of neural activity. But what in a deep neural network should correspond to what in a biological system? This question is addressed implicitly in the use of comparison measures that relate specific neural or behavioral dimensions via a particular functional form. However, distinct comparison methodologies can give conflicting results in recovering even a known ground-truth model in an idealized setting, leaving open the question of what to conclude from the outcome of a systems comparison using any given methodology. Here, we develop a framework to make explicit and quantitative the effect of both hypothesis-driven aspects—such as details of the architecture of a deep neural network—as well as methodological choices in a systems comparison setting. We demonstrate via the learning dynamics of deep neural networks that, while the role of the comparison methodology is often de-emphasized relative to hypothesis-driven aspects, this choice can impact and even invert the conclusions to be drawn from a comparison between neural systems. We provide evidence that the right way to adjudicate a comparison depends on the use case—the scientific hypothesis under investigation—which could range from identifying single-neuron or circuit-level correspondences to capturing generalizability to new stimulus properties

Founded in 2002, the Brain Connectivity Workshop (BCW) is an annual international meeting for in-depth discussions of all aspects of brain connectivity research. By bringing together experts in computational neuroscience, neuroscience methodology and experimental neuroscience, it aims to improve the understanding of the relationship between anatomical connectivity, brain dynamics and cognitive function. These workshops have a unique format, featuring only short presentations followed by intense discussion. This year’s workshop is co-organised by Wellcome, putting the spotlight on brain connectivity in mental health disorders. We look forward to having you join us for this exciting, thought-provoking and inclusive event.

Analogical reasoning is one of the most complex cognitive functions in humans that allows abstract thinking, high-level reasoning, and learning. Based on analogical reasoning, one can extract an abstract and general concept (i.e., an analogy schema) from a familiar situation and apply it to a new context or domain (i.e., analogy transfer). These processes allow us to solve problems we never encountered before and generate new ideas. However, the place of analogy transfer in problem solving mechanisms is unclear. This presentation will describe several experiments with three main findings. First, we show how analogy transfer facilitates problem-solving, replicating existing empirical data largely based on the radiation/fortress problems with four new riddles. Second, we propose a new experimental task that allows us to quantify analogy transfer. Finally, using science network methodology, we show how restructuring the mental representation of a problem can predict successful solving of an analogous problem. These results shed new light on the cognitive mechanism underlying solution transfer by analogy and provide a new tool to quantify individual abilities.

Evidence-based education programmes, like many clinical interventions, are multi-faceted and can be expensive to implement. In this talk I will describe an alternative: distilling the common elements across many evidence-based programmes. Published programme manuals are selected through systematic review, then extensively coded and cross-referenced. Finally, the common elements that emerge are shared with practitioners as part of a ‘library’ of practices (rather than a holistic programme manual). Although the common elements methodology has been used in the prevention and intervention sciences, this project reflects the first attempt at applying this approach to early childhood education. I will describe the common elements methods and preliminary findings from our Nuffield-funded project, in collaboration with the Early Intervention Foundation. I will discuss the challenges and opportunities we have encountered, alongside our strategies for sharing evidence with practitioners in a digestible way.

Gradient-based optimization powered by the backpropagation algorithm proved to be the pivotal method in the training of non-spiking artificial neural networks. At the same time, spiking neural networks hold the promise for efficient processing of real-world sensory data by communicating using discrete events in continuous time. We derive the backpropagation algorithm for a recurrent network of spiking (leaky integrate-and-fire) neurons with hard thresholds and show that the backward dynamics amount to an event-based backpropagation of errors through time. Our derivation uses the jump conditions for partial derivatives at state discontinuities found by applying the implicit function theorem, allowing us to avoid approximations or substitutions. We find that the gradient exists and is finite almost everywhere in weight space, up to the null set where a membrane potential is precisely tangent to the threshold. Our presented algorithm, EventProp, computes the exact gradient with respect to a general loss function based on spike times and membrane potentials. Crucially, the algorithm allows for an event-based communication scheme in the backward phase, retaining the potential advantages of temporal sparsity afforded by spiking neural networks. We demonstrate the optimization of spiking networks using gradients computed via EventProp and the Yin-Yang and MNIST datasets with either a spike time-based or voltage-based loss function and report competitive performance. Our work supports the rigorous study of gradient-based optimization in spiking neural networks as well as the development of event-based neuromorphic architectures for the efficient training of spiking neural networks. While we consider the leaky integrate-and-fire model in this work, our methodology generalises to any neuron model defined as a hybrid dynamical system.

On behalf of NeurotechEU, we are pleased to invite you to participate in the Summit 2021 pre-selection event on October 23, 2021. The event will be held online via the Platform Crowdcast.io, and it is going to be organized by NeurotechEU-The European University of Brain and Technology. Students from all over NeurotechEU have the chance to present their research (bachelor’s thesis, Master’s thesis, PhD, post-doc…) following the methodology of three minutes thesis (3MT from the University of Queensland): https://threeminutethesis.uq.edu.au/resources/3mt-competitor-guide. There will be one session per university and at the end of it, two semi-finalists will be selected from each university. They will compete in the Summit 2021 on November 22nd. There will be prizes for the winners who will be selected by voting of the audience.

A brain-machine interface (BMI) is a system that enables users to interact with computers and robots through the voluntary modulation of their brain activity. Such a BMI is particularly relevant as an aid for patients with severe neuromuscular disabilities, although it also opens up new possibilities in human-machine interaction for able-bodied people. Real-time signal processing and decoding of brain signals are certainly at the heart of a BMI. Yet, this does not suffice for subjects to operate a brain-controlled device. In the first part of my talk I will review some of our recent studies, most involving participants with severe motor disabilities, that illustrate additional principles of a reliable BMI that enable users to operate different devices. In particular, I will show how an exclusive focus on machine learning is not necessarily the solution as it may not promote subject learning. This highlights the need for a comprehensive mutual learning methodology that foster learning at the three critical levels of the machine, subject and application. To further illustrate that BMI is more than just decoding, I will discuss how to enhance subject learning and BMI performance through appropriate feedback modalities. Finally, I will show how these principles translate to motor rehabilitation, where in a controlled trial chronic stroke patients achieved a significant functional recovery after the intervention, which was retained 6-12 months after the end of therapy.

Methodology for understanding the basic phenomena of life can be done in vitro or in vivo, under tightly-controlled experimental conditions designed to limit variability. However stringent the protocol, these experiments do not occur in a cultural vacuum and they are often subject to the same societal biases as other research disciplines. Many researchers uphold the status quo of biased basic research by not questioning the characteristics of their experimental animals, or the people from whom their tissue samples were collected. This means that our fundamental understanding of life has been built on biased models. This session will explore the ways in which basic life sciences research can be biased and the implications of this. We will discuss practical ways to assess your research design and how to make sure it is representative.

The ‘Hunt the Sex Difference’ agenda has informed brain research for decades, if not centuries. This talk aims to demonstrate how a fixed belief in differences between ‘male’ and ‘female’ brains can narrow and even distort the research process. This can include the questions that are asked, the methodology selected and the analytical pipeline. It can also powerfully inform the interpretation of results and the ‘spin’ used in the public communication of such research.

Consciousness is a phenomenon unlike any other studied in natural science. Yet when building theories and designing experiments, we often proceed as if this were not the case. In this talk, I present two recent investigations of mine which explore the implications of consciousness' unique epistemic context for scientific theory building and experimental design. The first investigation is concerned with falsifications of theories of consciousness and identifies a rather deep problem in the usual scheme of testing theories. The second is an axiomatization and subsequent formalization of some of consciousness' more problematic epistemic features that allows to precisely quantify where the usual scientific methodology ceases to be applicable. For both cases, I indicate ways to resolve the problem.