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Computational neuroscience is a branch of neuroscience which employs mathematical models, computer simulations, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system.
The sensory nervous system is a part of the nervous system responsible for processing sensory information. A sensory system consists of sensory neurons, neural pathways, and parts of the brain involved in sensory perception. Commonly recognized sensory systems are those for vision, hearing, touch, taste, smell, and balance. Senses are transducers from the physical world to the realm of the mind where people interpret the information, creating their perception of the world around them.
The olfactory bulb is a neural structure of the vertebrate forebrain involved in olfaction, the sense of smell. It sends olfactory information to be further processed in the amygdala, the orbitofrontal cortex (OFC) and the hippocampus where it plays a role in emotion, memory and learning. The bulb is divided into two distinct structures: the main olfactory bulb and the accessory olfactory bulb. The main olfactory bulb connects to the amygdala via the piriform cortex of the primary olfactory cortex and directly projects from the main olfactory bulb to specific amygdala areas. The accessory olfactory bulb resides on the dorsal-posterior region of the main olfactory bulb and forms a parallel pathway. Destruction of the olfactory bulb results in ipsilateral anosmia, while irritative lesions of the uncus can result in olfactory and gustatory hallucinations.
A place cell is a kind of pyramidal neuron in the hippocampus that becomes active when an animal enters a particular place in its environment, which is known as the place field. Place cells are thought to act collectively as a cognitive representation of a specific location in space, known as a cognitive map. Place cells work with other types of neurons in the hippocampus and surrounding regions to perform this kind of spatial processing. They have been found in a variety of animals, including rodents, bats, monkeys and humans.
Matteo Carandini is a neuroscientist who studies the visual system. He is currently a professor at University College London, where he co-directs the Cortical Processing Laboratory with Kenneth D Harris.
David J. Heeger is an American neuroscientist, psychologist, computer scientist, data scientist, and entrepreneur. He is a professor at New York University, Chief Scientific Officer of Statespace Labs, and Chief Scientific Officer and co-founder of Epistemic AI.
The efficient coding hypothesis was proposed by Horace Barlow in 1961 as a theoretical model of sensory coding in the brain. Within the brain, neurons communicate with one another by sending electrical impulses referred to as action potentials or spikes. One goal of sensory neuroscience is to decipher the meaning of these spikes in order to understand how the brain represents and processes information about the outside world. Barlow hypothesized that the spikes in the sensory system formed a neural code for efficiently representing sensory information. By efficient Barlow meant that the code minimized the number of spikes needed to transmit a given signal. This is somewhat analogous to transmitting information across the internet, where different file formats can be used to transmit a given image. Different file formats require different number of bits for representing the same image at given distortion level, and some are better suited for representing certain classes of images than others. According to this model, the brain is thought to use a code which is suited for representing visual and audio information representative of an organism's natural environment.
Neural coding is a neuroscience field concerned with characterising the hypothetical relationship between the stimulus and the individual or ensemble neuronal responses and the relationship among the electrical activity of the neurons in the ensemble. Based on the theory that sensory and other information is represented in the brain by networks of neurons, it is thought that neurons can encode both digital and analog information.
A topographic map is the ordered projection of a sensory surface, like the retina or the skin, or an effector system, like the musculature, to one or more structures of the central nervous system. Topographic maps can be found in all sensory systems and in many motor systems.
Complex cells can be found in the primary visual cortex (V1), the secondary visual cortex (V2), and Brodmann area 19 (V3).
Repetition priming refers to improvements in a behavioural response when stimuli are repeatedly presented. The improvements can be measured in terms of accuracy or reaction time, and can occur when the repeated stimuli are either identical or similar to previous stimuli. These improvements have been shown to be cumulative, so as the number of repetitions increases the responses get continually faster up to a maximum of around seven repetitions. These improvements are also found when the repeated items are changed slightly in terms of orientation, size and position. The size of the effect is also modulated by the length of time the item is presented for and the length time between the first and subsequent presentations of the repeated items.
The sense of smell, or olfaction, is the special sense through which smells are perceived. The sense of smell has many functions, including detecting desirable foods, hazards, and pheromones, and plays a role in taste.
Neural decoding is a neuroscience field concerned with the hypothetical reconstruction of sensory and other stimuli from information that has already been encoded and represented in the brain by networks of neurons. Reconstruction refers to the ability of the researcher to predict what sensory stimuli the subject is receiving based purely on neuron action potentials. Therefore, the main goal of neural decoding is to characterize how the electrical activity of neurons elicit activity and responses in the brain.
Joseph Anthony Movshon is an American neuroscientist. He has made contributions to the understanding of the brain mechanisms that represent the form and motion of objects, and the way these mechanisms contribute to perceptual judgments and visually guided movement. He is a founding co-editor of the Annual Review of Vision Science.
Surround suppression is where the relative firing rate of a neuron may under certain conditions decrease when a particular stimulus is enlarged. It has been observed in electrophysiology studies of the brain and has been noted in many sensory neurons, most notably in the early visual system. Surround suppression is defined as a reduction in the activity of a neuron in response to a stimulus outside its classical receptive field.
In neuroscience, predictive coding is a theory of brain function which postulates that the brain is constantly generating and updating a "mental model" of the environment. According to the theory, such a mental model is used to predict input signals from the senses that are then compared with the actual input signals from those senses. With the rising popularity of representation learning, the theory is being actively pursued and applied in machine learning and related fields.
In neuroscience, a neurometric function is a mathematical formula relating the activity of brain cells to aspects of an animal's sensory experience or motor behavior. Neurometric functions provide a quantitative summary of the neural code of a particular brain region.
Li Zhaoping, born in Shanghai, China, is a neuroscientist at the University of Tübingen in Germany. She is the only woman to win the first place in CUSPEA, an annual national physics competition in China, during CUSPEA's 10 year history (1979–1989). She proposed V1 Saliency Hypothesis (V1SH), and is the author of Understanding vision: theory, models, and data published by Oxford University Press.
Laura Busse is a German neuroscientist and professor of Systemic Neuroscience within the Division of Neurobiology at the Ludwig Maximilian University of Munich. Busse's lab studies context-dependent visual processing in mouse models by performing large scale in vivo electrophysiological recordings in the thalamic and cortical circuits of awake and behaving mice.
Carsen Stringer is an American computational neuroscientist and Group Leader at the Howard Hughes Medical Institute Janelia Research Campus. Stringer uses machine learning and deep neural networks to visualize large scale neural recordings and then probe the neural computations that give rise to visual processing in mice. Stringer has also developed several novel software packages that enable cell segmentation and robust analyses of neural recordings and mouse behavior.
References
- 1 2 Carandini, M.; Heeger, D. J. (2011). "Normalization as a canonical neural computation". Nature Reviews Neuroscience. 13 (1): 51–62. doi:10.1038/nrn3136. PMC 3273486 . PMID 22108672.
- ↑ Heeger, D. J. (1992). "Normalization of cell responses in cat striate cortex". Visual Neuroscience. 9 (2): 181–197. doi:10.1017/S0952523800009640. PMID 1504027.
- ↑ Carandini, M; Heeger, DJ; Movshon, JA (1997). "Linearity and normalization in simple cells of the macaque primary visual cortex". Journal of Neuroscience. 17 (21): 8621–44. doi: 10.1523/JNEUROSCI.17-21-08621.1997 . PMC 6573724 . PMID 9334433.
- ↑ Olsen SR, Bhandawat V, Wilson R (2011). "Divisive normalization in olfactory population codes". Neuron. 66 (2): 287–299. doi:10.1016/j.neuron.2010.04.009. PMC 2866644 . PMID 20435004.
{{cite journal}}: CS1 maint: multiple names: authors list (link) - ↑ Bhatia A, Moza S, Bhalla US (2019). "Precise excitation-inhibition balance controls gain and timing in the hippocampus". eLife. 8. doi:10.7554/eLife.43415. PMC 6517031 . PMID 31021319.
{{cite journal}}: CS1 maint: multiple names: authors list (link) - ↑ Schwartz, O; Simoncelli, EP (2001). "Natural signal statistics and sensory gain control". Nature neuroscience. 4 (8): 819–25. doi: 10.1038/90526 . PMID 11477428.
- ↑ Bucher, SF; Brandenburger, AM (2022). "Divisive normalization is an efficient code for multivariate Pareto-distributed environments". Proceedings of the National Academy of Sciences of the United States of America. 119 (40): e2120581119. doi: 10.1073/pnas.2120581119 . PMID 36161961.
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