Neural coding is a key problem in neuroscience aimed to understand the information processing mechanism in brain. Among the classical theories of neural coding, population rate coding has been studied widely in many w...
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Neural coding is a key problem in neuroscience aimed to understand the information processing mechanism in brain. Among the classical theories of neural coding, population rate coding has been studied widely in many works. In computational studies, neurons are usually classified into excitatory or inhibitory ones. Excitatory neurons have excitatory output synapses, and inhibitory neurons have inhibitory output synapses. However, according to physiological observations, neurons potentially have both types of output synapses. Thus, in this paper, neuronal networks consisting of neurons with mixed excitatory-inhibitory synapses are constructed to investigate the population rate coding fidelity of neuronal systems. It is revealed that, under intermediate values of recurrent probability, inhibitory-excitatory strength ratio, and noise intensity, the performance of population rate coding could be improved by both excitatory synaptic strength and synaptic time constant. It is indicated that external stimuli can be encoded in the form of population firing rate by the studied neuronal networks very well. What is more exciting is that we find the neuronal networks considered in our work have higher coding efficiency than the traditional ones. Therefore, neurons with mixed excitatory-inhibitory synapses may be much more rational.
Neuron transmits spikes to postsynaptic neurons through synapses. Experimental observations indicated that the communication between neurons is unreliable. However most modelling and computational studies considered d...
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Neuron transmits spikes to postsynaptic neurons through synapses. Experimental observations indicated that the communication between neurons is unreliable. However most modelling and computational studies considered deterministic synaptic interaction model. In this paper, we investigate the population rate coding in an all-to-all coupled recurrent neuronal network consisting of both excitatory and inhibitory neurons connected with unreliable synapses. We use a stochastic on-off process to model the unreliable synaptic transmission. We find that synapses with suitable successful transmission probability can enhance the encoding performance in the case of weak noise;while in the case of strong noise, the synaptic interactions reduce the encoding performance. We also show that several important synaptic parameters, such as the excitatory synaptic strength, the relative strength of inhibitory and excitatory synapses, as well as the synaptic time constant, have significant effects on the performance of the population rate coding. Further simulations indicate that the encoding dynamics of our considered network cannot be simply determined by the average amount of received neurotransmitter for each neuron in a time instant. Moreover, we compare our results with those obtained in the corresponding random neuronal networks. Our numerical results demonstrate that the network randomness has the similar qualitative effect as the synaptic unreliability but not completely equivalent in quantity.
Uncovering the principle of neural coding is essential for understanding how our mysterious brain works. Recent studies have reported the laminar differences of alpha-beta and gamma rhythms in the sensory cortex, yet ...
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Uncovering the principle of neural coding is essential for understanding how our mysterious brain works. Recent studies have reported the laminar differences of alpha-beta and gamma rhythms in the sensory cortex, yet it remains unclear about the underlying function role of frequency-dependent interlaminar interactions in neural coding. Using a rate-based network model to simulate the cortical laminar under the external time-varying stimuli, we showed that the physiological specificity of rhythms for layers enables the cortical laminae to preferentially encode information in different frequency ranges. The interplay of the supragranular layer and infragranular layer contributes significantly to improving the neural representation of external time-varying input at the population level. Further investigations revealed the essential role of recurrent connections of the cortical laminae in regulating the population rate coding. In particular, the laminar network optimally encodes the time-varying input at intermediate strengths of intralaminar excitatory-inhibitory circuits and interlaminar connections. Additionally, we verified the crucial role of adaptation in improving populationcoding by introducing slow dynamics and suppressing the noise-like excitatory activity in the laminar network. These findings highlight the crucial role of frequency-dependent interlaminar interactions in encoding time-varying stimuli and may shed light on the underlying function of cortical structural specificity in neural information processing.
population rate coding and temporal coding are common neural codes. Recent studies suggest that these two codes may be alternatively used in one neural system. Based on the fact that there are massive gap junctions in...
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population rate coding and temporal coding are common neural codes. Recent studies suggest that these two codes may be alternatively used in one neural system. Based on the fact that there are massive gap junctions in the brain, we explore how this switching behavior may be related to neural codes in networks of neurons connected by gap junctions. First, we show that under time-varying inputs, such neural networks show switching between synchronous and asynchronous states. Then, we quantify network dynamics by three mutual information measures to show that population rate coding carries more information in asynchronous states and temporal coding does so in synchronous states. (c) 2006 Elsevier Ltd. All rights reserved.
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