scholarly journals Modelling novelty detection in the thalamocortical loop

2021 ◽  
Author(s):  
Chao Han ◽  
Gwendolyn English ◽  
Hannes P. Saal ◽  
Giacomo Indiveri ◽  
Aditya Gilra ◽  
...  
Keyword(s):  
Mismatch Negativity ◽  
Novelty Detection ◽  
Mechanistic Model ◽  
Synaptic Depression ◽  
Recurrent Network ◽  
Cortical Layer ◽  
Secondary Response ◽  
Neuron Activity ◽  
Natural Environments ◽  
Population Activity

In complex natural environments, sensory systems are constantly exposed to a large stream of inputs. Novel or rare stimuli, which are often associated with behaviorally important events, are typically processed differently than the steady sensory background, which has less relevance. Neural signatures of such differential processing, commonly referred to as novelty detection, have been identified on the level of EEG recordings as mismatch negativity and the level of single neurons as stimulus-specific adaptation. Here, we propose a multi-scale recurrent network with synaptic depression to explain how novelty detection can arise in the whisker-related part of the somatosensory thalamocortical loop. The architecture and dynamics of the model presume that neurons in cortical layer 6 adapt, via synaptic depression, specifically to a frequently presented stimulus, resulting in reduced population activity in the corresponding cortical column when compared with the population activity evoked by a rare stimulus. This difference in population activity is then projected from the cortex to the thalamus and amplified through the interaction between neurons of the primary and reticular nuclei of the thalamus, resulting in spindle-like, rhythmic oscillations. These differentially activated thalamic oscillations are forwarded to cortical layer 4 as a late secondary response that is specific to rare stimuli that violate a particular stimulus pattern. Model results show a strong analogy between this late single neuron activity and EEG-based mismatch negativity in terms of their common sensitivity to presentation context and timescales of response latency, as observed experimentally. Our results indicate that adaptation in L6 can establish the thalamocortical dynamics that produce signatures of SSA and MMN and suggest a mechanistic model of novelty detection that could generalize to other sensory modalities.

scholarly journals Orexin receptor antagonists reverse aberrant dopamine neuron activity and related behaviors in a rodent model of stress-induced psychosis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hannah B. Elam ◽  
Stephanie M. Perez ◽  
Jennifer J. Donegan ◽  
Daniel J. Lodge
Keyword(s):  
United States ◽  
Sensorimotor Gating ◽  
The United States ◽  
Dopamine Neuron ◽  
Neuron Activity ◽  
Sprague Dawley ◽  
Post Traumatic Stress ◽  
Neuron Population ◽  
Population Activity ◽  
Novel Approach

AbstractPost-traumatic stress disorder (PTSD) is a prevalent condition affecting approximately 8% of the United States population and 20% of United States combat veterans. In addition to core symptoms of the disorder, up to 64% of individuals diagnosed with PTSD experience comorbid psychosis. Previous research has demonstrated a positive correlation between symptoms of psychosis and increases in dopamine transmission. We have recently demonstrated projections from the paraventricular nucleus of the thalamus (PVT) to the nucleus accumbens (NAc) can regulate dopamine neuron activity in the ventral tegmental area (VTA). Specifically, inactivation of the PVT leads to a reversal of aberrant dopamine system function and psychosis-like behavior. The PVT receives dense innervation from orexin containing neurons, therefore, targeting orexin receptors may be a novel approach to restore dopamine neuron activity and alleviate PTSD-associated psychosis. In this study, we induced stress-related pathophysiology in male Sprague Dawley rats using an inescapable foot-shock procedure. We observed a significant increase in VTA dopamine neuron population activity, deficits in sensorimotor gating, and hyperresponsivity to psychomotor stimulants. Administration of selective orexin 1 receptor (OX1R) and orexin 2 receptor (OX2R) antagonists (SB334867 and EMPA, respectively) or the FDA-approved, dual-orexin receptor antagonist, Suvorexant, were found to reverse stress-induced increases in dopamine neuron population activity. However, only Suvorexant and SB334867 were able to reverse deficits in behavioral corelates of psychosis. These results suggest that the orexin system may be a novel pharmacological target for the treatment of comorbid psychosis related to PTSD.

Temporal Integration by Stochastic Recurrent Network Dynamics With Bimodal Neurons

2007 ◽  
Vol 97 (6) ◽  
pp. 3859-3867 ◽  
Author(s):  
Hiroshi Okamoto ◽  
Yoshikazu Isomura ◽  
Masahiko Takada ◽  
Tomoki Fukai
Keyword(s):  
Cortical Neurons ◽  
Cognitive Task ◽  
Temporal Integration ◽  
Activity Patterns ◽  
Recurrent Network ◽  
External Input ◽  
Delay Period ◽  
Anterior Cingulate ◽  
Population Activity ◽  
The Individual

Temporal integration of externally or internally driven information is required for a variety of cognitive processes. This computation is generally linked with graded rate changes in cortical neurons, which typically appear during a delay period of cognitive task in the prefrontal and other cortical areas. Here, we present a neural network model to produce graded (climbing or descending) neuronal activity. Model neurons are interconnected randomly by AMPA-receptor–mediated fast excitatory synapses and are subject to noisy background excitatory and inhibitory synaptic inputs. In each neuron, a prolonged afterdepolarizing potential follows every spike generation. Then, driven by an external input, the individual neurons display bimodal rate changes between a baseline state and an elevated firing state, with the latter being sustained by regenerated afterdepolarizing potentials. When the variance of background input and the uniform weight of recurrent synapses are adequately tuned, we show that stochastic noise and reverberating synaptic input organize these bimodal changes into a sequence that exhibits graded population activity with a nearly constant slope. To test the validity of the proposed mechanism, we analyzed the graded activity of anterior cingulate cortex neurons in monkeys performing delayed conditional Go/No-go discrimination tasks. The delay-period activities of cingulate neurons exhibited bimodal activity patterns and trial-to-trial variability that are similar to those predicted by the proposed model.

scholarly journals Dynamic Circuitry for Updating Spatial Representations. III. From Neurons to Behavior

2007 ◽  
Vol 98 (1) ◽  
pp. 105-121 ◽  
Author(s):  
Rebecca A. Berman ◽  
Laura M. Heiser ◽  
Catherine A. Dunn ◽  
Richard C. Saunders ◽  
Carol L. Colby
Keyword(s):  
Spatial Behavior ◽  
Neuron Activity ◽  
Spatial Representations ◽  
Behavioral Performance ◽  
Double Step ◽  
Population Activity ◽  
Extensive Experience ◽  
Split Brain ◽  
Lateral Intraparietal Cortex ◽  
The Difference

Each time the eyes move, the visual system must adjust internal representations to account for the accompanying shift in the retinal image. In the lateral intraparietal cortex (LIP), neurons update the spatial representations of salient stimuli when the eyes move. In previous experiments, we found that split-brain monkeys were impaired on double-step saccade sequences that required updating across visual hemifields, as compared to within hemifield. Here we describe a subsequent experiment to characterize the relationship between behavioral performance and neural activity in LIP in the split-brain monkey. We recorded from single LIP neurons while split-brain and intact monkeys performed two conditions of the double-step saccade task: one required across-hemifield updating and the other required within-hemifield updating. We found that, despite extensive experience with the task, the split-brain monkeys were significantly more accurate for within-hemifield than for across-hemifield sequences. In parallel, we found that population activity in LIP of the split-brain monkeys was significantly stronger for the within-hemifield than for the across-hemifield condition of the double-step task. In contrast, in the normal monkey, both the average behavioral performance and population activity showed no bias toward the within-hemifield condition. Finally, we found that the difference between within-hemifield and across-hemifield performance in the split-brain monkeys was reflected at the level of single-neuron activity in LIP. These findings indicate that remapping activity in area LIP is present in the split-brain monkey for the double-step task and covaries with spatial behavior on within-hemifield compared to across-hemifield sequences.

A mechanistic model of mismatch negativity in the ageing brain

2014 ◽  
Vol 125 (9) ◽  
pp. 1774-1782 ◽  
Author(s):  
Gerald Cooray ◽  
Marta I. Garrido ◽  
L. Hyllienmark ◽  
Tom Brismar
Keyword(s):  
Mismatch Negativity ◽  
Mechanistic Model ◽  
Ageing Brain

Application of floating silicon-based linear multielectrode arrays for acute recording of single neuron activity in awake behaving monkeys

2014 ◽  
Vol 59 (4) ◽  
Author(s):  
Luca Bonini ◽  
Monica Maranesi ◽  
Alessandro Livi ◽  
Stefania Bruni ◽  
Leonardo Fogassi ◽  
...  
Keyword(s):  
Single Neuron ◽  
Signal To Noise Ratio ◽  
Brain Area ◽  
Cortical Layer ◽  
Neuron Activity ◽  
Multielectrode Arrays ◽  
Behaving Monkeys ◽  
Silicon Based ◽  
The Brain

AbstractOne of the fundamental challenges in behavioral neurophysiology in awake animals is the steady recording of action potentials of many single neurons for as long as possible. Here, we present single neuron data obtained during acute recordings mainly from premotor cortices of three macaque monkeys using a silicon-based linear multielectrode array. The most important aspect of these probes, compared with similar models commercially available, is that, once inserted into the brain using a dedicated insertion device providing an intermediate probe fixation by means of vacuum, they can be released and left floating in the brain. On the basis of our data, these features appear to provide (i) optimal physiological conditions for extracellular recordings, (ii) good or even excellent signal-to-noise ratio depending on the recorded brain area and cortical layer, and (iii) extreme stability of the signal over relatively long periods. The quality of the recorded signal did not change significantly after several penetrations into the same restricted cortical sector, suggesting limited tissue damage due to probe insertion. These results indicate that these probes offer several advantages for acute neurophysiological experiments in awake monkeys, and suggest the possibility to employ them for semichronic or even chronic studies.

Population Models of Temporal Differentiation

2010 ◽  
Vol 22 (3) ◽  
pp. 621-659 ◽  
Author(s):  
Bryan P. Tripp ◽  
Chris Eliasmith
Keyword(s):  
Network Dynamics ◽  
Synaptic Depression ◽  
Recurrent Network ◽  
Membrane Dynamics ◽  
Feedforward Network ◽  
Current Decay ◽  
Input Noise ◽  
Feedback Strength ◽  
Wide Range ◽  
Depression Scales

Temporal derivatives are computed by a wide variety of neural circuits, but the problem of performing this computation accurately has received little theoretical study. Here we systematically compare the performance of diverse networks that calculate derivatives using cell-intrinsic adaptation and synaptic depression dynamics, feedforward network dynamics, and recurrent network dynamics. Examples of each type of network are compared by quantifying the errors they introduce into the calculation and their rejection of high-frequency input noise. This comparison is based on both analytical methods and numerical simulations with spiking leaky-integrate-and-fire (LIF) neurons. Both adapting and feedforward-network circuits provide good performance for signals with frequency bands that are well matched to the time constants of postsynaptic current decay and adaptation, respectively. The synaptic depression circuit performs similarly to the adaptation circuit, although strictly speaking, precisely linear differentiation based on synaptic depression is not possible, because depression scales synaptic weights multiplicatively. Feedback circuits introduce greater errors than functionally equivalent feedforward circuits, but they have the useful property that their dynamics are determined by feedback strength. For this reason, these circuits are better suited for calculating the derivatives of signals that evolve on timescales outside the range of membrane dynamics and, possibly, for providing the wide range of timescales needed for precise fractional-order differentiation.

scholarly journals Small, correlated changes in synaptic connectivity may facilitate rapid motor learning

2021 ◽  
Author(s):  
Barbara Feulner ◽  
Matthew G. Perich ◽  
Raeed H. Chowdhury ◽  
Lee E. Miller ◽  
Juan Álvaro Gallego ◽  
...  
Keyword(s):  
Neural Activity ◽  
Recurrent Network ◽  
Neuron Activity ◽  
Local Connectivity ◽  
Neural Recordings ◽  
Synaptic Noise ◽  
Synaptic Changes ◽  
Single Neuron Activity ◽  
Good Agreement

Animals can rapidly adapt their movements to external perturbations. This adaptation is paralleled by changes in single neuron activity in the motor cortices. Behavioural and neural recording studies suggest that when animals learn to counteract a visuomotor perturbation, these changes originate from altered inputs to the motor cortices rather than from changes in local connectivity, as neural covariance is largely preserved during adaptation. Since measuring synaptic changes in vivo remains very challenging, we used a modular recurrent network model to compare the expected neural activity changes following learning through altered inputs (Hinput) and learning through local connectivity changes (Hlocal). Learning under Hinput produced small changes in neural activity and largely preserved the neural covariance, in good agreement with neural recordings in monkeys. Surprisingly given the presumed dependence of stable neural covariance on preserved circuit connectivity, Hlocal led to only slightly larger changes in neural activity and covariance compared to Hinput. This similarity is due to Hlocal only requiring small, correlated connectivity changes to counteract the perturbation, which provided the network with significant robustness against simulated synaptic noise. Simulations of tasks that impose increasingly larger behavioural changes revealed a growing difference between Hinput and Hlocal, which could be exploited when designing future experiments.

scholarly journals Functional triplet motifs underlie accurate predictions of single-trial responses in populations of tuned and untuned V1 neurons

2017 ◽  
Author(s):  
Joseph B Dechery ◽  
Jason N MacLean
Keyword(s):  
Visual Cortex ◽  
Local Population ◽  
Visual Stimuli ◽  
Response Variability ◽  
Local Network ◽  
Neuron Activity ◽  
Single Trial ◽  
Population Activity ◽  
Response Properties ◽  
Large Populations

AbstractVisual stimuli are encoded in the activity patterns of neocortical neuronal populations. Trial-averaged neuronal activity is selectively modulated by particular visual stimulus parameters, such as the direction of a moving bar of light, resulting in well-defined tuning properties. However, a large number of neurons in visual cortex remain unmodulated by any given stimulus parameter, and the role of this untuned population is not well understood. Here, we use two-photon calcium imaging to record, in an unbiased manner, from large populations of layer 2/3 excitatory neurons in mouse primary visual cortex to describe co-varying activity on single trials in populations consisting of tuned and untuned neurons. Specifically, we summarize pairwise covariability with an asymmetric partial correlation coefficient, allowing us to analyze the population correlation structure with graph theory. Using the graph neighbors of a neuron, we find that the local population, including tuned and untuned neurons, are able to predict individual neuron activity on a single trial basis and recapitulate average tuning properties of tuned neurons. We also find that a specific functional triplet motif in the graph results in the best predictions, suggesting a signature of informative correlations in these populations. Variance explained in total population activity scales with the number of neurons imaged, suggesting larger sample sizes are required to fully capture local network interactions. In summary, we show that unbiased sampling of the local population can explain single trial response variability as well as trial-averaged tuning properties in V1, and the ability to predict responses is tied to the occurrence of a functional triplet motif.Author summaryV1 populations have historically been characterized by single cell response properties and pairwise co-variability. Many cells, however, do not show obvious dependencies to a given stimulus or behavioral task, and have consequently gone unanalyzed. We densely record from V1 populations to measure how trial-to-trial response variability relates to these previously understudied neurons. We find that individual neurons, regardless of response properties, are inextricably dependent on the population in which they are embedded. By studying patterns of correlations between groups of neurons, we identify a specific triplet motif that predicts this dependence on local population activity. Only by studying large populations simultaneously were we able to find an emergent property of this information. These results imply that understanding how the visual system operates with substantial trial-to-trial variability will necessitate a network perspective that accounts for both visual stimuli and activity in the local population.

scholarly journals An oligarchy of NO-producing interneurons controls basal and evoked blood flow in the cortex

2019 ◽  
Author(s):  
Christina T. Echagarruga ◽  
Kyle Gheres ◽  
Patrick J. Drew
Keyword(s):  
Nitric Oxide ◽  
Blood Flow ◽  
Neural Activity ◽  
Neural Population ◽  
Neuron Activity ◽  
Arterial Diameter ◽  
Cellular Mechanisms ◽  
Population Activity ◽  
Basal Diameter ◽  
Arterial Dilation

AbstractChanges in cortical neural activity are coupled to changes in local arterial diameter and blood flow. However, the neuronal types and the signaling mechanisms that control the basal diameter of cerebral arteries or their evoked dilations are not well understood. Using chronic two-photon microscopy, electrophysiology, chemogenetics, and pharmacology in awake, head-fixed mice, we dissected the cellular mechanisms controlling the basal diameter and evoked dilation in cortical arteries. We found that modulation of overall neural activity up or down caused corresponding increases or decreases in basal arterial diameter. Surprisingly, modulation of pyramidal neuron activity had minimal effects on basal or evoked arterial dilation. Instead, the neurally-mediated component of arterial dilation was largely regulated through nitric oxide released by neuronal nitric oxide synthase (nNOS)-expressing neurons, whose activity was not reflected in electrophysiological measures of population activity. Our results show that cortical hemodynamic signals are not controlled by the average activity of the neural population, but rather the activity of a small ‘oligarchy’ of neurons.

scholarly journals Decoupling Action Potential Bias from Cortical Local Field Potentials

2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
Stephen V. David ◽  
Nicolas Malaval ◽  
Shihab A. Shamma
Keyword(s):  
Local Field ◽  
Field Potential ◽  
Primary Auditory Cortex ◽  
Neuronal Population ◽  
Neuron Activity ◽  
Small Subset ◽  
Population Activity ◽  
Local Field Potential Lfp ◽  
Single Neuron Activity ◽  
Filtering Procedure

Neurophysiologists have recently become interested in studying neuronal population activity through local field potential (LFP) recordings during experiments that also record the activity of single neurons. This experimental approach differs from early LFP studies because it uses high impendence electrodes that can also isolate single neuron activity. A possible complication for such studies is that the synaptic potentials and action potentials of the small subset of isolated neurons may contribute disproportionately to the LFP signal, biasing activity in the larger nearby neuronal population to appear synchronous and cotuned with these neurons. To address this problem, we used linear filtering techniques to remove features correlated with spike events from LFP recordings. This filtering procedure can be applied for well-isolated single units or multiunit activity. We illustrate the effects of this correction in simulation and on spike data recorded from primary auditory cortex. We find that local spiking activity can explain a significant portion of LFP power at most recording sites and demonstrate that removing the spike-correlated component can affect measurements of auditory tuning of the LFP.

Sign in / Sign up

Export Citation Format

Share Document