scholarly journals Task-specific sensory coding strategies matched to detection and discrimination behaviors in Apteronotus leptorhynchus

2017 ◽  
Author(s):  
K.M. Allen ◽  
G. Marsat
Keyword(s):  
General Principle ◽  
Neural Coding ◽  
Sensory Systems ◽  
Model Systems ◽  
Behavioral Performance ◽  
Sensory Coding ◽  
Apteronotus Leptorhynchus ◽  
Neural Codes ◽  
Coding Strategies ◽  
Behavioral Tasks

ABSTRACTAll sensory systems must reliably translate information about the environment into a neural code, mediating perception. The most relevant aspects of stimuli may change as behavioral context changes, making efficient encoding of information more challenging. Sensory systems must balance rapid detection of a stimulus with perception of fine details that enable discrimination between similar stimuli. We show that in a species of weakly electric fish, Apteronotus leptorhynchus, two coding strategies are employed for these separate behavioral tasks. Using communication signals produced in different contexts, we demonstrate a strong correlation between neural coding strategies and behavioral performance on a discrimination task. Extracellular recordings of pyramidal cells within the electrosensory lateral line lobe of alert fish show two distinct response patterns, either burst discharges with little variation between different signals of the same category, or a graded, heterogeneous response that contains enough information to discriminate between signals with slight variations. When faced with a discrimination-based task, the behavioral performance of the fish closely matches predictions based on coding strategy. Comparisons of these results with neural and behavioral responses observed in other model systems suggest that our study highlights a general principle in the way different neural codes are utilized in the sensory system.SIGNIFICANCE STATEMENTResearch relating the structure of stimuli to the response of sensory neurons has left us with a detailed understanding of how different neural codes can represent information. Although various aspects of neural responses have been related to perceptual abilities, general principles relating behavioral tasks to sensory coding strategies are lacking. A major distinction can be made between signals that must simply be detected versus stimuli that must also be finely discriminated and evaluated. We show that these two different perceptual tasks are systematically matched by distinct neural coding strategies and we argue that our study identifies a general principle that is observed in various sensory systems.Conflict of interest statementThe authors declare no competing financial interests.

scholarly journals Neurophysiology goes wild: from exploring sensory coding in sound proof rooms to natural environments

2021 ◽  
Vol 207 (3) ◽  
pp. 303-319
Author(s):  
Heiner Römer
Keyword(s):  
Sensory System ◽  
Sensory Systems ◽  
Natural Environments ◽  
Sensory Coding ◽  
Noise Levels ◽  
Sensory Physiology ◽  
Sound Localisation ◽  
Physical Environments ◽  
History Of ◽  
Adaptive Behaviours

AbstractTo perform adaptive behaviours, animals have to establish a representation of the physical “outside” world. How these representations are created by sensory systems is a central issue in sensory physiology. This review addresses the history of experimental approaches toward ideas about sensory coding, using the relatively simple auditory system of acoustic insects. I will discuss the empirical evidence in support of Barlow’s “efficient coding hypothesis”, which argues that the coding properties of neurons undergo specific adaptations that allow insects to detect biologically important acoustic stimuli. This hypothesis opposes the view that the sensory systems of receivers are biased as a result of their phylogeny, which finally determine whether a sound stimulus elicits a behavioural response. Acoustic signals are often transmitted over considerable distances in complex physical environments with high noise levels, resulting in degradation of the temporal pattern of stimuli, unpredictable attenuation, reduced signal-to-noise levels, and degradation of cues used for sound localisation. Thus, a more naturalistic view of sensory coding must be taken, since the signals as broadcast by signallers are rarely equivalent to the effective stimuli encoded by the sensory system of receivers. The consequences of the environmental conditions for sensory coding are discussed.

scholarly journals Neural coding strategies and mechanisms of competition

2004 ◽  
Vol 5 (2) ◽  
pp. 93-117 ◽  
Author(s):  
M.W. Spratling ◽  
M.H. Johnson
Keyword(s):  
Neural Coding ◽  
Coding Strategies

scholarly journals Is coding a relevant metaphor for the brain?

2018 ◽  
Vol 42 ◽  
Author(s):  
Romain Brette
Keyword(s):  
Brain Function ◽  
Neural Coding ◽  
Causal Structure ◽  
Linear Chain ◽  
Neural Codes ◽  
Experimental Parameters ◽  
Representational Power ◽  
Causal Structures ◽  
Perceptual Systems ◽  
The Brain

Abstract “Neural coding” is a popular metaphor in neuroscience, where objective properties of the world are communicated to the brain in the form of spikes. Here I argue that this metaphor is often inappropriate and misleading. First, when neurons are said to encode experimental parameters, the neural code depends on experimental details that are not carried by the coding variable (e.g., the spike count). Thus, the representational power of neural codes is much more limited than generally implied. Second, neural codes carry information only by reference to things with known meaning. In contrast, perceptual systems must build information from relations between sensory signals and actions, forming an internal model. Neural codes are inadequate for this purpose because they are unstructured and therefore unable to represent relations. Third, coding variables are observables tied to the temporality of experiments, whereas spikes are timed actions that mediate coupling in a distributed dynamical system. The coding metaphor tries to fit the dynamic, circular, and distributed causal structure of the brain into a linear chain of transformations between observables, but the two causal structures are incongruent. I conclude that the neural coding metaphor cannot provide a valid basis for theories of brain function, because it is incompatible with both the causal structure of the brain and the representational requirements of cognition.

General Principles for Sensory Coding

2018 ◽  
Author(s):  
Tatyana O. Sharpee
Keyword(s):  
Sensory System ◽  
Sensory Systems ◽  
Multiple Delays ◽  
Minimal Amount ◽  
Sensory Coding ◽  
Trade Offs ◽  
Almost All ◽  
Comprehensive Framework ◽  
State Of The Environment ◽  
The Impact

Sensory systems exist to provide an organism with information about the state of the environment that can be used to guide future actions and decisions. Remarkably, two conceptually simple yet general theorems from information theory can be used to evaluate the performance of any sensory system. One theorem states that there is a minimal amount of energy that an organism has to spend in order to capture a given amount of information about the environment. The second theorem states that the maximum rate with which the organism can acquire resources from the environment, relative to its competitors, is limited by the information this organism collects about the environment, also relative to its competitors. These two theorems provide a scaffold for formulating and testing general principles of sensory coding but leave unanswered many important practical questions of implementation in neural circuits. These implementation questions have guided thinking in entire subfields of sensory neuroscience, and include: What features in the sensory environment should be measured? Given that we make decisions on a variety of time scales, how should one solve trade-offs between making simpler measurements to guide minimal decisions vs. more elaborate sensory systems that have to overcome multiple delays between sensation and action. Once we agree on the types of features that are important to represent, how should they be represented? How should resources be allocated between different stages of processing, and where is the impact of noise most damaging? Finally, one should consider trade-offs between implementing a fixed strategy vs. an adaptive scheme that readjusts resources based on current needs. Where adaptation is considered, under what conditions does it become optimal to switch strategies? Research over the past 60 years has provided answers to almost all of these questions but primarily in early sensory systems. Joining these answers into a comprehensive framework is a challenge that will help us understand who we are and how we can make better use of limited natural resources.

Local versus distributed: A poor taxonomy of neural coding strategies

2004 ◽  
Vol 27 (5) ◽  
pp. 700-702
Author(s):  
Michael W. Spratling
Keyword(s):  
Neural Coding ◽  
Neural Representation ◽  
Target Article ◽  
Coding Strategies

Page is to be congratulated for challenging some misconceptions about neural representation. However, his target article, and the commentaries to it, highlight that the terms “local” and “distributed” are open to misinterpretation. These terms provide a poor description of neural coding strategies and a better taxonomy might resolve some of the issues.

scholarly journals Electrosensory processing in Apteronotus albifrons: implications for general and specific neural coding strategies across wave-type weakly electric fish species

2016 ◽  
Vol 116 (6) ◽  
pp. 2909-2921 ◽  
Author(s):  
Diana Martinez ◽  
Michael G. Metzen ◽  
Maurice J. Chacron
Keyword(s):  
Fish Species ◽  
Related Species ◽  
Neural Coding ◽  
Electric Fish ◽  
Weakly Electric Fish ◽  
Closely Related Species ◽  
Tuning Curves ◽  
Baseline Activity ◽  
Coding Strategies ◽  
Weakly Electric

Understanding how the brain processes sensory input to generate behavior remains an important problem in neuroscience. Towards this end, it is useful to compare results obtained across multiple species to gain understanding as to the general principles of neural coding. Here we investigated hindbrain pyramidal cell activity in the weakly electric fish Apteronotus albifrons. We found strong heterogeneities when looking at baseline activity. Additionally, ON- and OFF-type cells responded to increases and decreases of sinusoidal and noise stimuli, respectively. While both cell types displayed band-pass tuning, OFF-type cells were more broadly tuned than their ON-type counterparts. The observed heterogeneities in baseline activity as well as the greater broadband tuning of OFF-type cells were both similar to those previously reported in other weakly electric fish species, suggesting that they constitute general features of sensory processing. However, we found that peak tuning occurred at frequencies ∼15 Hz in A. albifrons, which is much lower than values reported in the closely related species Apteronotus leptorhynchus and the more distantly related species Eigenmannia virescens. In response to stimuli with time-varying amplitude (i.e., envelope), ON- and OFF-type cells displayed similar high-pass tuning curves characteristic of fractional differentiation and possibly indicate optimized coding. These tuning curves were qualitatively similar to those of pyramidal cells in the closely related species A. leptorhynchus. In conclusion, comparison between our and previous results reveals general and species-specific neural coding strategies. We hypothesize that differences in coding strategies, when observed, result from different stimulus distributions in the natural/social environment.

scholarly journals Ambiguity and nonidentifiability in the statistical analysis of neural codes

2015 ◽  
Vol 112 (20) ◽  
pp. 6455-6460 ◽  
Author(s):  
Asohan Amarasingham ◽  
Stuart Geman ◽  
Matthew T. Harrison
Keyword(s):  
Statistical Analysis ◽  
Firing Rate ◽  
Statistical Modeling ◽  
Neural Coding ◽  
Experimental Studies ◽  
Spike Timing ◽  
Statistical Interpretation ◽  
Neural Codes ◽  
Firing Rates ◽  
Timing Variability

Many experimental studies of neural coding rely on a statistical interpretation of the theoretical notion of the rate at which a neuron fires spikes. For example, neuroscientists often ask, “Does a population of neurons exhibit more synchronous spiking than one would expect from the covariability of their instantaneous firing rates?” For another example, “How much of a neuron’s observed spiking variability is caused by the variability of its instantaneous firing rate, and how much is caused by spike timing variability?” However, a neuron’s theoretical firing rate is not necessarily well-defined. Consequently, neuroscientific questions involving the theoretical firing rate do not have a meaning in isolation but can only be interpreted in light of additional statistical modeling choices. Ignoring this ambiguity can lead to inconsistent reasoning or wayward conclusions. We illustrate these issues with examples drawn from the neural-coding literature.

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