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7 results

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A perturbative approach to understand retinal computations

Olivier Marre · Institut de la Vision, Paris

…an approach where we add small perturbations to natural scenes and measure how these perturbations change neuronal responses, to better understand the features extracted by sensory neurons. I will show se

Wed, Mar 12, 2025 · 15:00 UTC

Seminar · Computational Neuroscience

Long-term motor learning creates structure within neural space that shapes motor adaptation

Joanna Chang, Matthew Perich, Lee E. Miller et al. · COSYNE 2022

Motor adaptation is a widely-used paradigm for understanding short-term learning. However, it is unknown how existing skillsets acquired through the long-term learning process that begins in utero can affect motor adaptation. Long-term learning likely causes changes in neural connectivity, which may shape the neural dynamics that can be produced. To understand the interaction between circuit connectivity constraints and a neural population’s ability to change its activity patterns, we modeled the neural dynamics of the motor cortex during skill learning and subsequent adaptation using a recurr

ePoster · Computational Neuroscience

Keeping your Brain in Balance: the Ups and Downs of Homeostatic Plasticity (virtual)

Gina Turrigiano, PhD · Professor, Department of Biology, Brandeis University, USA

Our brains must generate and maintain stable activity patterns over decades of life, despite the dramatic changes in circuit connectivity and function induced by learning and experience-dependent plasticity. How do our brains acheive this balance between opposing need for plasticity and stability? Over the past two decades, we and others have uncovered a family of “homeostatic” negative feedback mechanisms that are theorized to stabilize overall brain activity while allowing specific connections to be reconfigured by experience. Here I discuss recent work in which we demonstrate that individua

Thu, Feb 17, 2022 · 10:00 UTC

Seminar · Neuroscience

Robustness in spiking networks: a geometric perspective

Christian Machens · Champalimaud Center, Lisboa

Neural systems are remarkably robust against various perturbations, a phenomenon that still requires a clear explanation. Here, we graphically illustrate how neural networks can become robust. We study spiking networks that generate low-dimensional representations, and we show that the neurons’ subthreshold voltages are confined to a convex region in a lower-dimensional voltage subspace, which we call a ‘bounding box.’ Any changes in network parameters (such as number of neurons, dimensionality of inputs, firing thresholds, synaptic weights, or transmission delays) can all be understood as def

Wed, Feb 16, 2022 · 05:00 UTC

Seminar · Computational Neuroscience

…network from observing its response to perturbations? I will first describe the results of optogenetic activation of inhibitory neurons in mice cortex, and show that the results are consistent with inhibition stabilization. I will then move to experiments in which excitatory neurons are activated optogenetically, with or without visual inputs, in mice and monkeys. In some conditions, these experiments show a surprising result that the distribution of firing rates is not significan

Wed, Jan 19, 2022 · 05:00 UTC

Seminar · Neuroscience

Advances in Computational Psychiatry: Understanding (cognitive) control as a network process

Danielle S. Bassett · University of Pennsylvania, & Santa Fe Institute

The human brain is a complex organ characterized by heterogeneous patterns of interconnections. Non-invasive imaging techniques now allow for these patterns to be carefully and comprehensively mapped in individual humans, paving the way for a better understanding of how wiring supports cognitive processes. While a large body of work now focuses on descriptive statistics to characterize these wiring patterns, a critical open question lies in how the organization of these networks constrains the potential repertoire of brain dynamics. In this talk, I will describe an approach for understanding

Thu, Jun 10, 2021 · 17:00 UTC

Seminar · Computational Neuroscience