Neural codes – Necessary but not sufficient for understanding brain function

2019 ◽  
Vol 42 ◽  
Author(s):  
Simon R. Schultz ◽  
Giuseppe P. Gava
Keyword(s):  
Information Theory ◽  
Information Processing ◽  
Control Theory ◽  
Brain Function ◽  
Neural Codes ◽  
Brain Circuits ◽  
External Signals ◽  
The Brain

Abstract Brains are information processing systems whose operational principles ultimately cannot be understood without resource to information theory. We suggest that understanding how external signals are represented in the brain is a necessary step towards employing further engineering tools (such as control theory) to understand the information processing performed by brain circuits during behaviour.

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 Entropy and the Brain: An Overview

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 917 ◽  
Author(s):  
Soheil Keshmiri
Keyword(s):  
Information Processing ◽  
Brain Function ◽  
Conscious Experience ◽  
Brain Networks ◽  
Fundamental Property ◽  
Future Research ◽  
Processing Capacity ◽  
Information Processing Capacity ◽  
Ageing Brain ◽  
The Brain

Entropy is a powerful tool for quantification of the brain function and its information processing capacity. This is evident in its broad domain of applications that range from functional interactivity between the brain regions to quantification of the state of consciousness. A number of previous reviews summarized the use of entropic measures in neuroscience. However, these studies either focused on the overall use of nonlinear analytical methodologies for quantification of the brain activity or their contents pertained to a particular area of neuroscientific research. The present study aims at complementing these previous reviews in two ways. First, by covering the literature that specifically makes use of entropy for studying the brain function. Second, by highlighting the three fields of research in which the use of entropy has yielded highly promising results: the (altered) state of consciousness, the ageing brain, and the quantification of the brain networks’ information processing. In so doing, the present overview identifies that the use of entropic measures for the study of consciousness and its (altered) states led the field to substantially advance the previous findings. Moreover, it realizes that the use of these measures for the study of the ageing brain resulted in significant insights on various ways that the process of ageing may affect the dynamics and information processing capacity of the brain. It further reveals that their utilization for analysis of the brain regional interactivity formed a bridge between the previous two research areas, thereby providing further evidence in support of their results. It concludes by highlighting some potential considerations that may help future research to refine the use of entropic measures for the study of brain complexity and its function. The present study helps realize that (despite their seemingly differing lines of inquiry) the study of consciousness, the ageing brain, and the brain networks’ information processing are highly interrelated. Specifically, it identifies that the complexity, as quantified by entropy, is a fundamental property of conscious experience, which also plays a vital role in the brain’s capacity for adaptation and therefore whose loss by ageing constitutes a basis for diseases and disorders. Interestingly, these two perspectives neatly come together through the association of entropy and the brain capacity for information processing.

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 Category Structure and Categorical Perception Jointly Explained by Similarity-Based Information Theory

Entropy ◽  
2018 ◽  
Vol 20 (7) ◽  
pp. 527 ◽  
Author(s):  
Romain Brasselet ◽  
Angelo Arleo
Keyword(s):  
Information Theory ◽  
Information Processing ◽  
Internal Structure ◽  
Categorical Perception ◽  
Theoretical Framework ◽  
Physical Distance ◽  
Approximate Matching ◽  
Fundamental Information ◽  
Abstract Categories ◽  
The Brain

Categorization is a fundamental information processing phenomenon in the brain. It is critical for animals to compress an abundance of stimulations into groups to react quickly and efficiently. In addition to labels, categories possess an internal structure: the goodness measures how well any element belongs to a category. Interestingly, this categorization leads to an altered perception referred to as categorical perception: for a given physical distance, items within a category are perceived closer than items in two different categories. A subtler effect is the perceptual magnet: discriminability is reduced close to the prototypes of a category and increased near its boundaries. Here, starting from predefined abstract categories, we naturally derive the internal structure of categories and the phenomenon of categorical perception, using an information theoretical framework that involves both probabilities and pairwise similarities between items. Essentially, we suggest that pairwise similarities between items are to be tuned to render some predefined categories as well as possible. However, constraints on these pairwise similarities only produce an approximate matching, which explains concurrently the notion of goodness and the warping of perception. Overall, we demonstrate that similarity-based information theory may offer a global and unified principled understanding of categorization and categorical perception simultaneously.

scholarly journals Principles for models of neural information processing

2017 ◽  
Author(s):  
Kendrick N. Kay ◽  
Kevin S. Weiner
Keyword(s):  
Information Processing ◽  
Cognitive Neuroscience ◽  
Brain Function ◽  
Network Models ◽  
Neural Network Models ◽  
Specific Effects ◽  
Neural Information ◽  
Neural Information Processing ◽  
The Difference ◽  
The Brain

AbstractThe goal of cognitive neuroscience is to understand how mental operations are performed by the brain. Given the complexity of the brain, this is a challenging endeavor that requires the development of formal models. Here, we provide a perspective on models of neural information processing in cognitive neuroscience. We define what these models are, explain why they are useful, and specify criteria for evaluating models. We also highlight the difference between functional and mechanistic models, and call attention to the value that neuroanatomy has for understanding brain function. Based on the principles we propose, we proceed to evaluate the merit of recently touted deep neural network models. We contend that these models are promising, but substantial work is necessary to (i) clarify what type of explanation these models provide, (ii) determine what specific effects they accurately explain, and (iii) improve our understanding of how they work.

scholarly journals STDP forms associations between memory traces in networks of spiking neurons

2017 ◽  
Author(s):  
Christoph Pokorny ◽  
Matias J. Ison ◽  
Arjun Rao ◽  
Robert Legenstein ◽  
Christos Papadimitriou ◽  
...  
Keyword(s):  
Brain Function ◽  
Spatial Information ◽  
Spike Timing ◽  
Spiking Neurons ◽  
Neural Codes ◽  
Memory Traces ◽  
Conflicting Evidence ◽  
Two Parameters ◽  
Excitability Of Neurons ◽  
The Brain

AbstractMemory 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. How-ever, 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 (STDP). The model depends critically on two parameters, which control the excitability of neurons and the scale of initial synaptic weights. By modifying these two 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 two neural codes for associations, and dynamically switch between them during consolidation.

Monoamines: Dopamine, Norepinephrine, and Serotonin, Beyond Modulation, “Switches” That Alter the State of Target Networks

2020 ◽  
pp. 107385842097433
Author(s):  
Sayed Ausim Azizi
Keyword(s):  
Nervous System ◽  
Information Processing ◽  
Brain Function ◽  
Anatomy And Physiology ◽  
Neuropsychiatric Diseases ◽  
Brain States ◽  
Brain Operations ◽  
And Function ◽  
Electrophysiological Effects ◽  
The Brain

How do monoamines influence the perceptual and behavioral aspects of brain function? A library of information regarding the genetic, molecular, cellular, and function of monoamines in the nervous system and other organs has accumulated. We briefly review monoamines’ anatomy and physiology and discuss their effects on the target neurons and circuits. Monoaminergic cells in the brain stem receive inputs from sensory, limbic, and prefrontal areas and project extensively to the forebrain and hindbrain. We review selected studies on molecular, cellular, and electrophysiological effects of monoamines on the brain’s target areas. The idea is that monoamines, by reversibly modulating the “primary” information processing circuits, regulate and switch the functions of brain networks and can reversibly alter the “brain states,” such as consciousness, emotions, and movements. Monoamines, as the drivers of normal motor and sensory brain operations, including housekeeping, play essential roles in pathogenesis of neuropsychiatric diseases.

SPECIFIC NEUROPSYCHOLOGICAL AND NEUROPHYSIOLOGICAL DYSFUNCTIONS OF A PATIENT WITH FIRST-ONSET SCHIZOPHRENIA AND COMORBID WHITE MATTER DAMAGE

2017 ◽  
Vol 15 (2) ◽  
pp. 0-0
Author(s):  
Paweł Krukow ◽  
Kamil Jonak ◽  
Justyna Morylowska-Topolska ◽  
Hanna Karakula-Juchnowicz
Keyword(s):  
Information Processing ◽  
White Matter ◽  
Brain Function ◽  
White Matter Damage ◽  
Mri Scan ◽  
First Onset ◽  
Schizophrenic Patients ◽  
Healthy Control ◽  
The Brain

[b]Background: [/b]Contemporary research on the neurobiological determinants of schizophrenia is focused on the role of white matter abnormalities, studied mainly at the cellular level using Diffusion Tensor Imaging. At the same time, there are few reports on the effects of white matter damage that can be visualized in a typical MRI scan, on the brain function of schizophrenic patients. The aim of this study was to identify the specific features of the neuropsychological and neurophysiological functioning of a female patient with first-onset schizophrenia and comorbid white matter damage, which discriminated her from a healthy control and from a patient with an identical psychiatric diagnosis, but having no structural brain changes seen in an MRI scan. Identification of those features may help understand the role of subcortical brain dysfunctions in the aetiology and clinical picture of schizophrenia. [b]Case study:[/b]The investigation encompassed clinical, neuropsychological and neurophysiological assessment of two schizophrenic patients, of whom one had comorbid white matter damage imaged by structural MRI, and a healthy control. A number of areas of cognitive functioning were examined, including the speed of information processing and executive and memory functions. The study was conducted using EEG coherence analysis, power spectral density, and energy evaluation of neuronal activity with the Matching Pursuit algorithm.[b]Results: [/b]The study showed that, despite the fact that there were no differences in the psychopathological pictures of the schizophrenic patients, the neuropsychological and neurophysiological differences between them were substantial and related to the profile of cognitive impairments and the specific features of the brain function of the patient with abnormalities in the white matter: that patient’s EEG showed discoherence in the anterior part of the brain, reduced diversity of the dominant frequency of neuronal activity, and pathologically increased energy parameters for low-frequency bands. [b]Conclusions: [/b]Comorbidity of white matter damage with schizophrenia has a potentially significant effect on cerebral activity giving rise to specific information processing deficits. Further research in this area should be conducted with a view to determining biomarkers of mental diseases and improving the validity of clinical psychiatric diagnosis.

scholarly journals Cellular-scale silicon probes for high-density, precisely localized neurophysiology

2020 ◽  
Vol 124 (6) ◽  
pp. 1578-1587 ◽  
Author(s):  
Daniel Egert ◽  
Jeffrey R. Pettibone ◽  
Stefan Lemke ◽  
Paras R. Patel ◽  
Ciara M. Caldwell ◽  
...  
Keyword(s):  
Information Processing ◽  
High Density ◽  
Immunohistochemical Markers ◽  
Brain Circuits ◽  
Neural Information ◽  
Neural Information Processing ◽  
The Brain ◽  
Silicon Probes

Devices with many electrodes penetrating into the brain are an important tool for investigating neural information processing, but they are typically large compared with neurons. This results in substantial damage and makes it harder to reconstruct recording locations within brain circuits. This paper presents high-channel-count silicon probes with much smaller features and a method for slicing through probe, brain, and skull all together. This allows probe tips to be directly observed relative to immunohistochemical markers.

Sign in / Sign up

Export Citation Format

Share Document