In reward-based learning, synaptic modifications depend on a brief stimulus and a temporally delayed reward, which poses the question of how synaptic activity patterns associate with a delayed reward. A theoretical solution to this so-called “distal reward problem” has been the notion of activity-generated ‘synaptic eligibility traces’, silent and transient synaptic tags that can be converted into long-term changes in synaptic strength by reward-linked neuromodulators. Here we report the first experimental demonstration of eligibility traces in cortical synapses. We demonstrate the Hebbian induction of distinct traces for LTP and LTD and their subsequent timing-dependent transformation into lasting changes by specific monoaminergic receptors anchored to postsynaptic proteins. Notably, the temporal properties of these transient traces allow stable learning in a recurrent neural network that accurately predicts the timing of the reward, further validating the induction/transformation of eligibility traces for LTP and LTD as a plausible synaptic substrate for reward-based learning.
Models of firing rate homeostasis such as synaptic scaling and the sliding synaptic plasticity modification threshold predict that decreasing neuronal activity (e.g. by sensory deprivation) will enhance synaptic function. Manipulations of cortical activity during two forms of visual deprivation (dark exposure (DE) and binocular lid suture (BS)) revealed that, contrary to expectations, spontaneous firing in conjunction with loss of visual input is necessary to lower the threshold for Hebbian plasticity and increases mEPSC amplitude. Blocking activation of GluN2B receptors, which are up-regulated by DE, also prevents the increase in mEPSC amplitude, suggesting that DE potentiates mEPSCs primarily through a Hebbian mechanism, not through synaptic scaling. Nevertheless, NMDAR-independent changes in mEPSC amplitude consistent with synaptic scaling could be induced by extreme reductions of activity. Therefore, two distinct mechanisms operate within different ranges of neuronal activity to homeostatically regulate synaptic strength.
The conventional interpretation of spikes is from the perspective of an external observer with knowledge of a neuron’s inputs and outputs who is ignorant of the contents of the “black box” that is the neuron. Here we consider a neuron to be an observer and we interpret spikes from the neuron’s perspective. We propose both a descriptive hypothesis based on physics and logic, and a prescriptive hypothesis based on biological optimality. Our descriptive hypothesis is that a neuron’s membrane excitability is “known” and the amplitude of a future excitatory postsynaptic conductance (EPSG) is “unknown”. Therefore excitability is an expectation of EPSG amplitude and a spike is generated only when EPSG amplitude exceeds its expectation (“prediction error”). Our prescriptive hypothesis is that a diversity of synaptic inputs and voltage-regulated ion channels implement “predictive homeostasis”, working to insure that the expectation is accurate. The homeostatic ideal and optimal expectation would be achieved when an EPSP reaches precisely to spike threshold, so that spike output is exquisitely sensitive to small variations in EPSG input. To an external observer who knows neither EPSG amplitude nor membrane excitability, spikes would appear random if the neuron is making accurate predictions. We review experimental evidence that spike probabilities are indeed maintained near an average of 0.5 under natural conditions, and we suggest that the same principles may also explain why synaptic vesicle release appears to be “stochastic”. Whereas the present hypothesis accords with principles of efficient coding dating back to Barlow (1961), it contradicts decades of assertions that neural activity is substantially “random” or “noisy”. The apparent randomness is by design, and like many other examples of apparent randomness, it corresponds to the ignorance of external macroscopic observers about the detailed inner workings of a microscopic system.
A general theory views the function of all neurons as prediction, and one component of this theory is that of “predictive homeostasis” or “prediction error.” It is well established that sensory systems adapt so that neuronal output maintains sensitivity to sensory input, in accord with information theory. Predictive homeostasis applies the same principle at the cellular level, where the challenge is to maintain membrane excitability at the optimal homeostatic level so that spike generation is maximally sensitive to small gradations in synaptic drive. Negative feedback is a hallmark of homeostatic mechanisms, as exemplified by depolarization-activated potassium channels. In contrast, T-type calcium channels exhibit positive feedback that appears at odds with the theory. In thalamocortical neurons of lateral geniculate nucleus (LGN), T-type channels are capable of causing bursts of spikes with an all-or-none character in response to excitation from a hyperpolarized potential. This “burst mode” would partially uncouple visual input from spike output and reduce the information spikes convey about gradations in visual input. However, past observations of T-type-driven bursts may have resulted from unnaturally high membrane excitability. Here we have mimicked within rat brain slices the patterns of synaptic conductance that occur naturally during vision. In support of the theory of predictive homeostasis, we found that T-type channels restored excitability toward its homeostatic level during periods of hyperpolarization. Thus, activation of T-type channels allowed two retinal input spikes to cause one output spike on average, and we observed almost no instances in which output count exceeded input count (a “burst”). T-type calcium channels therefore help to maintain a single optimal mode of transmission rather than creating a second mode. More fundamentally our results support the general theory, which seeks to predict the properties of a neuron's ion channels and synapses given knowledge of natural patterns of synaptic input.
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