scholarly journals The generation of cortical novelty responses through inhibitory plasticity

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Auguste Schulz ◽  
Christoph Miehl ◽  
Michael J Berry ◽  
Julijana Gjorgjieva
Keyword(s):  
Spike Timing ◽  
Inhibitory Neurons ◽  
Excitatory Synapses ◽  
Spiking Network ◽  
Reliable Detection ◽  
Plausible Mechanism ◽  
Novelty Response ◽  
Inhibitory Plasticity ◽  
Familiar Stimuli ◽  
Synaptic Mechanisms

Animals depend on fast and reliable detection of novel stimuli in their environment. Neurons in multiple sensory areas respond more strongly to novel in comparison to familiar stimuli. Yet, it remains unclear which circuit, cellular, and synaptic mechanisms underlie those responses. Here, we show that spike-timing-dependent plasticity of inhibitory-to-excitatory synapses generates novelty responses in a recurrent spiking network model. Inhibitory plasticity increases the inhibition onto excitatory neurons tuned to familiar stimuli, while inhibition for novel stimuli remains low, leading to a network novelty response. The generation of novelty responses does not depend on the periodicity but rather on the distribution of presented stimuli. By including tuning of inhibitory neurons, the network further captures stimulus-specific adaptation. Finally, we suggest that disinhibition can control the amplification of novelty responses. Therefore, inhibitory plasticity provides a flexible, biologically plausible mechanism to detect the novelty of bottom-up stimuli, enabling us to make experimentally testable predictions.

scholarly journals The generation of cortical novelty responses through inhibitory plasticity

2020 ◽  
Author(s):  
Auguste Schulz ◽  
Christoph Miehl ◽  
Michael J. Berry ◽  
Julijana Gjorgjieva
Keyword(s):  
Temporal Structure ◽  
Inhibitory Neurons ◽  
Specific Adaptation ◽  
Spiking Network ◽  
Reliable Detection ◽  
Plausible Mechanism ◽  
Novelty Response ◽  
Inhibitory Plasticity ◽  
Familiar Stimuli ◽  
Synaptic Mechanisms

AbstractAnimals depend on fast and reliable detection of novel stimuli in their environment. Indeed, neurons in multiple sensory areas respond more strongly to novel in comparison to familiar stimuli. Yet, it remains unclear which circuit, cellular and synaptic mechanisms underlie those responses. Here, we show that inhibitory synaptic plasticity readily generates novelty responses in a recurrent spiking network model. Inhibitory plasticity increases the inhibition onto excitatory neurons tuned to familiar stimuli, while inhibition for novel stimuli remains low, leading to a network novelty response. Generated novelty responses do not depend on the exact temporal structure but rather on the distribution of presented stimuli. By including tuning of inhibitory neurons, the network further captures stimulus-specific adaptation. Finally, we suggest that disinhibition can control the amplification of novelty responses. Therefore, inhibitory plasticity provides a flexible, biologically-plausible mechanism to detect the novelty of bottom-up stimuli, enabling us to make numerous experimentally testable predictions.

Organization and Plasticity of Cortical Inhibition

2017 ◽  
Author(s):  
Bernard Kripkee ◽  
Robert C. Froemke
Keyword(s):  
Neural Activity ◽  
Cortical Inhibition ◽  
Network Activity ◽  
Inhibitory Neurons ◽  
Inhibitory Synapses ◽  
Inhibitory Interneurons ◽  
Excitatory Synapses ◽  
Different Types ◽  
Inhibitory Plasticity

Plasticity of inhibitory synapses keeps inhibition in balance and in register with excitation when changes occur in excitatory synapses. Inhibition has many functions to perform, and there are many kinds of inhibitory neurons to perform various computations and regulate network activity. Different forms of long-term changes in inhibitory synapses have been demonstrated that depend on neural activity. Inhibitory plasticity appears to be partly responsible for the specificity of the inhibitory connections needed to carry out some inhibitory functions. The evolving story of cortical inhibitory plasticity shows that different types of inhibitory interneurons play different roles in a variety of inhibitory functions, that several types of inhibitory plasticity have been attested, and that different forms of plasticity can be expected to have different effects on the organization and specificity of inhibitory connections.

scholarly journals A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Karim El-Laithy ◽  
Martin Bogdan
Keyword(s):  
Reinforcement Learning ◽  
Spike Timing ◽  
Neural Representation ◽  
Model Parameters ◽  
Learning Framework ◽  
Reference Target ◽  
Wide Range ◽  
Spiking Network ◽  
Dynamic Synapses ◽  
Exclusive Or

An integration of both the Hebbian-based and reinforcement learning (RL) rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing.

Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning

2001 ◽  
Vol 13 (10) ◽  
pp. 2221-2237 ◽  
Author(s):  
Rajesh P. N. Rao ◽  
Terrence J. Sejnowski
Keyword(s):  
Cortical Neuron ◽  
Action Potentials ◽  
Learning Rule ◽  
Spike Timing ◽  
Biophysical Model ◽  
Excitatory Synapses ◽  
Temporal Difference ◽  
Temporal Difference Learning ◽  
Neocortical Neurons ◽  
Hebbian Plasticity

A spike-timing-dependent Hebbian mechanism governs the plasticity of recurrent excitatory synapses in the neocortex: synapses that are activated a few milliseconds before a postsynaptic spike are potentiated, while those that are activated a few milliseconds after are depressed. We show that such a mechanism can implement a form of temporal difference learning for prediction of input sequences. Using a biophysical model of a cortical neuron, we show that a temporal difference rule used in conjunction with dendritic backpropagating action potentials reproduces the temporally asymmetric window of Hebbian plasticity observed physiologically. Furthermore, the size and shape of the window vary with the distance of the synapse from the soma. Using a simple example, we show how a spike-timing-based temporal difference learning rule can allow a network of neocortical neurons to predict an input a few milliseconds before the input's expected arrival.

Fast Inhibition Alters First Spike Timing in Auditory Brainstem Neurons

2004 ◽  
Vol 92 (4) ◽  
pp. 2615-2621 ◽  
Author(s):  
Antonio G. Paolini ◽  
Janine C. Clarey ◽  
Karina Needham ◽  
Graeme M. Clark
Keyword(s):  
Lateral Inhibition ◽  
Cochlear Nucleus ◽  
Stellate Cell ◽  
Discharge Rate ◽  
Stellate Cells ◽  
Auditory Pathway ◽  
Auditory Brainstem ◽  
Spike Timing ◽  
Inhibitory Neurons

Within the first processing site of the central auditory pathway, inhibitory neurons (D stellate cells) broadly tuned to tonal frequency project on narrowly tuned, excitatory output neurons (T stellate cells). The latter is thought to provide a topographic representation of sound spectrum, whereas the former is thought to provide lateral inhibition that improves spectral contrast, particularly in noise. In response to pure tones, the overall discharge rate in T stellate cells is unlikely to be suppressed dramatically by D stellate cells because they respond primarily to stimulus onset and provide fast, short-duration inhibition. In vivo intracellular recordings from the ventral cochlear nucleus (VCN) showed that, when tones were presented above or below the characteristic frequency (CF) of a T stellate neuron, they were inhibited during depolarization. This resulted in a delay in the initial action potential produced by T stellate cells. This ability of fast inhibition to alter the first spike timing of a T stellate neuron was confirmed by electrically activating the D stellate cell pathway that arises in the contralateral cochlear nucleus. Delay was also induced when two tones were presented: one at CF and one outside the frequency response area of the T stellate neuron. These findings suggest that the traditional view of lateral inhibition within the VCN should incorporate delay as one of its principle outcomes.

scholarly journals Circuit and synaptic organization of forebrain-to-midbrain pathways that promote and suppress vocalization

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Valerie Michael ◽  
Jack Goffinet ◽  
John Pearson ◽  
Fan Wang ◽  
Katherine Tschida ◽  
...  
Keyword(s):  
Preoptic Area ◽  
Brain Slices ◽  
Social Cues ◽  
Ultrasonic Vocalizations ◽  
Behavioral Effects ◽  
Inhibitory Neurons ◽  
Boundary Zone ◽  
Synaptic Organization ◽  
Two Populations ◽  
Synaptic Mechanisms

Animals vocalize only in certain behavioral contexts, but the circuits and synapses through which forebrain neurons trigger or suppress vocalization remain unknown. Here, we used transsynaptic tracing to identify two populations of inhibitory neurons that lie upstream of neurons in the periaqueductal gray (PAG) that gate the production of ultrasonic vocalizations (USVs) in mice (i.e. PAG-USV neurons). Activating PAG-projecting neurons in the preoptic area of the hypothalamus (POAPAG neurons) elicited USV production in the absence of social cues. In contrast, activating PAG-projecting neurons in the central-medial boundary zone of the amygdala (AmgC/M-PAG neurons) transiently suppressed USV production without disrupting non-vocal social behavior. Optogenetics-assisted circuit mapping in brain slices revealed that POAPAG neurons directly inhibit PAG interneurons, which in turn inhibit PAG-USV neurons, whereas AmgC/M-PAG neurons directly inhibit PAG-USV neurons. These experiments identify two major forebrain inputs to the PAG that trigger and suppress vocalization, respectively, while also establishing the synaptic mechanisms through which these neurons exert opposing behavioral effects.

scholarly journals Balanced networks under spike-time dependent plasticity

2021 ◽  
Vol 17 (5) ◽  
pp. e1008958
Author(s):  
Alan Eric Akil ◽  
Robert Rosenbaum ◽  
Krešimir Josić
Keyword(s):  
Spike Timing ◽  
Recurrent Networks ◽  
Spike Time ◽  
Weight Changes ◽  
Initial State ◽  
Dependent Plasticity ◽  
Plausible Mechanism ◽  
Spike Time Dependent Plasticity ◽  
Induced Changes ◽  
Balanced Networks

The dynamics of local cortical networks are irregular, but correlated. Dynamic excitatory–inhibitory balance is a plausible mechanism that generates such irregular activity, but it remains unclear how balance is achieved and maintained in plastic neural networks. In particular, it is not fully understood how plasticity induced changes in the network affect balance, and in turn, how correlated, balanced activity impacts learning. How do the dynamics of balanced networks change under different plasticity rules? How does correlated spiking activity in recurrent networks change the evolution of weights, their eventual magnitude, and structure across the network? To address these questions, we develop a theory of spike–timing dependent plasticity in balanced networks. We show that balance can be attained and maintained under plasticity–induced weight changes. We find that correlations in the input mildly affect the evolution of synaptic weights. Under certain plasticity rules, we find an emergence of correlations between firing rates and synaptic weights. Under these rules, synaptic weights converge to a stable manifold in weight space with their final configuration dependent on the initial state of the network. Lastly, we show that our framework can also describe the dynamics of plastic balanced networks when subsets of neurons receive targeted optogenetic input.

scholarly journals Influence of Autapses on Synchronization in Neural Networks With Chemical Synapses

2020 ◽  
Vol 14 ◽  
Author(s):  
Paulo R. Protachevicz ◽  
Kelly C. Iarosz ◽  
Iberê L. Caldas ◽  
Chris G. Antonopoulos ◽  
Antonio M. Batista ◽  
...  
Keyword(s):  
Neural Networks ◽  
Random Network ◽  
Inhibitory Neurons ◽  
Neural Dynamics ◽  
Excitatory Synapses ◽  
Closed Loops ◽  
Integrate And Fire ◽  
Network Topologies ◽  
Chemical Synapses ◽  
Synchronous Behavior

A great deal of research has been devoted on the investigation of neural dynamics in various network topologies. However, only a few studies have focused on the influence of autapses, synapses from a neuron onto itself via closed loops, on neural synchronization. Here, we build a random network with adaptive exponential integrate-and-fire neurons coupled with chemical synapses, equipped with autapses, to study the effect of the latter on synchronous behavior. We consider time delay in the conductance of the pre-synaptic neuron for excitatory and inhibitory connections. Interestingly, in neural networks consisting of both excitatory and inhibitory neurons, we uncover that synchronous behavior depends on their synapse type. Our results provide evidence on the synchronous and desynchronous activities that emerge in random neural networks with chemical, inhibitory and excitatory synapses where neurons are equipped with autapses.

STDP Provides the Substrate for Igniting Synfire Chains by Spatiotemporal Input Patterns

2008 ◽  
Vol 20 (2) ◽  
pp. 415-435 ◽  
Author(s):  
Ryosuke Hosaka ◽  
Osamu Araki ◽  
Tohru Ikeguchi
Keyword(s):  
Neural Network ◽  
Experimental Studies ◽  
Spike Timing ◽  
Inhibitory Neurons ◽  
Spatiotemporal Pattern ◽  
Spiking Neural Network ◽  
Synfire Chains ◽  
The Neural Network ◽  
Stdp Rule ◽  
Neural Network Structure

Spike-timing-dependent synaptic plasticity (STDP), which depends on the temporal difference between pre- and postsynaptic action potentials, is observed in the cortices and hippocampus. Although several theoretical and experimental studies have revealed its fundamental aspects, its functional role remains unclear. To examine how an input spatiotemporal spike pattern is altered by STDP, we observed the output spike patterns of a spiking neural network model with an asymmetrical STDP rule when the input spatiotemporal pattern is repeatedly applied. The spiking neural network comprises excitatory and inhibitory neurons that exhibit local interactions. Numerical experiments show that the spiking neural network generates a single global synchrony whose relative timing depends on the input spatiotemporal pattern and the neural network structure. This result implies that the spiking neural network learns the transformation from spatiotemporal to temporal information. In the literature, the origin of the synfire chain has not been sufficiently focused on. Our results indicate that spiking neural networks with STDP can ignite synfire chains in the cortices.

scholarly journals Learning Precise Spike Train–to–Spike Train Transformations in Multilayer Feedforward Neuronal Networks

2016 ◽  
Vol 28 (5) ◽  
pp. 826-848 ◽  
Author(s):  
Arunava Banerjee
Keyword(s):  
Spike Train ◽  
Gradient Descent ◽  
Probabilistic Models ◽  
Spike Timing ◽  
Inhibitory Neurons ◽  
Closed Form Solutions ◽  
Learning Framework ◽  
Error Functional ◽  
The Error Functional ◽  
Quantities Of Interest

We derive a synaptic weight update rule for learning temporally precise spike train–to–spike train transformations in multilayer feedforward networks of spiking neurons. The framework, aimed at seamlessly generalizing error backpropagation to the deterministic spiking neuron setting, is based strictly on spike timing and avoids invoking concepts pertaining to spike rates or probabilistic models of spiking. The derivation is founded on two innovations. First, an error functional is proposed that compares the spike train emitted by the output neuron of the network to the desired spike train by way of their putative impact on a virtual postsynaptic neuron. This formulation sidesteps the need for spike alignment and leads to closed-form solutions for all quantities of interest. Second, virtual assignment of weights to spikes rather than synapses enables a perturbation analysis of individual spike times and synaptic weights of the output, as well as all intermediate neurons in the network, which yields the gradients of the error functional with respect to the said entities. Learning proceeds via a gradient descent mechanism that leverages these quantities. Simulation experiments demonstrate the efficacy of the proposed learning framework. The experiments also highlight asymmetries between synapses on excitatory and inhibitory neurons.

Sign in / Sign up

Export Citation Format

Share Document