What is he best known for?
The fields of study he is best known for:
- Retina
- Artificial intelligence
- Neuron
His scientific interests lie mostly in Retina, Neuroscience, Retinal, Anatomy and Retinal ganglion.
Fred Rieke combines Retina and Voltage range in his studies.
Fred Rieke is studying Excitatory postsynaptic potential, which is a component of Neuroscience.
His work on Visual phototransduction, Photopigment and RPE65 as part of general Retinal research is frequently linked to Cis-trans-Isomerases and Cellular noise, thereby connecting diverse disciplines of science.
His study in Anatomy is interdisciplinary in nature, drawing from both Electroretinography, Retinal pigment epithelium, Ophthalmology, Biophysics and Adaptation.
His Retinal ganglion research integrates issues from Stimulus, Retinal ganglion cell and Patch clamp.
His most cited work include:
- Spikes: Exploring the Neural Code (2264 citations)
- READING A NEURAL CODE (899 citations)
- NATURALISTIC STIMULI INCREASE THE RATE AND EFFICIENCY OF INFORMATION TRANSMISSION BY PRIMARY AUDITORY AFFERENTS (265 citations)
What are the main themes of his work throughout his whole career to date?
Fred Rieke mainly focuses on Neuroscience, Retina, Retinal, Retinal ganglion and Artificial intelligence.
His Retina study which covers Anatomy that intersects with Photopigment.
His research integrates issues of Cone, Perception, Optics and Cell biology in his study of Retinal.
His Retinal ganglion research is multidisciplinary, incorporating perspectives in Nerve net, Inhibitory postsynaptic potential, Neuron, Visual system and Synapse.
His work on Neural coding as part of general Artificial intelligence study is frequently linked to Code, therefore connecting diverse disciplines of science.
His studies in Adaptation integrate themes in fields like Biophysics and Contrast.
He most often published in these fields:
- Neuroscience (46.34%)
- Retina (42.93%)
- Retinal (23.90%)
What were the highlights of his more recent work (between 2017-2021)?
- Neuroscience (46.34%)
- Retina (42.93%)
- Artificial intelligence (14.63%)
In recent papers he was focusing on the following fields of study:
Fred Rieke focuses on Neuroscience, Retina, Artificial intelligence, Retinal and Stimulus.
His work deals with themes such as Choline acetyltransferase, Ganglion, Receptive field and Primate, which intersect with Retina.
His Artificial intelligence study integrates concerns from other disciplines, such as Structure, Computer vision and Pattern recognition.
Fred Rieke has researched Retinal in several fields, including Cholinergic, Cholinergic neuron, Perception and Contrast.
The concepts of his Stimulus study are interwoven with issues in Large dynamic range and Ocular physiology.
His studies deal with areas such as Synapse, Biological system, Neuron and Excitatory postsynaptic potential as well as Retinal ganglion.
Between 2017 and 2021, his most popular works were:
- Parallel Processing of Rod and Cone Signals: Retinal Function and Human Perception. (22 citations)
- Receptive field center-surround interactions mediate context-dependent spatial contrast encoding in the retina (21 citations)
- S-cone photoreceptors in the primate retina are functionally distinct from L and M cones. (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Neuron
- Retina
Fred Rieke spends much of his time researching Neuroscience, Retina, Retinal, Stimulus and Primate.
In general Neuroscience study, his work on Sensory system, Retinal ganglion cell and Macaque often relates to the realm of Vertebrate, thereby connecting several areas of interest.
His work deals with themes such as Choline acetyltransferase, Sensory processing and Cholinergic neuron, which intersect with Retina.
The Retinal study combines topics in areas such as Light intensity, Adaptation, Cholinergic and Contrast.
His Stimulus research incorporates elements of Cognitive science, Large dynamic range and Ocular physiology.
His Primate research is multidisciplinary, incorporating elements of Cone, Light level, Models of neural computation and Human visual perception.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
