Invariant odor recognition with ON–OFF neural ensembles

Invariant stimulus recognition is a challenging pattern-recognition problem that must be dealt with by all sensory systems. Since neural responses evoked by a stimulus are perturbed in a multitude of ways, how can this computational capability be achieved? We examine this issue in the locust olfactory system. We find that locusts trained in an appetitive-conditioning assay robustly recognize the trained odorant independent of variations in stimulus durations, dynamics, or history, or changes in background and ambient conditions. However, individual- and population-level neural responses vary unpredictably with many of these variations. Our results indicate that linear statistical decoding schemes, which assign positive weights to ON neurons and negative weights to OFF neurons, resolve this apparent confound between neural variability and behavioral stability. Furthermore, simplification of the decoder using only ternary weights ({+1, 0, −1}) (i.e., an “ON-minus-OFF” approach) does not compromise performance, thereby striking a fine balance between simplicity and robustness.

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Invariant Odor Recognition with ON-OFF Neural Ensembles

10.1101/2020.11.07.372870 ◽

2020 ◽

Author(s):

Srinath Nizampatnam ◽

Lijun Zhang ◽

Rishabh Chandak ◽

Nalin Katta ◽

Barani Raman

Keyword(s):

Pattern Recognition ◽

Recognition Task ◽

Total Activity ◽

Projection Neurons ◽

Ambient Conditions ◽

Odor Recognition ◽

Pattern Recognition Problem ◽

Neural Ensembles ◽

Computational Capability ◽

Bayesian Logistic Regression

ABSTRACTInvariant recognition of a stimulus is a challenging pattern-recognition problem that must be dealt with by all sensory systems. Since neural responses evoked by a stimulus could be perturbed in a multitude of ways, could a single scheme be devised to achieve this computational capability? We examined this issue in locust olfactory system. We found that odor-evoked responses in individual projection neurons in the locust antennal lobe varied unpredictably with repetition, stimulus dynamics, stimulus history, presence of background odorants, and changes in ambient conditions. Yet, a highly-constrained Bayesian logistic regression approach with ternary weights could provide robust odor recognition. We found that this approach could be further simplified: sum firing rates of ON neurons and subtract total activity in OFF neurons (‘ON minus OFF’ classifier). Notably, we found that this approach could be generalized to develop a Boolean neural network that can perform well in a non-olfactory pattern recognition task.

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Simultaneous Reorganization in Thalamocortical Ensembles Evolves Over Several Hours After Perioral Capsaicin Injections

Journal of Neurophysiology ◽

10.1152/jn.1999.82.2.963 ◽

1999 ◽

Vol 82 (2) ◽

pp. 963-977 ◽

Cited By ~ 33

Author(s):

Donald B. Katz ◽

S. A. Simon ◽

Aaron Moody ◽

Miguel A. L. Nicolelis

Keyword(s):

Cortical Neurons ◽

Time Course ◽

Cortical Plasticity ◽

Population Analysis ◽

Receptive Fields ◽

Somatosensory System ◽

Spontaneous Firing ◽

Neural Responses ◽

Spatial Coupling ◽

Neural Ensembles

Reorganization of the somatosensory system was quantified by simultaneously recording from single-unit neural ensembles in the whisker regions of the ventral posterior medial (VPM) nucleus of the thalamus and the primary somatosensory (SI) cortex in anesthetized rats before, during, and after injecting capsaicin under the skin of the lip. Capsaicin, a compound that excites and then inactivates a subset of peripheral C and Aδ fibers, triggered increases in spontaneous firing of thalamocortical neurons (10–15 min after injection), as well as rapid reorganization of the whisker representations in both the VPM and SI. During the first hour after capsaicin injection, 57% of the 139 recorded neurons either gained or lost at least one whisker response in their receptive fields (RFs). Capsaicin-related changes continued to emerge for ≥6 h after the injection: Fifty percent of the single-neuron RFs changed between 1–2 and 5–6 h after capsaicin injection. Most (79%) of these late changes represented neural responses that had remained unchanged in the first postcapsaicin mapping; just under 20% of these late changes appeared in neurons that had previously shown no plasticity of response. The majority of the changes (55% immediately after injection, 66% 6 h later) involved “unmasking” of new tactile responses. RF change rates were comparable in SI and VPM (57–49%). Population analysis indicated that the reorganization was associated with a lessening of the “spatial coupling” between cortical neurons—a significant reduction in firing covariance that could be related to distances between neurons. This general loss of spatial coupling, in conjunction with increases in spontaneous firing, may create a situation that is favorable for the induction of synaptic plasticity. Our results indicate that the selective inactivation of a peripheral nociceptor subpopulation can induce rapid and long-evolving (≥6 h) shifts in the balance of inhibition and excitation in the somatosensory system. The time course of these processes suggest that thalamic and cortical plasticity is not a linear reflection of spinal and brainstem changes that occur following the application of capsaicin.

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A curved manifold orients rotational dynamics in motor cortex

10.1101/2021.09.09.459647 ◽

2021 ◽

Author(s):

David A. Sabatini ◽

Matthew T. Kaufman

Keyword(s):

Motor Cortex ◽

State Space ◽

Population Level ◽

Pattern Generation ◽

Rotational Dynamics ◽

Simple Relationship ◽

Time Varying ◽

Neural Responses ◽

Complex Time ◽

Time Courses

SummaryControlling arm movements requires complex, time-varying patterns of muscle activity 1,2. Accordingly, the responses of neurons in motor cortex are complex, time-varying, and heterogeneous during reaching 2–4. When examined at the population level, patterns of neural activity evolve over time according to dynamical rules 5,6. During reaching, these rules have been argued to be “rotational” 7 or variants thereof 8,9, containing coordinated oscillations in the spike rates of individual neurons. While these models capture key aspects of the neural responses, they fail to capture others – accounting for only 20-50% of the neural response variance. Here, we consider a broader class of dynamical models. We find that motor cortex dynamics take an unexpected form: there were 3-4 rotations at fixed frequencies in M1 and PMd explaining more than 90% of neural responses, but these rotations occurred in different portions of state space when movements differ. These rotations appear to reflect a curved manifold of fixed points in state space, around which dynamics are locally rotational. These fixed-frequency rotations obeyed a simple relationship with movement: the orientation of rotations in motor cortex activity were related almost linearly to the movement the animal made, allowing linear decoding of reach kinematic time-courses on single trials. This model constitutes a fundamentally novel way to consider pattern generation: like placing a record player in a large bowl, the frequency of activity is fixed, but the location of motor cortex activity on a curved manifold sets the orientation of locally-rotational dynamics. This system simplifies motor control, helps reconcile conflicting frameworks for interpreting motor cortex, and enables greatly improved neural decoding.

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The neural response to the temporal fine structure of continuous musical pieces is not affected by selective attention

10.1101/2021.01.27.428483 ◽

2021 ◽

Author(s):

Octave Etard ◽

Rémy Ben Messaoud ◽

Gabriel Gaugain ◽

Tobias Reichenbach

Keyword(s):

Fine Structure ◽

Selective Attention ◽

Population Level ◽

Neural Response ◽

Temporal Fine Structure ◽

Neural Responses ◽

Neural Encoding ◽

Human Volunteers ◽

Eeg Recordings ◽

Single Instrument

AbstractSpeech and music are spectro-temporally complex acoustic signals that a highly relevant for humans. Both contain a temporal fine structure that is encoded in the neural responses of subcortical and cortical processing centres. The subcortical response to the temporal fine structure of speech has recently been shown to be modulated by selective attention to one of two competing voices. Music similarly often consists of several simultaneous melodic lines, and a listener can selectively attend to a particular one at a time. However, the neural mechanisms that enable such selective attention remain largely enigmatic, not least since most investigations to date have focussed on short and simplified musical stimuli. Here we study the neural encoding of classical musical pieces in human volunteers, using scalp electroencephalography (EEG) recordings. We presented volunteers with continuous musical pieces composed of one or two instruments. In the latter case, the participants were asked to selectively attend to one of the two competing instruments and to perform a vibrato identification task. We used linear encoding and decoding models to relate the recorded EEG activity to the stimulus waveform. We show that we can measure neural responses to the temporal fine structure of melodic lines played by one single instrument, at the population level as well as for most individual subjects. The neural response peaks at a latency of 7.6 ms and is not measurable past 15 ms. When analysing the neural responses elicited by competing instruments, we find no evidence of attentional modulation. Our results show that, much like speech, the temporal fine structure of music is tracked by neural activity. In contrast to speech, however, this response appears unaffected by selective attention in the context of our experiment.

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No Evidence of Attentional Modulation of the Neural Response to the Temporal Fine Structure of Continuous Musical Pieces

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01811 ◽

2021 ◽

pp. 1-14

Author(s):

Octave Etard ◽

Rémy Ben Messaoud ◽

Gabriel Gaugain ◽

Tobias Reichenbach

Keyword(s):

Fine Structure ◽

Selective Attention ◽

Neural Activity ◽

Low Frequency ◽

Population Level ◽

Neural Response ◽

Temporal Fine Structure ◽

Neural Responses ◽

Attentional Modulation ◽

Eeg Recordings

Abstract Speech and music are spectrotemporally complex acoustic signals that are highly relevant for humans. Both contain a temporal fine structure that is encoded in the neural responses of subcortical and cortical processing centers. The subcortical response to the temporal fine structure of speech has recently been shown to be modulated by selective attention to one of two competing voices. Music similarly often consists of several simultaneous melodic lines, and a listener can selectively attend to a particular one at a time. However, the neural mechanisms that enable such selective attention remain largely enigmatic, not least since most investigations to date have focused on short and simplified musical stimuli. Here, we studied the neural encoding of classical musical pieces in human volunteers, using scalp EEG recordings. We presented volunteers with continuous musical pieces composed of one or two instruments. In the latter case, the participants were asked to selectively attend to one of the two competing instruments and to perform a vibrato identification task. We used linear encoding and decoding models to relate the recorded EEG activity to the stimulus waveform. We show that we can measure neural responses to the temporal fine structure of melodic lines played by one single instrument, at the population level as well as for most individual participants. The neural response peaks at a latency of 7.6 msec and is not measurable past 15 msec. When analyzing the neural responses to the temporal fine structure elicited by competing instruments, we found no evidence of attentional modulation. We observed, however, that low-frequency neural activity exhibited a modulation consistent with the behavioral task at latencies from 100 to 160 msec, in a similar manner to the attentional modulation observed in continuous speech (N100). Our results show that, much like speech, the temporal fine structure of music is tracked by neural activity. In contrast to speech, however, this response appears unaffected by selective attention in the context of our experiment.

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Action representation in the mouse parieto-frontal network

10.1101/646414 ◽

2019 ◽

Cited By ~ 1

Author(s):

Tuce Tombaz ◽

Benjamin A. Dunn ◽

Karoline Hovde ◽

Ryan J. Cubero ◽

Bartul Mimica ◽

...

Keyword(s):

Mirror Neurons ◽

Posterior Parietal Cortex ◽

Wheel Running ◽

Action Representation ◽

Neural Responses ◽

Brain Areas ◽

Neural Ensembles ◽

Sensorimotor Transformations ◽

Posterior Parietal ◽

Goal Directed Behavior

AbstractThe posterior parietal cortex (PPC), along with anatomically linked frontal areas, form a cortical network which mediates several functions that support goal-directed behavior, including sensorimotor transformations and decision making. In primates, this network also links performed and observed actions via mirror neurons, which fire both when an individual performs an action and when they observe the same action performed by a conspecific. Mirror neurons are thought to be important for social learning and imitation, but it is not known whether mirror-like neurons occur in similar networks in other species that can learn socially, such as rodents. We therefore imaged Ca2+ responses in large neural ensembles in PPC and secondary motor cortex (M2) while mice performed and observed several actions in pellet reaching and wheel running tasks. In all animals, we found spatially overlapping neural ensembles in PPC and M2 that robustly encoded a variety of naturalistic behaviors, and that subsets of cells could stably encode multiple actions. However, neural responses to the same set of observed actions were absent in both brain areas, and across animals. Statistical modeling analyses also showed that performed actions, especially those that were task-specific, outperformed observed actions in predicting neural responses. Overall, these findings show that performed and observed actions do not drive the same cells in the parieto-frontal network in mice, and suggest that sensorimotor mirroring in the mammalian cortex may have evolved more recently, and only in certain species.

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Dynamic coordination of the perirhinal cortical neurons supports coherent representations between task epochs

Communications Biology ◽

10.1038/s42003-020-01129-3 ◽

2020 ◽

Vol 3 (1) ◽

Cited By ~ 1

Author(s):

Tomoya Ohnuki ◽

Yuma Osako ◽

Hiroyuki Manabe ◽

Yoshio Sakurai ◽

Junya Hirokawa

Keyword(s):

Cortical Neurons ◽

Single Neuron ◽

Principal Component ◽

Population Level ◽

Neural Responses ◽

Population Code ◽

Firing Patterns ◽

Dynamic Coordination ◽

The Individual ◽

Dynamic Population

AbstractCortical neurons show distinct firing patterns across multiple task epochs characterized by different computations. Recent studies suggest that such distinct patterns underlie dynamic population code achieving computational flexibility, whereas neurons in some cortical areas often show coherent firing patterns across epochs. To understand how coherent single-neuron code contributes to dynamic population code, we analyzed neural responses in the rat perirhinal cortex (PRC) during cue and reward epochs of a two-alternative forced-choice task. We found that the PRC neurons often encoded the opposite choice directions between those epochs. By using principal component analysis as a population-level analysis, we identified neural subspaces associated with each epoch, which reflected coordination across the neurons. The cue and reward epochs shared neural dimensions where the choice directions were consistently discriminated. Interestingly, those dimensions were supported by dynamically changing contributions of the individual neurons. These results demonstrated heterogeneity of coherent single-neuron representations in their contributions to population code.

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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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Dynamic coordination of the perirhinal cortical neurons supports coherent representations between task epochs

10.1101/514612 ◽

2019 ◽

Cited By ~ 1

Author(s):

Tomoya Ohnuki ◽

Yuma Osako ◽

Hiroyuki Manabe ◽

Yoshio Sakurai ◽

Junya Hirokawa

Keyword(s):

Cortical Neurons ◽

Single Neuron ◽

Principal Component ◽

Population Level ◽

Neural Responses ◽

Population Code ◽

Firing Patterns ◽

Dynamic Coordination ◽

Computational Aspects ◽

Dynamic Population

AbstractCortical neurons show distinct firing patterns across multiple task-epochs characterized by distinct computational aspects. Recent studies suggest that such distinct patterns underly dynamic population code achieving computational flexibility, whereas neurons in some cortical areas often show coherent firing patterns across epochs. To understand how such coherent single-neuron code contribute to dynamic population code, we analyzed neural responses in the perirhinal cortex (PRC) during cue and reward epochs of a two-alternative forced-choice task. We found that the PRC neurons often encoded the opposite choice-directions between those epochs. By using principal component analysis as population-level analysis, we identified neural subspaces associated with each epoch, which reflected coordinated patterns across the neurons. The cue and reward epochs shared neural dimensions where the choice directions were consistently discriminated. Interestingly, those dimensions were supported by dynamically changing contributions of individual neurons. These results indicated heterogeneity of coherent single-neuron responses in their contribution to population code.

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Contextual gating of motivationally-relevant stimuli in the mouse nucleus accumbens

10.1101/2021.06.24.449720 ◽

2021 ◽

Author(s):

Jimmie Gmaz ◽

Matthijs van der Meer

Keyword(s):

Nucleus Accumbens ◽

Behavioral Flexibility ◽

Population Level ◽

Prediction Errors ◽

Neural Ensembles ◽

Gating Model ◽

Reward Prediction ◽

Odor Discrimination Task ◽

Flexible Behavior ◽

Value Coding

Neural activity in the nucleus accumbens (NAc) is thought to track fundamentally value- centric quantities such as current or future expected reward, reward prediction errors, the value of work, opportunity cost, and approach vigor. However, the NAc also contributes to flexible behavior in ways that are difficult to explain based on value signals alone, raising the question of if and how non-value signals are encoded in NAc. We recorded NAc neural ensembles while head-fixed mice performed a context-dependent odor discrimination task, and extracted single-unit and population-level correlates of task features. We found coding for context-setting cues that modulate the stimulus-outcome association of subsequently presented reward-predictive cues. This context signal occupied a subspace orthogonal to classic value representations, suggesting that it does not interfere with value-related NAc output. Finally, we show that the context signal is predictive of subsequent value coding, supporting a circuit-level gating model for how the NAc contributes to behavioral flexibility and providing a novel population-level perspective from which to view NAc computations.

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