Neural Network Models Explained! | Neuroscience Methods 101

With neural network models activity in the brain can be simulated. Here we explain how they work.

Artificial neural networks, computational neural networks, or neural network models were first modelled after the visual cortex. They attempt to explain how a certain input can be transformed to a particular output. For example, a picture entering the eyes, is eventually transformed into a categorical output about what is on the picture. Our brain does this with ease, and also neural networks can do this artificially.

Neural networks consists of nodes and connections There are multiple layers: An input layer, where information enters, one or more hidden layers, where information is transformed, and an output layer where the final prediction or results is presented.

The connections have different strengths, also referred to weights. During the training of the model, these weights are constantly updated. This is how the model learns particular features of the input information, which helps it to optimize the output.

Neural networks are not only useful for neuroscience. Their biggest applications happen outside of neuroscience, for various reasons.

References/resources:
Barak O. (2017). Recurrent neural networks as versatile tools of neuroscience research. Current opinion in neurobiology, 46, 1–6. https://doi.org/10.1016/j.conb.2017.0...

van Gerven, M., & Bohte, S. (2017). Editorial: Artificial Neural Networks as Models of Neural Information Processing. Frontiers in computational neuroscience, 11, 114. https://doi.org/10.3389/fncom.2017.00114

Yuste R. (2015). From the neuron doctrine to neural networks. Nature reviews. Neuroscience, 16(8), 487–497. https://doi.org/10.1038/nrn3962

Narrated by: Miles Wischnewski

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