Artificial neural network

The sensory experience of an organism is shaped by movement, either of the entire body or of individual sensors. Many organisms can exploit sensor motion in ways that enrich the quality of sensory cues and enhance their ability to estimate unknown quantities. For instance, mantids perform peering head movements before striking prey, a behavior thought to aid in depth estimation. A key challenge in sensory neuroscience is to uncover what motivates such behaviors. How can we understand what information is gained via sensor motion? Here, we present a novel computational method for modeling active

Identifying and characterizing the wiring rules of neural circuits is critical for our understanding of the computations that they perform and how they learn. The reconstruction of complete connectivity maps of neural networks at the single cell resolution make the quantitative study of their design principles possible. We examined the detailed connectivity of a column of the mouse visual cortex with a generative model that predicts connectivity based on explicit biological and physical features: excitatory or inhibitory cell type, the distance between cell bodies, and cell's depth, which is c

…While much is known about the brain networks underlying visual search, our understanding of the neural computations driving this behavior is limited. Simulating this behavior in-silico can provide a tool for developing and testing hypotheses about these neural computations. To address this gap, we trained an image-computable artificial neural networ

Practicing a visual task leads to long-lasting perceptual improvements in humans and other animals. However, the generalization of Visual Learning (VL) to unseen conditions varies across different tasks. Particularly, it has been shown that learning ‘easy’ visual tasks leads to better generalization than learning ‘hard’ ones. Moreover, experimental evidence suggests that greater generalizability in VL can be achieved through sequential curriculum training, specifically by training on ‘easier’ versions of a task before progressing to ‘harder’ versions. Yet, a neurocomputational explanation for

…understanding synaptic plasticity and neural network stability. In this work, we develop a model for spine size dynamics using a simple stochastic process based on minimal biological assumptions. The model effectively captures the key characteristics of spontaneous spine fluctuations, particularly replicating the observed lognormal distribution of spine sizes. We then train an artificial neural network in which synaptic strengths fluctuate according to our model. T

…Aided by an artificial neural network, we found that FPl involved a parallel processing t

…understanding synaptic plasticity and neural network stability [1,2]. In this work, we develop a model for spine size dynamics using a simple stochastic process based on minimal biological assumptions. This model effectively captures the key characteristics of spontaneous spine fluctuations, particularly replicating the observed lognormal distribution of spine sizes. We then use an artificial neural network structure where synaptic strengths fluctuate accor

Dementia is characterised by a profound decline in cognitive and behavioural functions, drastically affecting daily life and social interactions. There is a pressing need for novel therapeutic strategies, particularly if these can be repurposed from available medicine – recent research has suggested antidiabetic drugs may have neuroprotective properties, but validation requires translatable pre-clinical models paired with unbiased assessment. Rocky Mountain Laboratory (RML) scrapie is a well-characterised model that mimics many of the hallmarks of neurodegeneration seen in dementia but does no

In this study, we studied the localization problem of weakly electric fish using their electric field activity. These fish emit electric fields and employ electroreceptors to understand their surroundings. The goal of this study is to localize individual fish within a 1.5-meter by 1.5-meter testing aquarium using a custom-built data acquisition system. This system incorporates a 3 by 3 silver electrode array, alongside an analog circuitry that effectively filters and amplifies the recorded electric field activity leveraging instrumentation amplifiers. Furthermore, the digitization and logging

…is computationally challenging for artificial agents, yet many animals such as primates can perform it with ease. To study one-exemplar generalization in the lab, it would be ideal to develop appropriate behavioral tasks in more tractable model organisms, such as rodents. Here we report that mice can perform one-exemplar generalization with difficult texture-based visual stimuli such as “leaves” and “rocks”. We trained mice to discriminate between single images from two texture cat

…We train an artificial neural network on a predictive coding task. We continue training in the presence of noise after go

Working memory (WM) maintains information during a delay period by internally generated neuronal activity. Here we report the dynamic organization of global cell-assembly activity that encodes WM information via cross-regional sequential spiking. Multiple Neuropixels-probe single-neuron recordings were made from over 60 brain regions in head-fixed mice performing an olfactory WM task. We found that neurons encoded WM during the delay period predominantly by transient activity that formed cross-region sequential waves, with the proportions of these neurons progressively reduced from olfactory t

…address these issues, we developed an artificial neural network, TweetyNet[2], that segments and labels

…type of computationally efficient Artificial Neural Network (ANN) called Rank Similarity Filters. They can be used to both transform and classify nonlinearly separable datasets with many datapoints and dimensions. The weights of the filters are set using the rank orders of features in

…based on Deep Convolutional Neural Networks (DNNs) is currently emerging as the most promising technique to solve such complex

…Second, we construct a neural coding framework by which these rational thoughts, their computational dynamics, and actions can be identified within the manifold of neural activity. We illustrate the value of this a