AIMC Topic: Neural Networks, Computer

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Clarifying status of DNNs as models of human vision.

The Behavioral and brain sciences
On several key issues we agree with the commentators. Perhaps most importantly, everyone seems to agree that psychology has an important role to play in building better models of human vision, and (most) everyone agrees (including us) that deep neura...

and in deep neural network models of neurological network functions.

The Behavioral and brain sciences
Depending on what we mean by "explanation," challenges to the explanatory depth and reach of deep neural network models of visual and other forms of intelligent behavior may need revisions to both the elementary building blocks of neural nets (the ex...

A deep new look at color.

The Behavioral and brain sciences
Bowers et al. counter deep neural networks (DNNs) as good models of human visual perception. From our color perspective we feel their view is based on three misconceptions: A misrepresentation of the state-of-the-art of color perception; the type of ...

There is a fundamental, unbridgeable gap between DNNs and the visual cortex.

The Behavioral and brain sciences
Deep neural networks (DNNs) are not just inadequate models of the visual system but are so different in their structure and functionality that they are not even on the same playing field. DNN units have almost nothing in common with neurons, and, unl...

Perceptual learning in humans: An active, top-down-guided process.

The Behavioral and brain sciences
Deep neural network (DNN) models of human-like vision are typically built by feeding blank slate DNN visual images as training data. However, the literature on human perception and perceptual learning suggests that developing DNNs that truly model hu...

Neural networks need real-world behavior.

The Behavioral and brain sciences
Bowers et al. propose to use controlled behavioral experiments when evaluating deep neural networks as models of biological vision. We agree with the sentiment and draw parallels to the notion that "neuroscience needs behavior." As a promising path f...

The scientific value of explanation and prediction.

The Behavioral and brain sciences
Deep neural network models have revived long-standing debates on the value of explanation versus prediction for advancing science. Bowers et al.'s critique will not make these models go away, but it is likely to prompt new work that seeks to reconcil...

Modelling human vision needs to account for subjective experience.

The Behavioral and brain sciences
Vision is inseparably connected to perceptual awareness which can be seen as the culmination of sensory processing. Studies on conscious vision reveal that object recognition is just one of the means through which our representation of the world is b...

Neither hype nor gloom do DNNs justice.

The Behavioral and brain sciences
Neither the hype exemplified in some exaggerated claims about deep neural networks (DNNs), nor the gloom expressed by Bowers et al. do DNNs as models in vision science justice: DNNs rapidly evolve, and today's limitations are often tomorrow's success...

Review of machine learning and deep learning models for toxicity prediction.

Experimental biology and medicine (Maywood, N.J.)
The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional ...