scholarly journals A quantitative analysis of information about past and present stimuli encoded by spikes of A1 neurons

2012 ◽  
Vol 108 (5) ◽  
pp. 1366-1380 ◽  
Author(s):  
Stefan Klampfl ◽  
Stephen V. David ◽  
Pingbo Yin ◽  
Shihab A. Shamma ◽  
Wolfgang Maass
Keyword(s):  
Quantitative Analysis ◽  
Temporal Structure ◽  
Temporal Integration ◽  
Primary Auditory Cortex ◽  
Neural Response ◽  
Specific Information ◽  
Acoustic Environment ◽  
Linear Classifiers ◽  
A1 Neurons ◽  
The Rich

To process the rich temporal structure of their acoustic environment, organisms have to integrate information over time into an appropriate neural response. Previous studies have addressed the modulation of responses of auditory neurons to a current sound in dependence of the immediate stimulation history, but a quantitative analysis of this important computational process has been missing. In this study, we analyzed temporal integration of information in the spike output of 122 single neurons in primary auditory cortex (A1) of four awake ferrets in response to random tone sequences. We quantified the information contained in the responses about both current and preceding sounds in two ways: by estimating directly the mutual information between stimulus and response, and by training linear classifiers to decode information about the stimulus from the neural response. We found that 1) many neurons conveyed a significant amount of information not only about the current tone but also simultaneously about the previous tone, 2) the neural response to tone sequences was a nonlinear combination of responses to the tones in isolation, and 3) nevertheless, much of the information about current and previous tones could be extracted by linear decoders. Furthermore, our analysis of these experimental data shows that methods from information theory and the application of standard machine learning methods for extracting specific information yield quite similar results.

scholarly journals Linking Cortical Spike Pattern Codes to Auditory Perception

2008 ◽  
Vol 20 (1) ◽  
pp. 135-152 ◽  
Author(s):  
Kerry M. M. Walker ◽  
Bashir Ahmed ◽  
Jan W. H. Schnupp
Keyword(s):  
Temporal Structure ◽  
Primary Auditory Cortex ◽  
Neural Correlates ◽  
Spike Count ◽  
Auditory Task ◽  
Spike Pattern ◽  
Somatosensory Cortices ◽  
Rate Coding ◽  
The Rich ◽  
Discharge Patterns

Neurometric analysis has proven to be a powerful tool for studying links between neural activity and perception, especially in visual and somatosensory cortices, but conventional neurometrics are based on a simplistic rate-coding hypothesis that is clearly at odds with the rich and complex temporal spiking patterns evoked by many natural stimuli. In this study, we investigated the possible relationships between temporal spike pattern codes in the primary auditory cortex (A1) and the perceptual detection of subtle changes in the temporal structure of a natural sound. Using a two-alternative forced-choice oddity task, we measured the ability of human listeners to detect local time reversals in a marmoset twitter call. We also recorded responses of neurons in A1 of anesthetized and awake ferrets to these stimuli, and analyzed these responses using a novel neurometric approach that is sensitive to temporal discharge patterns. We found that although spike count-based neurometrics were inadequate to account for behavioral performance on this auditory task, neurometrics based on the temporal discharge patterns of populations of A1 units closely matched the psychometric performance curve, but only if the spiking patterns were resolved at temporal resolutions of 20 msec or better. These results demonstrate that neurometric discrimination curves can be calculated for temporal spiking patterns, and they suggest that such an extension of previous spike count-based approaches is likely to be essential for understanding the neural correlates of the perception of stimuli with a complex temporal structure.

Interval Coding. II. Dendrite-Dependent Mechanisms

2007 ◽  
Vol 97 (4) ◽  
pp. 2744-2757 ◽  
Author(s):  
Brent Doiron ◽  
Anne-Marie M. Oswald ◽  
Leonard Maler
Keyword(s):  
Neural Coding ◽  
Pyramidal Neurons ◽  
Temporal Structure ◽  
Spike Trains ◽  
Sensory Cells ◽  
Cellular Mechanics ◽  
Dynamic Stimuli ◽  
Multiple Dimensions ◽  
Sensory Modalities ◽  
The Rich

The rich temporal structure of neural spike trains provides multiple dimensions to code dynamic stimuli. Popular examples are spike trains from sensory cells where bursts and isolated spikes can serve distinct coding roles. In contrast to analyses of neural coding, the cellular mechanics of burst mechanisms are typically elucidated from the neural response to static input. Bridging the mechanics of bursting with coding of dynamic stimuli is an important step in establishing theories of neural coding. Electrosensory lateral line lobe (ELL) pyramidal neurons respond to static inputs with a complex dendrite-dependent burst mechanism. Here we show that in response to dynamic broadband stimuli, these bursts lack some of the electrophysiological characteristics observed in response to static inputs. A simple leaky integrate-and-fire (LIF)-style model with a dendrite-dependent depolarizing afterpotential (DAP) is sufficient to match both the output statistics and coding performance of experimental spike trains. We use this model to investigate a simplification of interval coding where the burst interspike interval (ISI) codes for the scale of a canonical upstroke rather than a multidimensional stimulus feature. Using this stimulus reduction, we compute a quantization of the burst ISIs and the upstroke scale to show that the mutual information rate of the interval code is maximized at a moderate DAP amplitude. The combination of a reduced description of ELL pyramidal cell bursting and a simplification of the interval code increases the generality of ELL burst codes to other sensory modalities.

scholarly journals Fixel-based Analysis of Diffusion MRI: Methods, Applications, Challenges and Opportunities

2020 ◽  
Author(s):  
Thijs Dhollander ◽  
Adam Clemente ◽  
Mervyn Singh ◽  
Frederique Boonstra ◽  
Oren Civier ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
State Of The Art ◽  
Specific Information ◽  
Analysis Framework ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
P Diffusion ◽  
Modelling Techniques ◽  
The Rich

Diffusion MRI has provided the neuroimaging community with a powerful tool to acquire in-vivo data sensitive to microstructural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes. The key to extracting such valuable information lies in complex modelling techniques, which form the link between the rich diffusion MRI data and various metrics related to the microstructural organisation. Over time, increasingly advanced techniques have been developed, up to the point where some diffusion MRI models can now provide access to properties specific to individual fibre populations in each voxel in the presence of multiple "crossing" fibre pathways. While highly valuable, such fibre-specific information poses unique challenges for typical image processing pipelines and statistical analysis. In this work, we review the "fixel-based analysis" (FBA) framework that implements bespoke solutions to this end, and has recently seen a stark increase in adoption for studies of both typical (healthy) populations as well as a wide range of clinical populations. We describe the main concepts related to fixel-based analyses, as well as the methods and specific steps involved in a state-of-the-art FBA pipeline, with a focus on providing researchers with practical advice on how to interpret results. We also include an overview of the scope of current fixel-based analysis studies (until August 2020), categorised across a broad range of neuroscientific domains, listing key design choices and summarising their main results and conclusions. Finally, we critically discuss several aspects and challenges involved with the fixel-based analysis framework, and outline some directions and future opportunities.

Corticofugal Modulation of the Tactile Response Coherence of Projecting Neurons in the Gracilis Nucleus

2007 ◽  
Vol 98 (5) ◽  
pp. 2537-2549 ◽  
Author(s):  
Nazareth P. Castellanos ◽  
Eduardo Malmierca ◽  
Angel Nuñez ◽  
Valeri A. Makarov
Keyword(s):  
Electrical Stimulation ◽  
Tactile Stimulation ◽  
Spectral Power ◽  
Temporal Structure ◽  
Neural Response ◽  
Spike Timing ◽  
Sensory Stimulus ◽  
Functional Coupling ◽  
Tactile Response ◽  
Stimulation Of

Precise and reproducible spike timing is one of the alternatives of the sensory stimulus encoding. We test coherence (repeatability) of the response patterns elicited in projecting gracile neurons by tactile stimulation and its modulation provoked by electrical stimulation of the corticofugal feedback from the somatosensory (SI) cortex. To gain the temporal structure we adopt the wavelet-based approach for quantification of the functional stimulus–neural response coupling. We show that the spontaneous firing patterns (when they exist) are essentially random. Tactile stimulation of the neuron receptive field strongly increases the spectral power in the stimulus and 5- to 15-Hz frequency bands. However, the functional coupling (coherence) between the sensory stimulus and the neural response exhibits ultraslow oscillation (0.07 Hz). During this oscillation the stimulus coherence can temporarily fall below the statistically significant level, i.e., the functional stimulus–response coupling may be temporarily lost for a single neuron. We further demonstrate that electrical stimulation of the SI cortex increases the stimulus coherence for about 60% of cells. We find no significant correlation between the increment of the firing rate and the stimulus coherence, but we show that there is a positive correlation with the amplitude of the peristimulus time histogram. The latter argues that the observed facilitation of the neural response by the corticofugal pathway, at least in part, may be mediated through an appropriate ordering of the stimulus-evoked firing pattern, and the coherence enhancement is more relevant in gracilis nucleus than an increase of the number of spikes elicited by the tactile stimulus.

Delayed Inhibition in Cortical Receptive Fields and the Discrimination of Complex Stimuli

2005 ◽  
Vol 94 (4) ◽  
pp. 2970-2975 ◽  
Author(s):  
Rajiv Narayan ◽  
Ayla Ergün ◽  
Kamal Sen
Keyword(s):  
Auditory Cortex ◽  
Temporal Structure ◽  
Receptive Fields ◽  
Primary Auditory Cortex ◽  
Natural Sounds ◽  
Functional Roles ◽  
Complex Stimuli ◽  
Animal Vocalizations ◽  
Field L ◽  
Cortical Receptive Fields

Although auditory cortex is thought to play an important role in processing complex natural sounds such as speech and animal vocalizations, the specific functional roles of cortical receptive fields (RFs) remain unclear. Here, we study the relationship between a behaviorally important function: the discrimination of natural sounds and the structure of cortical RFs. We examine this problem in the model system of songbirds, using a computational approach. First, we constructed model neurons based on the spectral temporal RF (STRF), a widely used description of auditory cortical RFs. We focused on delayed inhibitory STRFs, a class of STRFs experimentally observed in primary auditory cortex (ACx) and its analog in songbirds (field L), which consist of an excitatory subregion and a delayed inhibitory subregion cotuned to a characteristic frequency. We quantified the discrimination of birdsongs by model neurons, examining both the dynamics and temporal resolution of discrimination, using a recently proposed spike distance metric (SDM). We found that single model neurons with delayed inhibitory STRFs can discriminate accurately between songs. Discrimination improves dramatically when the temporal structure of the neural response at fine timescales is considered. When we compared discrimination by model neurons with and without the inhibitory subregion, we found that the presence of the inhibitory subregion can improve discrimination. Finally, we modeled a cortical microcircuit with delayed synaptic inhibition, a candidate mechanism underlying delayed inhibitory STRFs, and showed that blocking inhibition in this model circuit degrades discrimination.

scholarly journals Facilitatory and Inhibitory Frequency Tuning of Combination-Sensitive Neurons in the Primary Auditory Cortex of Mustached Bats

1999 ◽  
Vol 82 (5) ◽  
pp. 2327-2345 ◽  
Author(s):  
Jagmeet S. Kanwal ◽  
Douglas C. Fitzpatrick ◽  
Nobuo Suga
Keyword(s):  
Auditory Cortex ◽  
Frequency Tuning ◽  
Temporal Structure ◽  
Primary Auditory Cortex ◽  
Frequency Discrimination ◽  
Inhibitory Response ◽  
Center Frequency ◽  
Constant Frequency ◽  
Single Tone ◽  
Threshold Response

Mustached bats, Pteronotus parnellii parnellii,emit echolocation pulses that consist of four harmonics with a fundamental consisting of a constant frequency (CF1-4) component followed by a short, frequency-modulated (FM1-4) component. During flight, the pulse fundamental frequency is systematically lowered by an amount proportional to the velocity of the bat relative to the background so that the Doppler-shifted echo CF2 is maintained within a narrowband centered at ∼61 kHz. In the primary auditory cortex, there is an expanded representation of 60.6- to 63.0-kHz frequencies in the “Doppler-shifted CF processing” (DSCF) area where neurons show sharp, level-tolerant frequency tuning. More than 80% of DSCF neurons are facilitated by specific frequency combinations of ∼25 kHz (BFlow) and ∼61 kHz (BFhigh). To examine the role of these neurons for fine frequency discrimination during echolocation, we measured the basic response parameters for facilitation to synthesized echolocation signals varied in frequency, intensity, and in their temporal structure. Excitatory response areas were determined by presenting single CF tones, facilitative curves were obtained by presenting paired CF tones. All neurons showing facilitation exhibit at least two facilitative response areas, one of broad spectral tuning to frequencies centered at BFlowcorresponding to a frequency in the lower half of the echolocation pulse FM1 sweep and another of sharp tuning to frequencies centered at BFhigh corresponding to the CF2 in the echo. Facilitative response areas for BFhigh are broadened by ∼0.38 kHz at both the best amplitude and 50 dB above threshold response and show lower thresholds compared with the single-tone excitatory BFhigh response areas. An increase in the sensitivity of DSCF neurons would lead to target detection from farther away and/or for smaller targets than previously estimated on the basis of single-tone responses to BFhigh. About 15% of DSCF neurons show oblique excitatory and facilitatory response areas at BFhigh so that the center frequency of the frequency-response function at any amplitude decreases with increasing stimulus amplitudes. DSCF neurons also have inhibitory response areas that either skirt or overlap both the excitatory and facilitatory response areas for BFhigh and sometimes for BFlow. Inhibition by a broad range of frequencies contributes to the observed sharpness of frequency tuning in these neurons. Recordings from orthogonal penetrations show that the best frequencies for facilitation as well as excitation do not change within a cortical column. There does not appear to be any systematic representation of facilitation ratios across the cortical surface of the DSCF area.

scholarly journals Coding of Amplitude Modulation in Primary Auditory Cortex

2011 ◽  
Vol 105 (2) ◽  
pp. 582-600 ◽  
Author(s):  
Pingbo Yin ◽  
Jeffrey S. Johnson ◽  
Kevin N. O'Connor ◽  
Mitchell L. Sutter
Keyword(s):  
Auditory Cortex ◽  
Mixed Mode ◽  
Modulation Frequency ◽  
Primary Auditory Cortex ◽  
Auditory Pathway ◽  
Temporal Modulation ◽  
Temporal Synchrony ◽  
Trial Analysis ◽  
A1 Neurons ◽  
Vector Strength

Conflicting results have led to different views about how temporal modulation is encoded in primary auditory cortex (A1). Some studies find a substantial population of neurons that change firing rate without synchronizing to temporal modulation, whereas other studies fail to see these nonsynchronized neurons. As a result, the role and scope of synchronized temporal and nonsynchronized rate codes in AM processing in A1 remains unresolved. We recorded A1 neurons' responses in awake macaques to sinusoidal AM noise. We find most (37–78%) neurons synchronize to at least one modulation frequency (MF) without exhibiting nonsynchronized responses. However, we find both exclusively nonsynchronized neurons (7–29%) and “mixed-mode” neurons (13–40%) that synchronize to at least one MF and fire nonsynchronously to at least one other. We introduce new measures for modulation encoding and temporal synchrony that can improve the analysis of how neurons encode temporal modulation. These include comparing AM responses to the responses to unmodulated sounds, and a vector strength measure that is suitable for single-trial analysis. Our data support a transformation from a temporally based population code of AM to a rate-based code as information ascends the auditory pathway. The number of mixed-mode neurons found in A1 indicates this transformation is not yet complete, and A1 neurons may carry multiplexed temporal and rate codes.

scholarly journals Sensory Input Directs Spatial and Temporal Plasticity in Primary Auditory Cortex

2001 ◽  
Vol 86 (1) ◽  
pp. 326-338 ◽  
Author(s):  
Michael P. Kilgard ◽  
Pritesh K. Pandya ◽  
Jessica Vazquez ◽  
Anil Gehi ◽  
Christoph E. Schreiner ◽  
...  
Keyword(s):  
Receptive Field ◽  
Auditory Cortex ◽  
Carrier Frequency ◽  
Basal Forebrain ◽  
Sensory Input ◽  
Cortical Plasticity ◽  
Primary Auditory Cortex ◽  
Cortical Response ◽  
Modulation Rate ◽  
A1 Neurons

The cortical representation of the sensory environment is continuously modified by experience. Changes in spatial (receptive field) and temporal response properties of cortical neurons underlie many forms of natural learning. The scale and direction of these changes appear to be determined by specific features of the behavioral tasks that evoke cortical plasticity. The neural mechanisms responsible for this differential plasticity remain unclear partly because important sensory and cognitive parameters differ among these tasks. In this report, we demonstrate that differential sensory experience directs differential plasticity using a single paradigm that eliminates the task-specific variables that have confounded direct comparison of previous studies. Electrical activation of the basal forebrain (BF) was used to gate cortical plasticity mechanisms. The auditory stimulus paired with BF stimulation was systematically varied to determine how several basic features of the sensory input direct plasticity in primary auditory cortex (A1) of adult rats. The distributed cortical response was reconstructed from a dense sampling of A1 neurons after 4 wk of BF-sound pairing. We have previously used this method to show that when a tone is paired with BF activation, the region of the cortical map responding to that tone frequency is specifically expanded. In this report, we demonstrate that receptive-field size is determined by features of the stimulus paired with BF activation. Specifically, receptive fields were narrowed or broadened as a systematic function of both carrier-frequency variability and the temporal modulation rate of paired acoustic stimuli. For example, the mean bandwidth of A1 neurons was increased (+60%) after pairing BF stimulation with a rapid train of tones and decreased (−25%) after pairing unmodulated tones of different frequencies. These effects are consistent with previous reports of receptive-field plasticity evoked by natural learning. The maximum cortical following rate and minimum response latency were also modified as a function of stimulus modulation rate and carrier-frequency variability. The cortical response to a rapid train of tones was nearly doubled if BF stimulation was paired with rapid trains of random carrier frequency, while no following rate plasticity was observed if a single carrier frequency was used. Finally, we observed significant increases in response strength and total area of functionally defined A1 following BF activation paired with certain classes of stimuli and not others. These results indicate that the degree and direction of cortical plasticity of temporal and receptive-field selectivity are specified by the structure and schedule of inputs that co-occur with basal forebrain activation and suggest that the rules of cortical plasticity do not operate on each elemental stimulus feature independently of others.

scholarly journals Measuring Information Spatial Densities

2002 ◽  
Vol 14 (2) ◽  
pp. 405-420 ◽  
Author(s):  
Michele Bezzi ◽  
Inés Samengo ◽  
Stefan Leutgeb ◽  
Sheri J. Mizumori
Keyword(s):  
Spatial Information ◽  
Neural Response ◽  
Lateral Septum ◽  
Specific Information ◽  
Information Distribution ◽  
Metric Set ◽  
Definition Of

A novel definition of the stimulus-specific information is presented, which is particularly useful when the stimuli constitute a continuous and metric set, as, for example, position in space. The approach allows one to build the spatial information distribution of a given neural response. The method is applied to the investigation of putative differences in the coding of position in hippocampus and lateral septum.

scholarly journals A complex feature-based representation of vocalizations emerges in the superficial layers of primary auditory cortex

2021 ◽  
Author(s):  
Pilar Montes-Lourido ◽  
Manaswini Kar ◽  
Stephen V David ◽  
Srivatsun Sadagopan
Keyword(s):  
Auditory Cortex ◽  
Auditory Processing ◽  
Receptive Fields ◽  
Primary Auditory Cortex ◽  
Neural Representation ◽  
Specific Information ◽  
Intermediate Step ◽  
Stimulus Information ◽  
Feature Selectivity ◽  
Feature Based

Early in auditory processing, neural responses faithfully reflect acoustic input. At higher stages of auditory processing, however, neurons become selective for particular call types, eventually leading to specialized regions of cortex that preferentially process calls at the highest auditory processing stages. We previously proposed that an intermediate step in how non-selective responses are transformed into call-selective responses is the detection of informative call features. But how neural selectivity for informative call features emerges from non-selective inputs, whether feature selectivity gradually emerges over the processing hierarchy, and how stimulus information is represented in non-selective and feature-selective populations remain open questions. In this study, using unanesthetized guinea pigs, a highly vocal and social rodent, as an animal model, we characterized the neural representation of calls in three auditory processing stages: the thalamus (vMGB), and thalamorecipient (L4) and superficial layers (L2/3) of primary auditory cortex (A1). We found that neurons in vMGB and A1 L4 did not exhibit call-selective responses and responded throughout the call durations. However, A1 L2/3 neurons showed high call-selectivity with about a third of neurons responding to only one or two call types. These A1 L2/3 neurons only responded to restricted portions of calls suggesting that they were highly selective for call features. Receptive fields of these A1 L2/3 neurons showed complex spectrotemporal structures that could underlie their high call feature selectivity. Information theoretic analysis revealed that in A1 L4 stimulus information was distributed over the population and was spread out over the call durations. In contrast, in A1 L2/3, individual neurons showed brief bursts of high stimulus-specific information, and conveyed high levels of information per spike. These data demonstrate that a transformation in the neural representation of calls occurs between A1 L4 and A1 L2/3, leading to the emergence of a feature-based representation of calls in A1 L2/3. Our data thus suggest that observed cortical specializations for call processing emerge in A1, and set the stage for further mechanistic studies.

Sign in / Sign up

Export Citation Format

Share Document