In both neuroscience and artificial intelligence, popular functional frameworks and neural network formulations operate by making use of extrinsic error measurements and global learning algorithms. Through a set of conjectures based on evolutionary insights on the origin of cellular adaptive mechanisms, we reinterpret the core meaning of sensory signals to allow the brain to be interpreted as a negative feedback control system, and show how this could lead to local learning algorithms without the need for global error correction metrics. Thereby, a sufficiently good minima in sensory activity can be the complete reward signal of the network, as well as being both necessary and sufficient for biological learning to arise. We show that this method of learning was likely already present in the earliest unicellular life forms on earth. We show evidence that the same principle holds and scales to multicellular organisms where it in addition can lead to division of labour between cells. Available evidence shows that the evolution of the nervous system likely was an adaptation to more effectively communicate intercellular signals to support such division of labour. We therefore propose that the same learning principle that evolved already in the earliest unicellular life forms, i.e. negative feedback control of externally and internally generated sensor signals, has simply been scaled up to become a fundament of the learning we see in biological brains today. We illustrate diverse biological settings, from the earliest unicellular organisms to humans, where this operational principle appears to be a plausible interpretation of the meaning of sensor signals in biology, and how this relates to current neuroscientific theories and findings.

We study the application of a neural network architecture for identifying charged particle trajectories via unsupervised learning of delays and synaptic weights using a spike-time-dependent plasticity rule. In the considered model, the neurons receive time-encoded information on the position of particle hits in a tracking detector for a particle collider, modeled according to the geometry of the Compact Muon Solenoid Phase II detector. We show how a spiking neural network is capable of successfully identifying in a completely unsupervised way the signal left by charged particles in the presence of conspicuous noise from accidental or combinatorial hits. These results open the way to applications of neuromorphic computing to particle tracking, motivating further studies into its potential for real-time, low-power particle tracking in future high-energy physics experiments.

Tactile afferents such as (RA), and Pacinian (PC) afferents that respond to external stimuli enable complicated actions such as grasping, stroking and identifying an object. To understand the tactile sensation induced by these actions deeply, the activities of the tactile afferents need to be revealed. For this purpose, we develop a computational model for each tactile afferent for vibration stimuli, combining finite element analysis finite element method (FEM) analysis and a leaky integrate-and-fire model that represents the neural characteristics. This computational model can easily estimate the neural activities of the tactile afferents without measuring biological data. Skin deformation calculated using FEM analysis is substituted into the integrate-and-fire model as current input to calculate the membrane potential of each tactile afferent. We optimized parameters in the integrate-and-fire models using reported biological data. Then, we calculated the responses of the numerical models to sinusoidal, diharmonic, and white-noise-like mechanical stimuli to validate the proposed numerical models. From the result, the computational models well reproduced the neural responses to vibration stimuli such as sinusoidal, diharmonic, and noise stimuli and compare favorably with the similar computational models that can simulate the responses to vibration stimuli. Introduction Our tactile senses can perceive not only the shape and material of an object but also the texture of an object, enabling us to perform actions such as grasping, stroking, and identifying an object. Tactile afferents located in the skin that respond to external stimuli enable these complicated actions. Usually, sensory evaluations are performed to interpret the tactile sensation induced by these actions. To understand the perceived tactile sensation quantitatively, it is necessary to reveal the relationship between the skin deformation induced by an object and the activities of tactile afferents in the skin. Of note, there are two possible methods to understand how the tactile afferents are activated: the first is to directly measure the action potential of tactile afferents by inserting electrodes into nerve fibers [1-3].

FINALIST Riccardo Beltramo Riccardo Beltramo received his undergraduate degree from the University of Turin and a Ph.D. from the Italian Institute of Technology. After his doctoral training, Beltramo joined the Howard Hughes Medical Institute at the University of California, San Diego and the University of California, San Francisco, where he is completing his postdoctoral work. He studies sensory perception in the mouse visual system, focusing on understanding how cortical and subcortical neural circuits process visual information to drive behavior. [www.sciencemag.org/content/370/6512/46.2][1] In the mid—19th century, Bartolomeo Panizza, a professor at the University of Pavia, observed patients who became blind after a stroke in the posterior part of their brain and made a bold claim: Visual function is localized in the cerebral cortex ([ 1 ][2]). Given that widely accepted theories at that time assumed that all parts of the brain equally contributed to every mental activity, the idea of localized function—a fundamental pillar of modern neuroscience—was truly revolutionary. Performing targeted cortical ablations in animals, Panizza confirmed his hypothesis of a cortical region dedicated to visual processing, which became known as the primary visual cortex, or V1. V1 receives retinal input through the dorsolateral geniculate nucleus of the thalamus and extracts basic features from the visual world ([ 2 ][3]). It then projects to a constellation of higher visual cortices that compute more complex aspects of the visual scene ([ 2 ][3]). The classical hierarchical model of cortical processing implies that visual responses in downstream areas depend on the activity of V1 ([ 3 ][4]). Indeed, in multiple species, from cats to rodents to primates, lesions of V1 severely impair the visual responses of all known higher visual cortices ([ 3 ][4]–[ 7 ][5]). Thus, V1 has long been considered the driver of all visually evoked activity in the cortex. Panizza's pioneering studies and our mutual Italian ancestry inspired my postdoctoral research. I began by characterizing the anatomy, function, and gene expression of V1 cells in the mouse visual cortex, focusing on V1 neurons that target higher visual cortices ([ 8 ][6]). Fascinated by the hierarchical organization of the cortex ([ 3 ][4]), I wanted to determine whether visual responses in downstream visual areas depend on V1 activity, as posited by traditional dogma. Taking advantage of the optogenetic tools available for use in mice, I systematically verified the effects of silencing V1 on the responses of downstream visual cortices ([ 9 ][7]). I discovered that one of them—the postrhinal cortex (POR)—was minimally affected upon V1 silencing. The POR is a lateral-temporal cortex ([ 10 ][8]) known to be innervated by V1 axons ([ 11 ][9]). If this region does not receive visual input from V1, I wondered, where does this information come from? The dorsolateral geniculate nucleus relays retinal information to V1 and also projects to higher visual cortices ([ 12 ][10]). To test whether the POR receives direct geniculate input, I injected it with retrograde tracers. I found that the POR did not receive geniculate afferents but was heavily targeted by another thalamic nucleus: the caudal pulvinar. But which structure relayed visual information to the caudal pulvinar and POR? In 2019, less than 100 miles from the laboratory where Panizza discovered V1, an Italian neurophysiologist at the University of Torino received a threatening letter. Enclosed inside was a bullet. The researcher, neuroscientist Marco Tamietto, had been targeted by animal rights activists for experiments involving V1 microlesions in primates ([ 13 ][11]). His studies focused on an enigmatic condition referred to as “blindsight” ([ 14 ][12]). Blindsighted patients are clinically blind from V1 lesions. However, they still respond to moving stimuli without consciously perceiving them . This intriguing phenomenon is believed to depend on a phylogenetically ancient structure called the superior colliculus (SC) ([ 15 ][13]). The SC, which is also found in nonmammalian vertebrates, receives direct input from the eye and projects to the caudal pulvinar ([ 16 ][14]). Could this ancestral structure drive the visual processing of the POR? Using anterograde transsynaptic viral tracers ([ 17 ][15]), I established a disynaptic connection between the SC and the POR and found that the caudal pulvinar neurons targeted by collicular axons directly innervate the POR. But is this the route taken by visual signals to activate the POR independently of V1? I needed functional evidence to answer this question. I optogenetically and pharmacologically silenced the SC to determine whether this perturbation would affect the POR's response to visual stimuli. Strikingly, collicular silencing abolished visual responses in the POR. My findings redefine the POR as a new cortical primary entry point for visual information independent of V1 and uncover a cortical area dedicated to collicular input ([ 9 ][7]). But why are there two cortical entry points for visual information? Does the POR extract specific information that is not already captured by V1? Seminal experiments on amphibians ([ 18 ][16]) have shown that the SC (called “optic tectum” in nonmammals) exhibits robust responses to small moving objects; think flies crossing the visual field of a frog. This feature earned the optic tectum the name “bug-detector” ([ 18 ][16]). If the distinctive collicular response properties are transferred to the POR, I reasoned, neurons in this region should detect small moving objects, perhaps even better than V1 neurons. To test this hypothesis, I recorded POR and V1 responses to small moving stimuli presented to head-fixed mice. POR neurons significantly outperformed V1 cells in distinguishing the linear motion of small objects. What is the use of such neurons? Perhaps the POR's exquisite sensitivity to motion facilitates the detection of movement while V1 facilitates the discrimination of the nature of the moving object, telling us, for example, whether it is a beautiful butterfly or a dangerous hornet. The POR might also be involved in blindsight because blindsighted people maintain visual responses to moving stimuli in lateral-temporal cortices ([ 19 ][17]), where the POR resides. Almost two centuries after Panizza's discovery, my findings demonstrate the existence of another primary visual cortex. Evolutionary anatomists have theorized that in a hypothetical ancestral vertebrate, the collicular pathway was the original link connecting the eye to the cortex, before the geniculate-V1 pathway development ([ 20 ][18]). If that is indeed the case, it is tempting to regard the POR as our ancestral primary visual cortex. 1. [↵][19]1. S. Zago, 2. M. Nurra, 3. G. Scarlato, 4. V. Silani , Arch. Neurol. 57, 1642 (2000). [OpenUrl][20][PubMed][21] 2. [↵][22]1. S. M. Sherman, 2. R. W. Guillery , Exploring the Thalamus and Its Role in Cortical Function MIT Press, (2001). 3. [↵][23]1. D. J. Felleman, 2. D. C. Van Essen , Cereb. Cortex 1, 1 (1991). [OpenUrl][24][CrossRef][25][PubMed][26][Web of Science][27] 4. 1. P. Girard, 2. P. A. Salin, 3. J. Bullier , J. Neurophysiol. 66, 1493 (1991). [OpenUrl][28][CrossRef][29][PubMed][30][Web of Science][31] 5. 1. S. Molotchnikoff, 2. F. Hubert , Brain Res. 510, 223 (1990). [OpenUrl][32][CrossRef][33][PubMed][34] 6. 1. P. H. Schiller, 2. J. G. 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We study an encoding/decoding mechanism accounting for the relative spike timing of the signals propagating from peripheral nerve fibers to second-order somatosensory neurons in the cuneate nucleus (CN). The CN is modeled as a population of spiking neurons receiving as inputs the spatiotemporal responses of real mechanoreceptors obtained via microneurography recordings in humans. The efficiency of the haptic discrimination process is quantified by a novel definition of entropy that takes into full account the metrical properties of the spike train space. This measure proves to be a suitable decoding scheme for generalizing the classical Shannon entropy to spike-based neural codes. It permits an assessment of neurotransmission in the presence of a large output space (i.e. hundreds of spike trains) with 1 ms temporal precision. It is shown that the CN population code performs a complete discrimination of 81 distinct stimuli already within 35 ms of the first afferent spike, whereas a partial discrimination (80% of the maximum information transmission) is possible as rapidly as 15 ms. This study suggests that the CN may not constitute a mere synaptic relay along the somatosensory pathway but, rather, it may convey optimal contextual accounts (in terms of fast and reliable information transfer) of peripheral tactile inputs to downstream structures of the central nervous system.

In macaque inferotemporal cortex (IT), neurons have been found to respond selectively to complex shapes while showing broad tuning ("invariance") with respect to stimulus transformations such as translation and scale changes and a limited tuning to rotation in depth.

In macaque inferotemporal cortex (IT), neurons have been found to respond selectively to complex shapes while showing broad tuning ("invariance") with respect to stimulus transformations such as translation and scale changes and a limited tuning to rotation in depth.

In macaque inferotemporal cortex (IT), neurons have been found to respond selectivelyto complex shapes while showing broad tuning ("invariance") withrespect to stimulus transformations such as translation and scale changes and a limited tuning to rotation in depth.

A great deal of interest has recently been focused on theories concerning parallel distributed processing in central nervous systems. In particular, many researchers have become very interested in the structure and function of "computational maps" in sensory systems. As defined in a recent review (Knudsen et al, 1987), a "map" is an array of nerve cells, within which there is a systematic variation in the "tuning" of neighboring cells for a particular parameter. For example, the projection from retina to visual cortex is a relatively simple topographic map; each cortical hypercolumn itself contains a more complex "computational" map of preferred line orientation representing the angle of tilt of a simple line stimulus. The overall goal of the research in my lab is to determine how a relatively complex mapped sensory system extracts and encodes information from external stimuli.

A great deal of interest has recently been focused on theories concerning parallel distributed processing in central nervous systems. In particular, many researchers have become very interested in the structure and function of "computational maps" in sensory systems. As defined in a recent review (Knudsen et al, 1987), a "map" is an array of nerve cells, within which there is a systematic variation in the "tuning" of neighboring cells for a particular parameter. For example, the projection from retina to visual cortex is a relatively simple topographic map; each cortical hypercolumn itself contains a more complex "computational" map of preferred line orientation representing the angle of tilt of a simple line stimulus. The overall goal of the research in my lab is to determine how a relatively complex mapped sensory system extracts and encodes information from external stimuli.