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 Attentional modulation of intrinsic timescales in visual cortex and spatial networks

2021 ◽  
Author(s):  
Roxana Zeraati ◽  
Yan-Liang Shi ◽  
Nicholas A Steinmetz ◽  
Marc A Gieselmann ◽  
Alexander Thiele ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Cognitive Task ◽  
Temporal Integration ◽  
Receptive Fields ◽  
Recurrent Network ◽  
Area V4 ◽  
Attention Tasks ◽  
Underlying Network ◽  
Network Mechanism ◽  
Spiking Activity

Neural activity fluctuates endogenously on timescales varying across the neocortex. The variation in these intrinsic timescales relates to the functional specialization of cortical areas and their involvement in the temporal integration of information. Yet, it is unknown whether the timescales can adjust rapidly and selectively to the demands of a cognitive task. We measured intrinsic timescales of local spiking activity within columns of area V4 while monkeys performed spatial attention tasks. The ongoing spiking activity unfolded across at least two distinct timescales---fast and slow---and the slow timescale increased when monkeys attended to the receptive fields location. A recurrent network model shows that multiple timescales in local dynamics arise from spatial connectivity mimicking vertical and horizontal interactions in visual cortex and that slow timescales increase with the efficacy of recurrent interactions. Our results reveal that targeted neural populations integrate information over variable timescales following the demands of a cognitive task and propose an underlying network mechanism.

scholarly journals Learning recurrent dynamics in spiking networks

2018 ◽  
Author(s):  
Christopher M. Kim ◽  
Carson C. Chow
Keyword(s):  
Cortical Neurons ◽  
Activity Patterns ◽  
Sufficient Conditions ◽  
Motor Planning ◽  
Network Capacity ◽  
Recurrent Network ◽  
Recursive Least Squares ◽  
Wide Range ◽  
Spiking Networks ◽  
Spiking Activity

AbstractSpiking activity of neurons engaged in learning and performing a task show complex spatiotemporal dynamics. While the output of recurrent network models can learn to perform various tasks, the possible range of recurrent dynamics that emerge after learning remains unknown. Here we show that modifying the recurrent connectivity with a recursive least squares algorithm provides sufficient flexibility for synaptic and spiking rate dynamics of spiking networks to produce a wide range of spatiotemporal activity. We apply the training method to learn arbitrary firing patterns, stabilize irregular spiking activity of a balanced network, and reproduce the heterogeneous spiking rate patterns of cortical neurons engaged in motor planning and movement. We identify sufficient conditions for successful learning, characterize two types of learning errors, and assess the network capacity. Our findings show that synaptically-coupled recurrent spiking networks possess a vast computational capability that can support the diverse activity patterns in the brain.

scholarly journals Network Model With Reduced Metabolic Rate Predicts Spatial Synchrony of Neuronal Activity

2021 ◽  
Vol 15 ◽  
Author(s):  
Pangyu Joo ◽  
Heonsoo Lee ◽  
Shiyong Wang ◽  
Seunghwan Kim ◽  
Anthony G. Hudetz
Keyword(s):  
Metabolic Rate ◽  
Network Model ◽  
Cortical Neurons ◽  
Activity Patterns ◽  
Oscillatory Activity ◽  
Population Activity ◽  
Atp Production ◽  
Rate Oscillation ◽  
Biophysical Mechanism ◽  
Hypometabolic State

In a cerebral hypometabolic state, cortical neurons exhibit slow synchronous oscillatory activity with sparse firing. How such a synchronization spatially organizes as the cerebral metabolic rate decreases have not been systemically investigated. We developed a network model of leaky integrate-and-fire neurons with an additional dependency on ATP dynamics. Neurons were scattered in a 2D space, and their population activity patterns at varying ATP levels were simulated. The model predicted a decrease in firing activity as the ATP production rate was lowered. Under hypometabolic conditions, an oscillatory firing pattern, that is, an ON-OFF cycle arose through a failure of sustainable firing due to reduced excitatory positive feedback and rebound firing after the slow recovery of ATP concentration. The firing rate oscillation of distant neurons developed at first asynchronously that changed into burst suppression and global synchronization as ATP production further decreased. These changes resembled the experimental data obtained from anesthetized rats, as an example of a metabolically suppressed brain. Together, this study substantiates a novel biophysical mechanism of neuronal network synchronization under limited energy supply conditions.

scholarly journals Learning temporal integration from internal feedback

2019 ◽  
Author(s):  
Erik Nygren ◽  
Alexandro Ramirez ◽  
Brandon McMahan ◽  
Emre Aksay ◽  
Walter Senn
Keyword(s):  
Temporal Integration ◽  
Oculomotor System ◽  
Recurrent Network ◽  
Independent Learning ◽  
Oculomotor Control ◽  
List Type ◽  
Population Activity ◽  
Visual Inputs ◽  
Low Pass ◽  
Teaching Signal

AbstractThere has been much focus on the mechanisms of temporal integration, but little on how circuits learn to integrate. In the adult oculomotor system, where a neural integrator maintains fixations, changes in integration dynamics can be driven by visual error signals. However, we show through dark-rearing experiments that visual inputs are not necessary for initial integrator development. We therefore propose a vision-independent learning mechanism whereby a recurrent network learns to integrate via a ‘teaching’ signal formed by low-pass filtered feedback of its population activity. The key is the segregation of local recurrent inputs onto a dendritic compartment and teaching inputs onto a somatic compartment of an integrator neuron. Model instantiation for oculomotor control shows how a self-corrective teaching signal through the cerebellum can generate an integrator with both the dynamical and tuning properties necessary to drive eye muscles and maintain gaze angle. This bootstrap learning paradigm may be relevant for development and plasticity of temporal integration more generally.Highlights- A neuronal architecture that learns to integrate saccadic commands for eye position.- Learning is based on the recurrent dendritic prediction of somatic teaching signals.- Experiment and model show that no visual feedback is required for initial integrator learning.- Cerebellum is an internal teacher for motor nuclei and integrator population.

scholarly journals Motor cortex is an input-driven dynamical system controlling dexterous movement

2018 ◽  
Author(s):  
Britton Sauerbrei ◽  
Jian-Zhong Guo ◽  
Matteo Mischiati ◽  
Wendy Guo ◽  
Mayank Kabra ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Activity Patterns ◽  
Large Body ◽  
Cortical Activity ◽  
Brain Regions ◽  
External Input ◽  
Time Varying ◽  
Movement Initiation ◽  
Population Activity ◽  
Motor Cortical

AbstractSkillful control of movement is central to our ability to sense and manipulate the world. A large body of work in nonhuman primates has demonstrated that motor cortex provides flexible, time-varying activity patterns that control the arm during reaching and grasping. Previous studies have suggested that these patterns are generated by strong local recurrent dynamics operating autonomously from inputs during movement execution. An alternative possibility is that motor cortex requires coordination with upstream brain regions throughout the entire movement in order to yield these patterns. Here, we developed an experimental preparation in the mouse to directly test these possibilities using optogenetics and electrophysiology during a skilled reach-to-grab-to-eat task. To validate this preparation, we first established that a specific, time-varying pattern of motor cortical activity was required to produce coordinated movement. Next, in order to disentangle the contribution of local recurrent motor cortical dynamics from external input, we optogenetically held the recurrent contribution constant, then observed how motor cortical activity recovered following the end of this perturbation. Both the neural responses and hand trajectory varied from trial to trial, and this variability reflected variability in external inputs. To directly probe the role of these inputs, we used optogenetics to perturb activity in the thalamus. Thalamic perturbation at the start of the trial prevented movement initiation, and perturbation at any stage of the movement prevented progression of the hand to the target; this demonstrates that input is required throughout the movement. By comparing motor cortical activity with and without thalamic perturbation, we were able to estimate the effects of external inputs on motor cortical population activity. Thus, unlike pattern-generating circuits that are local and autonomous, such as those in the spinal cord that generate left-right alternation during locomotion, the pattern generator for reaching and grasping is distributed across multiple, strongly-interacting brain regions.

Adding Lateral Inhibition to a Simple Feedforward Network Enables It to Perform Exclusive-Or

1998 ◽  
Vol 10 (2) ◽  
pp. 277-280
Author(s):  
Leslie S. Smith
Keyword(s):  
Lateral Inhibition ◽  
Recurrent Network ◽  
External Input ◽  
Output Function ◽  
Output Unit ◽  
Feedforward Network ◽  
Point Attractor ◽  
Exclusive Or

A simple laterally inhibited recurrent network that implementse xclusive-or is demonstrated. The network consists of two mutually inhibitory units with logistic output function, each receiving one external input and each connected to a simple threshold output unit. The mutually inhibitory units settle into a point attractor. We investigate the range of steepness of the logistic and the range of inhibitory weights for which the network can perform exclusive-or.

scholarly journals Growth and structural discrimination of cortical neurons on randomly oriented and vertically aligned dense carbon nanotube networks

2014 ◽  
Vol 5 ◽  
pp. 1575-1579 ◽  
Author(s):  
Christoph Nick ◽  
Sandeep Yadav ◽  
Ravi Joshi ◽  
Christiane Thielemann ◽  
Jörg J Schneider
Keyword(s):  
Carbon Nanotubes ◽  
Carbon Nanotube ◽  
Cortical Neurons ◽  
Three Dimensional ◽  
Vertically Aligned Carbon Nanotubes ◽  
Aligned Carbon Nanotubes ◽  
Vertically Aligned ◽  
The Individual ◽  
Carbon Nanotube Networks ◽  
Communication Paths

The growth of cortical neurons on three dimensional structures of spatially defined (structured) randomly oriented, as well as on vertically aligned, carbon nanotubes (CNT) is studied. Cortical neurons are attracted towards both types of CNT nano-architectures. For both, neurons form clusters in close vicinity to the CNT structures whereupon the randomly oriented CNTs are more closely colonised than the CNT pillars. Neurons develop communication paths via neurites on both nanoarchitectures. These neuron cells attach preferentially on the CNT sidewalls of the vertically aligned CNT architecture instead than onto the tips of the individual CNT pillars.

The frame/content theory of evolution of speech production

1998 ◽  
Vol 21 (4) ◽  
pp. 499-511 ◽  
Author(s):  
Peter F. MacNeilage
Keyword(s):  
Speech Production ◽  
Vocal Communication ◽  
Vocal Learning ◽  
General Purpose ◽  
External Input ◽  
Anterior Cingulate ◽  
Broca’S Area ◽  
Broca's Area ◽  
Theory Of Evolution ◽  
Species Specific

The species-specific organizational property of speech is a continual mouth open-close alternation, the two phases of which are subject to continual articulatory modulation. The cycle constitutes the syllable, and the open and closed phases are segments – vowels and consonants, respectively. The fact that segmental serial ordering errors in normal adults obey syllable structure constraints suggests that syllabic “frames” and segmental “content” elements are separately controlled in the speech production process. The frames may derive from cycles of mandibular oscillation present in humans from babbling onset, which are responsible for the open-close alternation. These communication- related frames perhaps first evolved when the ingestion-related cyclicities of mandibular oscillation (associated with mastication [chewing] sucking and licking) took on communicative significance as lipsmacks, tonguesmacks, and teeth chatters – displays that are prominent in many nonhuman primates. The new role of Broca's area and its surround in human vocal communication may have derived from its evolutionary history as the main cortical center for the control of ingestive processes. The frame and content components of speech may have subsequently evolved separate realizations within two general purpose primate motor control systems: (1) a motivation-related medial “intrinsic” system, including anterior cingulate cortex and the supplementary motor area, for self-generated behavior, formerly responsible for ancestral vocalization control and now also responsible for frames, and (2) a lateral “extrinsic” system, including Broca's area and surround, and Wernicke's area, specialized for response to external input (and therefore the emergent vocal learning capacity) and more responsible for content.

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