Plasticity of neocortical synapses enables transitions between rate and temporal coding. In: Malsburg C, Seelen W, Vorbrüggen JC, Sendhoff B, editors. Artificial Neural Networks — ICANN 96. Berlin: Springer Berlin Heidelberg; 1996. pp. 445-50. [DOI: 10.1007/3-540-61510-5_77]

The role of adaptation in generating monotonic rate codes in auditory cortex

In primary auditory cortex, slowly repeated acoustic events are represented temporally by the stimulus-locked activity of single neurons. Single-unit studies in awake marmosets (Callithrix jacchus) have shown that a sub-population of these neurons also monotonically increase or decrease their average discharge rate during stimulus presentation for higher repetition rates. Building on a computational single-neuron model that generates stimulus-locked responses with stimulus evoked excitation followed by strong inhibition, we find that stimulus-evoked short-term depression is sufficient to produce synchronized monotonic positive and negative responses to slowly repeated stimuli. By exploring model robustness and comparing it to other models for adaptation to such stimuli, we conclude that short-term depression best explains our observations in single-unit recordings in awake marmosets. Together, our results show how a simple biophysical mechanism in single neurons can generate complementary neural codes for acoustic stimuli.

SEFRON: A New Spiking Neuron Model With Time-Varying Synaptic Efficacy Function for Pattern Classification

This paper presents a new time-varying long-term Synaptic Efficacy Function-based leaky-integrate-and-fire neuRON model, referred to as SEFRON and its supervised learning rule for pattern classification problems. The time-varying synaptic efficacy function is represented by a sum of amplitude modulated Gaussian distribution functions located at different times. For a given pattern, the SEFRON's learning rule determines the changes in the amplitudes of weights at selected presynaptic spike times by minimizing a new error function reflecting the differences between the desired and actual postsynaptic firing times. Similar to the gamma-aminobutyric acid-switch phenomenon observed in a biological neuron that switches between excitatory and inhibitory postsynaptic potentials based on the physiological needs, the time-varying synapse model proposed in this paper allows the synaptic efficacy (weight) to switch signs in a continuous manner. The computational power and the functioning of SEFRON are first illustrated using a binary pattern classification problem. The detailed performance comparisons of a single SEFRON classifier with other spiking neural networks (SNNs) are also presented using four benchmark data sets from the UCI machine learning repository. The results clearly indicate that a single SEFRON provides a similar generalization performance compared to other SNNs with multiple layers and multiple neurons.

Mean-field theory of globally coupled integrate-and-fire neural oscillators with dynamic synapses

We analyze the effects of synaptic depression or facilitation on the existence and stability of the splay or asynchronous state in a population of all-to-all, pulse-coupled neural oscillators. We use mean-field techniques to derive conditions for the local stability of the splay state and determine how stability depends on the degree of synaptic depression or facilitation. We also consider the effects of noise. Extensions of the mean-field results to finite networks are developed in terms of the nonlinear firing time map.

Neural networks with dynamic synapses

Transmission across neocortical synapses depends on the frequency of presynaptic activity (Thomson & Deuchars, 1994). Interpyramidal synapses in layer V exhibit fast depression of synaptic transmission, while other types of synapses exhibit facilitation of transmission. To study the role of dynamic synapses in network computation, we propose a unified phenomenological model that allows computation of the postsynaptic current generated by both types of synapses when driven by an arbitrary pattern of action potential (AP) activity in a presynaptic population. Using this formalism, we analyze different regimes of synaptic transmission and demonstrate that dynamic synapses transmit different aspects of the presynaptic activity depending on the average presynaptic frequency. The model also allows for derivation of mean-field equations, which govern the activity of large, interconnected networks. We show that the dynamics of synaptic transmission results in complex sets of regular and irregular regimes of network activity.

Differential signaling via the same axon of neocortical pyramidal neurons

The nature of information stemming from a single neuron and conveyed simultaneously to several hundred target neurons is not known. Triple and quadruple neuron recordings revealed that each synaptic connection established by neocortical pyramidal neurons is potentially unique. Specifically, synaptic connections onto the same morphological class differed in the numbers and dendritic locations of synaptic contacts, their absolute synaptic strengths, as well as their rates of synaptic depression and recovery from depression. The same axon of a pyramidal neuron innervating another pyramidal neuron and an interneuron mediated frequency-dependent depression and facilitation, respectively, during high frequency discharges of presynaptic action potentials, suggesting that the different natures of the target neurons underlie qualitative differences in synaptic properties. Facilitating-type synaptic connections established by three pyramidal neurons of the same class onto a single interneuron, were all qualitatively similar with a combination of facilitation and depression mechanisms. The time courses of facilitation and depression, however, differed for these convergent connections, suggesting that different pre-postsynaptic interactions underlie quantitative differences in synaptic properties. Mathematical analysis of the transfer functions of frequency-dependent synapses revealed supra-linear, linear, and sub-linear signaling regimes in which mixtures of presynaptic rates, integrals of rates, and derivatives of rates are transferred to targets depending on the precise values of the synaptic parameters and the history of presynaptic action potential activity. Heterogeneity of synaptic transfer functions therefore allows multiple synaptic representations of the same presynaptic action potential train and suggests that these synaptic representations are regulated in a complex manner. It is therefore proposed that differential signaling is a key mechanism in neocortical information processing, which can be regulated by selective synaptic modifications.

The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability

Although signaling between neurons is central to the functioning of the brain, we still do not understand how the code used in signaling depends on the properties of synaptic transmission. Theoretical analysis combined with patch clamp recordings from pairs of neocortical pyramidal neurons revealed that the rate of synaptic depression, which depends on the probability of neurotransmitter release, dictates the extent to which firing rate and temporal coherence of action potentials within a presynaptic population are signaled to the postsynaptic neuron. The postsynaptic response primarily reflects rates of firing when depression is slow and temporal coherence when depression is fast. A wide range of rates of synaptic depression between different pairs of pyramidal neurons was found, suggesting that the relative contribution of rate and temporal signals varies along a continuum. We conclude that by setting the rate of synaptic depression, release probability is an important factor in determining the neural code.