feedback loop
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Learning and Memory
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.
Unmotivated bias
In this talk, I will explore how social affective biases arise even in the absence of motivational factors as an emergent outcome of the basic structure of social learning. In several studies, we found that initial negative interactions with some members of a group can cause subsequent avoidance of the entire group, and that this avoidance perpetuates stereotypes. Additional cognitive modeling discovered that approach and avoidance behavior based on biased beliefs not only influences the evaluative (positive or negative) impressions of group members, but also shapes the depth of the cognitive representations available to learn about individuals. In other words, people have richer cognitive representations of members of groups that are not avoided, akin to individualized vs group level categories. I will end presenting a series of multi-agent reinforcement learning simulations that demonstrate the emergence of these social-structural feedback loops in the development and maintenance of affective biases.
Why is the suprachiasmatic nucleus such a brilliant circadian time-keeper?
Circadian clocks dominate our lives. By creating and distributing an internal representation of 24-hour solar time, they prepare us, and thereby adapt us, to the daily and seasonal world. Jet-lag is an obvious indicator of what can go wrong when such adaptation is disrupted acutely. More seriously, the growing prevalence of rotational shift-work which runs counter to our circadian life, is a significant chronic challenge to health, presenting as increased incidence of systemic conditions such as metabolic and cardiovascular disease. Added to this, circadian and sleep disturbances are a recognised feature of various neurological and psychiatric conditions, and in some cases may contribute to disease progression. The “head ganglion” of the circadian system is the suprachiasmatic nucleus (SCN) of the hypothalamus. It synchronises the, literally, innumerable cellular clocks across the body, to each other and to solar time. Isolated in organotypic slice culture, it can maintain precise, high-amplitude circadian cycles of neural activity, effectively, indefinitely, just as it does in vivo. How is this achieved: how does this clock in a dish work? This presentation will consider SCN time-keeping at the level of molecular feedback loops, neuropeptidergic networks and neuron-astrocyte interactions.
NMC4 Short Talk: Brain-inspired spiking neural network controller for a neurorobotic whisker system
It is common for animals to use self-generated movements to actively sense the surrounding environment. For instance, rodents rhythmically move their whiskers to explore the space close to their body. The mouse whisker system has become a standard model to study active sensing and sensorimotor integration through feedback loops. In this work, we developed a bioinspired spiking neural network model of the sensorimotor peripheral whisker system, modelling trigeminal ganglion, trigeminal nuclei, facial nuclei, and central pattern generator neuronal populations. This network was embedded in a virtual mouse robot, exploiting the Neurorobotics Platform, a simulation platform offering a virtual environment to develop and test robots driven by brain-inspired controllers. Eventually, the peripheral whisker system was properly connected to an adaptive cerebellar network controller. The whole system was able to drive active whisking with learning capability, matching neural correlates of behaviour experimentally recorded in mice.
The developing visual brain – answers and questions
We will start our talk with a short video of our research, illustrating methods (some old and new) and findings that have provided our current understanding of how visual capabilities develop in infancy and early childhood. However, our research poses some outstanding questions. We will briefly discuss three issues, which are linked by a common focus on the development of visual attentional processing: (1) How do recurrent cortical loops contribute to development? Cortical selectivity (e.g., to orientation, motion, and binocular disparity) develops in the early months of life. However, these systems are not purely feedforward but depend on parallel pathways, with recurrent feedback loops playing a critical role. The development of diverse networks, particularly for motion processing, may explain changes in dynamic responses and resolve developmental data obtained with different methodologies. One possible role for these loops is in top-down attentional control of visual processing. (2) Why do hyperopic infants become strabismic (cross-eyes)? Binocular interaction is a particularly sensitive area of development. Standard clinical accounts suppose that long-sighted (hyperopic) refractive errors require accommodative effort, putting stress on the accommodation-convergence link that leads to its breakdown and strabismus. Our large-scale population screening studies of 9-month infants question this: hyperopic infants are at higher risk of strabismus and impaired vision (amblyopia and impaired attention) but these hyperopic infants often under- rather than over-accommodate. This poor accommodation may reflect poor early attention processing, possibly a ‘soft sign’ of subtle cerebral dysfunction. (3) What do many neurodevelopmental disorders have in common? Despite similar cognitive demands, global motion perception is much more impaired than global static form across diverse neurodevelopmental disorders including Down and Williams Syndromes, Fragile-X, Autism, children with premature birth and infants with perinatal brain injury. These deficits in motion processing are associated with deficits in other dorsal stream functions such as visuo-motor co-ordination and attentional control, a cluster we have called ‘dorsal stream vulnerability’. However, our neuroimaging measures related to motion coherence in typically developing children suggest that the critical areas for individual differences in global motion sensitivity are not early motion-processing areas such as V5/MT, but downstream parietal and frontal areas for decision processes on motion signals. Although these brain networks may also underlie attentional and visuo-motor deficits , we still do not know when and how these deficits differ across different disorders and between individual children. Answering these questions provide necessary steps, not only increasing our scientific understanding of human visual brain development, but also in designing appropriate interventions to help each child achieve their full potential.
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.
Object detection with deep learning and attention feedback loops
Bernstein Conference 2024
Basal Ganglia feedback loops as possible candidates for generation of beta oscillation
COSYNE 2022
A circuit library for exploring the functional logic of massive feedback loops in Drosophila brain
COSYNE 2022
VIP-to-SST interneuron feedback loop guides cortical reinforcement learning
FENS Forum 2024
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