Predictive Feedback Can Account for Biphasic Responses in the Lateral Geniculate Nucleus

By Janneke F. M. Jehee, Dana H. Ballard. 2009

81 citations recorded by the source corpus.

Biphasic neural response properties, where the optimal stimulus for driving a neural response changes from one stimulus pattern to the opposite stimulus pattern over short periods of time, have been described in several visual areas, including lateral geniculate nucleus (LGN), primary visual cortex (V1), and middle temporal area (MT). We describe a hierarchical model of predictive coding and simulations that capture these temporal variations in neuronal response properties. We focus on the LGN-V1 circuit and find that after training on natural images the model exhibits the brain's LGN-V1 connectivity structure, in which the structure of V1 receptive fields is linked to the spatial alignment and properties of center-surround cells in the LGN. In addition, the spatio-temporal response profile of LGN model neurons is biphasic in structure, resembling the biphasic response structure of neurons in cat LGN. Moreover, the model displays a specific pattern of influence of feedback, where LGN receptive fields that are aligned over a simple cell receptive field zone of the same polarity decrease their responses while neurons of opposite polarity increase their responses with feedback. This phase-reversed pattern of influence was recently observed in neurophysiology. These results corroborate the idea that predictive feedback is a general coding strategy in the brain.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. Peter Kok
2. S. Shipp

In Cognitive Neuroscience

1. Floris P. de Lange
2. Micha Heilbron
3. Peter Kok
4. S. Shipp
5. James C. R. Whittington
6. Rafal Bogacz

Citations in the corpus

• Theory of Mind: A Neural Prediction Problem (2013, neutral): “By hypothesis, predictions codes are more precise, more computationally efficient, and less noisy than error codes (Friston, 2005; Jehee and Ballard, 2009; Rao and Ballard, 1999; Spratling, 2008).”
• Neural Elements for Predictive Coding (2016, background): “…can be considered as a prediction of the relative lightness of that same element of the retinal image; if it acts to inhibit an LGN ON-center cell, this can be considered as generating an error signal, by subtracting the prediction from the incoming sensory data (Jehee and Ballard, 2009).”
• How Do Expectations Shape Perception? (2018, background): “First, extra-classical receptive field effects, both in space [5,125] and time [126], are readily explained by predictive coding as originating from predictive feedback.”
• Less Is More: Expectation Sharpens Representations in the Primary Visual Cortex (2012, unknown): “It should be noted that both these mechanisms are incorporated in a more recent model of predictive coding (Friston, 2005), which posits two functionally distinct subpopulations of neurons, encoding the conditional expectations of perceptual causes and the prediction error, respectively (Jehee and B”