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11 results

Newest first

Topological maps are for robust and wiring-efficient dimensionality reduction

Nicola Mendini, Michael Mangan, Stuart Wilson · Bernstein Conference 2024

Most mammalian species present topological maps of preference for the ori- entation of edges in their early visual cortex. However, despite the evidence that evolution has favoured topological map organisation in most species, a clear explanation for the functional role of such maps is lacking. Traditional approaches based on wiring minimisation are incomplete, showing how topology can reduce metabolic costs, but only after assuming a functional role of short- ranged like-tuned connectivity. In this work, we claim that topological maps are for reducing the complexity of the propagated informat

ePoster · Computational Neuroscience

Neuronal encoding of forelimb proprioception in the mouse somatosensory cortex and cerebellum

Mélanie Palacio Manzano, Irina Scheer, Ignacio Alonso et al. · FENS Forum 2024

Proprioception is the sensation of the position and movement of the body in space. Forelimb proprioceptive afferents not only drive reflexes circuits in the spinal cord, but also ascend to the brain via projections to the external cuneate nucleus (Ecu) in the brainstem. Secondary neurons in the Ecu relay the incoming information via two major pathways: 1) the cuneo-cerebellar track which innervates the cerebellum and allows movement adaptation to unexpected perturbations and 2) the dorsal column-medial lemniscus pathway targeting the somatosensory cortex and allowing for the perception of prop

ePoster · Neuroscience

Convex neural codes in recurrent networks and sensory systems

Vladimir Itskov · The Pennsylvania State University

Neural activity in many sensory systems is organized on low-dimensional manifolds by means of convex receptive fields. Neural codes in these areas are constrained by this organization, as not every neural code is compatible with convex receptive fields. The same codes are also constrained by the structure of the underlying neural network. In my talk I will attempt to provide answers to the following natural questions: (i) How do recurrent circuits generate codes that are compatible with the convexity of receptive fields? (ii) How can we utilize the constraints imposed by the convex receptive

Wed, Dec 14, 2022 · 05:00 UTC

Seminar · Computational Neuroscience

Long‐term dynamics of the entorhinal grid code

Noa Sadeh, Alon Rubin, Yaniv Ziv, Meytar Zemer · COSYNE 2022

…indicating a key role for entorhinal neural codes in long term memory. Recent studies have found that hippocampal place codes graduall

ePoster · Neuroscience

How many objects can be recognized under all possible views?

Blake Bordelon, Matthew Farrell, Shubhendu Trivedi, Cengiz Pehlevan · COSYNE 2022

…should also be represented in neural codes. A natural strategy to encode both the identity of an object and its view is to utilize an equivariant code, where the neural representation transforms in a manner consistent with the transformations to the inputs, and consistent across objects. A classic example of such a code is given by an intermediate layer of a convolutional ne

ePoster · Computational Neuroscience

Neural Codes for Natural Behaviors in Flying Bats

Nachum Ulanovsky · Weizmann Institute

…this point by describing studies of neural codes for spatial behaviors and social behaviors, in flying bats – using wireless neurophysiology methods that we developed – and will highlight new neuronal representations that we discovered in animals navigating through 3D spaces, or in very large-scale environments, or engaged in social interactions. In particular, I will discuss: (1) A multi-scale neural code for very large environments, which we dis

Thu, Jan 13, 2022 · 15:30 UTC

Seminar · Neuroscience

When and (maybe) why do high-dimensional neural networks produce low-dimensional dynamics?

Eric Shea-Brown · Department of Applied Mathematics, University of Washington

There is an avalanche of new data on activity in neural networks and the biological brain, revealing the collective dynamics of vast numbers of neurons. In principle, these collective dynamics can be of almost arbitrarily high dimension, with many independent degrees of freedom — and this may reflect powerful capacities for general computing or information. In practice, neural datasets reveal a range of outcomes, including collective dynamics of much lower dimension — and this may reflect other desiderata for neural codes. For what networks does each case occur? We begin by exploring bottom-up

Thu, Nov 18, 2021 · 16:00 UTC

Seminar · Computational Neuroscience

…We show that neural codes that maximize reward expectation—and not accurate sensory representations—account for retinal responses in insects, and retinotopically-specific adaptive codes in humans. Thus, our results provide evidence that fitness-maximizing rules imposed by the environment are applied at the ear

Thu, May 13, 2021 · 01:00 UTC

Seminar · Computational Neuroscience

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

Fri, Jan 29, 2021 · 15:00 UTC

Seminar · Neuroscience