In this and the following section we discuss temporal coding in specialized neuronal systems. We start in this section with the problem of electro-sensory signal processing in Mormoryd electric fish.
Mormoryd electric fish probe their environment with electric pulses. The electric organ of the fish emits a short electric discharge. The spatio-temporal electric field that is generated around the fish by the discharge depends on the location of objects in the surroundings. In order to reliably detect the location, size, or movement of objects the electro-sensory system must compare the momentary spatio-temporal electric field with the one that would occur in the absence of external objects. In other words, it must subtract the expected spatio-temporal image from the actual sensory input.
Experiments have shown that so-called medium ganglion cells in the electro-sensory lateral lobe (ELL) of electric fish can solve the task of subtracting expectations (Bell et al., 1997a). These cells receive two sets of input; cf. Fig. 12.10A. Information on the timing of a discharge pulse emitted by the electric organ is conveyed via a set of delay lines to the ganglion cells. The signal transmission delay between the electric organ discharge and spike arrival at the ganglion cell changes from one connection to the next and varies between zero and 100milliseconds. A second set of input conveys the characteristics of the spatio-temporal electric field sensed by the fish's electro-receptors.
In experiments, electric organ discharges are triggered repetitively at intervals of T = 150 ms. If the sensory input has, after each discharge, the same spatio-temporal characteristics, the ganglion cell responds with stochastic activity at a constant rate; cf. Fig. 12.11A. If the sensory input suddenly changes, the ganglion cell reacts strongly. Thus the ganglion cell can be seen as a novelty detector. The predictable contribution of the sensory image is subtracted, and only unpredictable aspects of a sensory image evoke a response. In the following paragraph we will show that a spike-time dependent learning rule with anti-Hebbian characteristics can solve the task of subtracting expectations (Roberts and Bell, 2000; Bell et al., 1997b).
In this section we review the model of Roberts and Bell (2000). We start with the model of the ganglion cell, turn then to the model of synaptic plasticity, and compare finally the model results with experimental results of ganglion cell activity.
We consider a single ganglion cell that receives two sets of inputs as indicated schematically in Fig. 12.10A. After each electric organ discharge, a volley of 150 input spikes arrives at different delays , .... Each spike evokes upon arrival an excitatory postsynaptic potential with time course (s). A second set of input carries the sensory stimulus. Instead of modeling the sequence of spike arrival times, the time course of the stimulus is summarized by a function hstim(s) where s = 0 is the moment of the electric organ discharge. The total membrane potential of the ganglion cell i is
A ganglion cell is described as a (semi-)linear Poisson model that emits spikes at a rate
The synaptic efficacy wij changes according to a spike-time dependent plasticity rule
A simulation of the model introduced above is shown in Fig. 12.11B. During the first 50 trials, no stimulus was applied ( hstim 0). In all subsequent trials up to trial 3500, an inhibitory stimulus hstim(s) with triangular time course has been applied. While the activity is clearly suppressed in trial 51, it recovers after several hundred repetitions of the experiment. If the stimulus is removed thereafter, the neuronal activity exhibits a negative after-image of the stimulus, just as in the experiments shown in Fig. 12.11A.
Using the methods developed in Chapter 11, it is possible to show that, for a1pre > 0 and < 0, the learning rule stabilizes the mean output rate (of broad spikes) at a fixed point = - a1pre/. Moreover, weights wij are adjusted so that the membrane potential has minimal fluctuations about uFP = + . To achieve this, the weights must be tuned so that the term wij (t - ) cancels the sensory input hstim(t) - which is the essence of sensory image cancellation (Roberts and Bell, 2000).
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