Temporal scaling of neural responses to compressed and dilated natural speech

Different brain areas integrate information over different timescales, and this capacity to accumulate information increases from early sensory areas to higher order perceptual and cognitive areas. It is currently unknown whether the timescale capacity of each brain area is fixed or whether it adaptively rescales depending on the rate at which information arrives from the world. Here, using functional MRI, we measured brain responses to an auditory narrative presented at different rates. We asked whether neural responses to slowed (speeded) versions of the narrative could be compressed (stretched) to match neural responses to the original narrative. Temporal rescaling was observed in early auditory regions (which accumulate information over short timescales) as well as linguistic and extra-linguistic brain areas (which can accumulate information over long timescales). The temporal rescaling phenomenon started to break down for stimuli presented at double speed, and intelligibility was also impaired for these stimuli. These data suggest that 1) the rate of neural information processing can be rescaled according to the rate of incoming information, both in early sensory regions as well as in higher order cortexes, and 2) the rescaling of neural dynamics is confined to a range of rates that match the range of behavioral performance.

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Cortical response to naturalistic stimuli is largely predictable with deep neural networks

Science Advances ◽

10.1126/sciadv.abe7547 ◽

2021 ◽

Vol 7 (22) ◽

pp. eabe7547

Author(s):

Meenakshi Khosla ◽

Gia H. Ngo ◽

Keith Jamison ◽

Amy Kuceyeski ◽

Mert R. Sabuncu

Keyword(s):

Brain Function ◽

Deep Neural Networks ◽

Neural Responses ◽

Dynamic Interactions ◽

Hierarchical Processing ◽

Human Connectome Project ◽

Neural Information ◽

Neural Information Processing ◽

Visual Interactions ◽

High Level

Naturalistic stimuli, such as movies, activate a substantial portion of the human brain, invoking a response shared across individuals. Encoding models that predict neural responses to arbitrary stimuli can be very useful for studying brain function. However, existing models focus on limited aspects of naturalistic stimuli, ignoring the dynamic interactions of modalities in this inherently context-rich paradigm. Using movie-watching data from the Human Connectome Project, we build group-level models of neural activity that incorporate several inductive biases about neural information processing, including hierarchical processing, temporal assimilation, and auditory-visual interactions. We demonstrate how incorporating these biases leads to remarkable prediction performance across large areas of the cortex, beyond the sensory-specific cortices into multisensory sites and frontal cortex. Furthermore, we illustrate that encoding models learn high-level concepts that generalize to task-bound paradigms. Together, our findings underscore the potential of encoding models as powerful tools for studying brain function in ecologically valid conditions.

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Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy, and Mobility

Neural Computation ◽

10.1162/neco_a_00269 ◽

2012 ◽

Vol 24 (5) ◽

pp. 1147-1185 ◽

Cited By ~ 20

Author(s):

C. C. Alan Fung ◽

K. Y. Michael Wong ◽

He Wang ◽

Si Wu

Keyword(s):

Information Processing ◽

Neural Systems ◽

Neural Responses ◽

Short Term ◽

Neuronal Interactions ◽

Neural Information ◽

Neural Information Processing ◽

Network States ◽

Improved Accuracy ◽

The Impact

Experimental data have revealed that neuronal connection efficacy exhibits two forms of short-term plasticity: short-term depression (STD) and short-term facilitation (STF). They have time constants residing between fast neural signaling and rapid learning and may serve as substrates for neural systems manipulating temporal information on relevant timescales. This study investigates the impact of STD and STF on the dynamics of continuous attractor neural networks and their potential roles in neural information processing. We find that STD endows the network with slow-decaying plateau behaviors: the network that is initially being stimulated to an active state decays to a silent state very slowly on the timescale of STD rather than on that of neuralsignaling. This provides a mechanism for neural systems to hold sensory memory easily and shut off persistent activities gracefully. With STF, we find that the network can hold a memory trace of external inputs in the facilitated neuronal interactions, which provides a way to stabilize the network response to noisy inputs, leading to improved accuracy in population decoding. Furthermore, we find that STD increases the mobility of the network states. The increased mobility enhances the tracking performance of the network in response to time-varying stimuli, leading to anticipative neural responses. In general, we find that STD and STP tend to have opposite effects on network dynamics and complementary computational advantages, suggesting that the brain may employ a strategy of weighting them differentially depending on the computational purpose.

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Same Story, Different Story

Psychological Science ◽

10.1177/0956797616682029 ◽

2017 ◽

Vol 28 (3) ◽

pp. 307-319 ◽

Cited By ~ 74

Author(s):

Yaara Yeshurun ◽

Stephen Swanson ◽

Erez Simony ◽

Janice Chen ◽

Christina Lazaridi ◽

...

Keyword(s):

Mirror Neuron System ◽

Mirror Neuron ◽

Neural Responses ◽

Brain Areas ◽

Neuron System ◽

Brain Responses ◽

The Difference ◽

Network Language ◽

Mri Study ◽

Language Areas

Differences in people’s beliefs can substantially impact their interpretation of a series of events. In this functional MRI study, we manipulated subjects’ beliefs, leading two groups of subjects to interpret the same narrative in different ways. We found that responses in higher-order brain areas—including the default-mode network, language areas, and subsets of the mirror neuron system—tended to be similar among people who shared the same interpretation, but different from those of people with an opposing interpretation. Furthermore, the difference in neural responses between the two groups at each moment was correlated with the magnitude of the difference in the interpretation of the narrative. This study demonstrates that brain responses to the same event tend to cluster together among people who share the same views.

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Cell assemblies at multiple time scales with arbitrary lag constellations

eLife ◽

10.7554/elife.19428 ◽

2017 ◽

Vol 6 ◽

Cited By ~ 27

Author(s):

Eleonora Russo ◽

Daniel Durstewitz

Keyword(s):

Time Scales ◽

Brain Area ◽

Theoretical Concept ◽

Task Demands ◽

Multiple Time Scales ◽

Multiple Time ◽

Time Lags ◽

Assembly Structure ◽

Neural Information ◽

Neural Information Processing

Hebb's idea of a cell assembly as the fundamental unit of neural information processing has dominated neuroscience like no other theoretical concept within the past 60 years. A range of different physiological phenomena, from precisely synchronized spiking to broadly simultaneous rate increases, has been subsumed under this term. Yet progress in this area is hampered by the lack of statistical tools that would enable to extract assemblies with arbitrary constellations of time lags, and at multiple temporal scales, partly due to the severe computational burden. Here we present such a unifying methodological and conceptual framework which detects assembly structure at many different time scales, levels of precision, and with arbitrary internal organization. Applying this methodology to multiple single unit recordings from various cortical areas, we find that there is no universal cortical coding scheme, but that assembly structure and precision significantly depends on the brain area recorded and ongoing task demands.

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Neural Information Processing in Different Brain Areas

An Introduction to Neural Information Processing ◽

10.1007/978-94-017-7393-5_5 ◽

2015 ◽

pp. 233-292

Author(s):

Peiji Liang ◽

Si Wu ◽

Fanji Gu

Keyword(s):

Information Processing ◽

Brain Areas ◽

Neural Information ◽

Neural Information Processing

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Pigeon nidopallium caudolaterale, entopallium, and mesopallium ventrolaterale neural responses during categorisation of Monet and Picasso paintings

Scientific Reports ◽

10.1038/s41598-020-72650-y ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Catrona Anderson ◽

Renelyn S. Parra ◽

Hayley Chapman ◽

Alina Steinemer ◽

Blake Porter ◽

...

Keyword(s):

Left Hemisphere ◽

Preliminary Evidence ◽

Higher Order ◽

Visual Area ◽

Extrastriate Cortex ◽

Hemispheric Differences ◽

Neural Responses ◽

Stimulus Information ◽

Brain Areas ◽

Hemisphere Dominance

Abstract Pigeons can successfully discriminate between sets of Picasso and Monet paintings. We recorded from three pallial brain areas: the nidopallium caudolaterale (NCL), an analogue of mammalian prefrontal cortex; the entopallium (ENTO), an intermediary visual area similar to primate extrastriate cortex; and the mesopallium ventrolaterale (MVL), a higher-order visual area similar to primate higher-order extrastriate cortex, while pigeons performed an S+/S− Picasso versus Monet discrimination task. In NCL, we found that activity reflected reward-driven categorisation, with a strong left-hemisphere dominance. In ENTO, we found that activity reflected stimulus-driven categorisation, also with a strong left-hemisphere dominance. Finally, in MVL, we found that activity reflected stimulus-driven categorisation, but no hemispheric differences were apparent. We argue that while NCL and ENTO primarily use reward and stimulus information, respectively, to discriminate Picasso and Monet paintings, both areas are also capable of integrating the other type of information during categorisation. We also argue that MVL functions similarly to ENTO in that it uses stimulus information to discriminate paintings, although not in an identical way. The current study adds some preliminary evidence to previous literature which emphasises visual lateralisation during discrimination learning in pigeons.

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Information Confusion Reveals an Innate Limit of the Information Processing by Neurons

10.1101/2020.10.19.345249 ◽

2020 ◽

Author(s):

Yang Tian ◽

Justin L. Gardner ◽

Guoqi Li ◽

Pei Sun

Keyword(s):

Information Processing ◽

Neural Coding ◽

Neural System ◽

Negative Answer ◽

Processing Capacity ◽

Neural Responses ◽

Information Processing Capacity ◽

Neural Ensembles ◽

Neural Information ◽

Neural Information Processing

AbstractInformation experiences complex transformation processes in the brain, involving various errors. A daunting and critical challenge in neuroscience is to understand the origin of these errors and their effects on neural information processing. While previous efforts have made substantial progresses in studying the information errors in bounded, unreliable and noisy transformation cases, it still remains elusive whether the neural system is inherently error-free under an ideal and noise-free condition. This work brings the controversy to an end with a negative answer. We propose a novel neural information confusion theory, indicating the widespread presence of information confusion phenomenon after the end of transmission process, which originates from innate neuron characteristics rather than external noises. Then, we reformulate the definition of zero-error capacity under the context of neuroscience, presenting an optimal upper bound of the zero-error transformation rates determined by the tuning properties of neurons. By applying this theory to neural coding analysis, we unveil the multi-dimensional impacts of information confusion on neural coding. Although it reduces the variability of neural responses and limits mutual information, it controls the stimulus-irrelevant neural activities and improves the interpretability of neural responses based on stimuli. Together, the present study discovers an inherent and ubiquitous precision limitation of neural information transformation, which shapes the coding process by neural ensembles. These discoveries reveal that the neural system is intrinsically error-prone in information processing even in the most ideal cases.Author summaryOne of the most central challenges in neuroscience is to understand the information processing capacity of the neural system. Decades of efforts have identified various errors in nonideal neural information processing cases, indicating that the neural system is not optimal in information processing because of the widespread presences of external noises and limitations. These incredible progresses, however, can not address the problem about whether the neural system is essentially error-free and optimal under ideal information processing conditions, leading to extensive controversies in neuroscience. Our work brings this well-known controversy to an end with a negative answer. We demonstrate that the neural system is intrinsically error-prone in information processing even in the most ideal cases, challenging the conventional ideas about the superior neural information processing capacity. We further indicate that the neural coding process is shaped by this innate limit, revealing how the characteristics of neural information functions and further cognitive functions are determined by the inherent limitation of the neural system.

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