scholarly journals Orthogonal but linked neural codes for value

2021 ◽  
Author(s):  
David J Maisson ◽  
Justin M Fine ◽  
Seng Bum Michael Yoo ◽  
Tyler Daniel Cash-Padgett ◽  
Maya Zhe Wang ◽  
...  
Keyword(s):  
Choice Task ◽  
Single Neurons ◽  
Neural Codes ◽  
Population Responses ◽  
Coordinated Action ◽  
Population Codes ◽  
Linear Transform ◽  
Core Value ◽  
Value Coding ◽  
The Brain

Our ability to effectively choose between dissimilar options implies that information regarding the options values must be available, either explicitly or implicitly, in the brain. Explicit realizations of value involve single neurons whose responses depend on value and not on the specific features that determine it. Implicit realizations, by contrast, come from the coordinated action of neurons that encode specific features. One signature of implicit value coding is that population responses to offers with the same value but different features should occupy semi- or fully orthogonal neural subspaces that are nonetheless linked. Here, we examined responses of neurons in six core value-coding areas in a choice task with risky and safe options. Using stricter criteria than some past studies have used, we find, surprisingly, no evidence for abstract value neurons (i.e., neurons with the response to equally valued risky and safe options) in any of these regions. Moreover, population codes for value resided in orthogonal subspaces; these subspaces were linked through a linear transform of each of their constituent subspaces. These results suggest that in all six regions, populations of neurons embed value implicitly in a distributed population.

scholarly journals Subspace alignment as a mechanism for binding

2021 ◽  
Author(s):  
Justin M Fine ◽  
Seng Bum Michael Yoo ◽  
Becket Ebitz ◽  
Ben Hayden
Keyword(s):  
Rhesus Macaques ◽  
Risky Choice ◽  
Choice Task ◽  
Single Neurons ◽  
Coding Regions ◽  
Left And Right ◽  
Per Se ◽  
Value Coding ◽  
Functional Partitioning ◽  
The Brain

To choose between options, we must solve two important binding problems. First, the features that determine each options values must be appropriately combined and kept separate from the corresponding features of other options. Second, options must be associated with the specific actions needed to select them. We hypothesized that the brain solves these problems through use of aligned (for bound dimensions) and orthogonal (for separated dimensions) population subspaces. We examined responses of single neurons in six putative value-coding regions in rhesus macaques performing a risky choice task. In all areas, single neurons encode the features that define the value of each option (stakes and probability) but only very weakly encode value per se. However, the coding dimensions associated with these features are aligned on a single subspace, from which a strong emergent value signal can be read out. Moreover, all six regions use nearly orthogonal subspaces for the left and right options, thereby linking options to their position in space, implementing functional partitioning, and reducing the possibility of misbinding. These results provide a new solution to the neuroeconomic binding problems and suggest that other forms of binding may work through similar principles.

scholarly journals Decision-related feedback in visual cortex lacks spatial selectivity

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Katrina R. Quinn ◽  
Lenka Seillier ◽  
Daniel A. Butts ◽  
Hendrikje Nienborg
Keyword(s):  
Contextual Information ◽  
Relevant Information ◽  
Common Mechanism ◽  
Spatial Selectivity ◽  
Population Responses ◽  
Stimulus Choice ◽  
Visual Areas ◽  
Computational Work ◽  
Feature Based ◽  
The Brain

AbstractFeedback in the brain is thought to convey contextual information that underlies our flexibility to perform different tasks. Empirical and computational work on the visual system suggests this is achieved by targeting task-relevant neuronal subpopulations. We combine two tasks, each resulting in selective modulation by feedback, to test whether the feedback reflected the combination of both selectivities. We used visual feature-discrimination specified at one of two possible locations and uncoupled the decision formation from motor plans to report it, while recording in macaque mid-level visual areas. Here we show that although the behavior is spatially selective, using only task-relevant information, modulation by decision-related feedback is spatially unselective. Population responses reveal similar stimulus-choice alignments irrespective of stimulus relevance. The results suggest a common mechanism across tasks, independent of the spatial selectivity these tasks demand. This may reflect biological constraints and facilitate generalization across tasks. Our findings also support a previously hypothesized link between feature-based attention and decision-related activity.

scholarly journals Primate prefrontal neurons signal economic risk derived from the statistics of recent reward experience

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Fabian Grabenhorst ◽  
Ken-Ichiro Tsutsui ◽  
Shunsuke Kobayashi ◽  
Wolfram Schultz
Keyword(s):  
Dorsolateral Prefrontal Cortex ◽  
Choice Task ◽  
Support Vector ◽  
Probabilistic Choice ◽  
Economic Decisions ◽  
Decision Variables ◽  
Dorsolateral Prefrontal ◽  
Process Risk ◽  
The Brain ◽  
Statistical Variance

Risk derives from the variation of rewards and governs economic decisions, yet how the brain calculates risk from the frequency of experienced events, rather than from explicit risk-descriptive cues, remains unclear. Here, we investigated whether neurons in dorsolateral prefrontal cortex process risk derived from reward experience. Monkeys performed in a probabilistic choice task in which the statistical variance of experienced rewards evolved continually. During these choices, prefrontal neurons signaled the reward-variance associated with specific objects (‘object risk’) or actions (‘action risk’). Crucially, risk was not derived from explicit, risk-descriptive cues but calculated internally from the variance of recently experienced rewards. Support-vector-machine decoding demonstrated accurate neuronal risk discrimination. Within trials, neuronal signals transitioned from experienced reward to risk (risk updating) and from risk to upcoming choice (choice computation). Thus, prefrontal neurons encode the statistical variance of recently experienced rewards, complying with formal decision variables of object risk and action risk.

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.

scholarly journals STDP Forms Associations between Memory Traces in Networks of Spiking Neurons

2019 ◽  
Vol 30 (3) ◽  
pp. 952-968
Author(s):  
Christoph Pokorny ◽  
Matias J Ison ◽  
Arjun Rao ◽  
Robert Legenstein ◽  
Christos Papadimitriou ◽  
...  
Keyword(s):  
Brain Function ◽  
Spatial Information ◽  
Spike Timing ◽  
Spiking Neurons ◽  
Recurrent Networks ◽  
Neural Codes ◽  
Memory Traces ◽  
Conflicting Evidence ◽  
Excitability Of Neurons ◽  
The Brain

Abstract Memory traces and associations between them are fundamental for cognitive brain function. Neuron recordings suggest that distributed assemblies of neurons in the brain serve as memory traces for spatial information, real-world items, and concepts. However, there is conflicting evidence regarding neural codes for associated memory traces. Some studies suggest the emergence of overlaps between assemblies during an association, while others suggest that the assemblies themselves remain largely unchanged and new assemblies emerge as neural codes for associated memory items. Here we study the emergence of neural codes for associated memory items in a generic computational model of recurrent networks of spiking neurons with a data-constrained rule for spike-timing-dependent plasticity. The model depends critically on 2 parameters, which control the excitability of neurons and the scale of initial synaptic weights. By modifying these 2 parameters, the model can reproduce both experimental data from the human brain on the fast formation of associations through emergent overlaps between assemblies, and rodent data where new neurons are recruited to encode the associated memories. Hence, our findings suggest that the brain can use both of these 2 neural codes for associations, and dynamically switch between them during consolidation.

scholarly journals Inference in the Brain: Statistics Flowing in Redundant Population Codes

Neuron ◽  
2017 ◽  
Vol 94 (5) ◽  
pp. 943-953 ◽  
Author(s):  
Xaq Pitkow ◽  
Dora E. Angelaki
Keyword(s):  
Population Codes ◽  
The Brain

Brain Plasticity by Multiple Single Neurons Scattered in the Brain Cortex

2014 ◽  
Vol 6 (1) ◽  
pp. 27-32
Author(s):  
Giorgio A. Brunelli ◽  
Luisa Monini ◽  
Klaus R. H. von Wild
Keyword(s):  
Brain Cortex ◽  
Brain Plasticity ◽  
Single Neurons ◽  
The Brain

Emergence of Optimal Decoding of Population Codes Through STDP

2013 ◽  
Vol 25 (6) ◽  
pp. 1371-1407 ◽  
Author(s):  
Stefan Habenschuss ◽  
Helmut Puhr ◽  
Wolfgang Maass
Keyword(s):  
Sensory Neurons ◽  
Network Motif ◽  
Generative Models ◽  
Homeostatic Plasticity ◽  
Learning Rules ◽  
Population Codes ◽  
Large Populations ◽  
External Stimuli ◽  
The Brain ◽  
Optimal Decoding

The brain faces the problem of inferring reliable hidden causes from large populations of noisy neurons, for example, the direction of a moving object from spikes in area MT. It is known that a theoretically optimal likelihood decoding could be carried out by simple linear readout neurons if weights of synaptic connections were set to certain values that depend on the tuning functions of sensory neurons. We show here that such theoretically optimal readout weights emerge autonomously through STDP in conjunction with lateral inhibition between readout neurons. In particular, we identify a class of optimal STDP learning rules with homeostatic plasticity, for which the autonomous emergence of optimal readouts can be explained on the basis of a rigorous learning theory. This theory shows that the network motif we consider approximates expectation-maximization for creating internal generative models for hidden causes of high-dimensional spike inputs. Notably, we find that this optimal functionality can be well approximated by a variety of STDP rules beyond those predicted by theory. Furthermore, we show that this learning process is very stable and automatically adjusts weights to changes in the number of readout neurons, the tuning functions of sensory neurons, and the statistics of external stimuli.

Finding the Brain in the Nose

2020 ◽  
Vol 43 (1) ◽  
pp. 277-295
Author(s):  
David H. Brann ◽  
Sandeep Robert Datta
Keyword(s):  
Brain Function ◽  
Perception And Action ◽  
Complex Mixtures ◽  
Peripheral Population ◽  
Volatile Chemicals ◽  
Sensory Modalities ◽  
Population Codes ◽  
Receptor Array ◽  
Olfactory Stimuli ◽  
The Brain

Olfaction is fundamentally distinct from other sensory modalities. Natural odor stimuli are complex mixtures of volatile chemicals that interact in the nose with a receptor array that, in rodents, is built from more than 1,000 unique receptors. These interactions dictate a peripheral olfactory code, which in the brain is transformed and reformatted as it is broadcast across a set of highly interconnected olfactory regions. Here we discuss the problems of characterizing peripheral population codes for olfactory stimuli, of inferring the specific functions of different higher olfactory areas given their extensive recurrence, and of ultimately understanding how odor representations are linked to perception and action. We argue that, despite the differences between olfaction and other sensory modalities, addressing these specific questions will reveal general principles underlying brain function.

Reason for optimism: How a shifting focus on neural population codes is moving cognitive neuroscience beyond phrenology

2016 ◽  
Vol 39 ◽  
Author(s):  
Carolyn Parkinson ◽  
Thalia Wheatley
Keyword(s):  
Cognitive Neuroscience ◽  
Brain Function ◽  
Pattern Analysis ◽  
Brain Regions ◽  
Neural Population ◽  
Multivariate Pattern Analysis ◽  
Population Responses ◽  
Population Codes ◽  
Multivariate Pattern ◽  
Insight Into

AbstractMultivariate pattern analysis can address many of the challenges for cognitive neuroscience highlighted in After Phrenology (Anderson 2014) by illuminating the information content of brain regions and by providing insight into whether functional overlap reflects the recruitment of common or distinct computational mechanisms. Further, failing to consider submaximal but reliable population responses can lead to an overly modular account of brain function.

Sign in / Sign up

Export Citation Format

Share Document