Rapid Memory Encoding in a Spiking Hippocampus Circuit Model.

Episodic memory is a recollection of past personal experiences associated with particular times and places. This kind of memory is commonly subject to loss of contextual information or “semantization,” which gradually decouples the encoded memory items from their associated contexts while transforming them into semantic or gist-like representations. Novel extensions to the classical Remember/Know (R/K) behavioral paradigm attribute the loss of episodicity to multiple exposures of an item in different contexts. Despite recent advancements explaining semantization at a behavioral level, the underlying neural mechanisms remain poorly understood. In this study, we suggest and evaluate a novel hypothesis proposing that Bayesian–Hebbian synaptic plasticity mechanisms might cause semantization of episodic memory. We implement a cortical spiking neural network model with a Bayesian–Hebbian learning rule called Bayesian Confidence Propagation Neural Network (BCPNN), which captures the semantization phenomenon and offers a mechanistic explanation for it. Encoding items across multiple contexts leads to item-context decoupling akin to semantization. We compare BCPNN plasticity with the more commonly used spike-timing-dependent plasticity (STDP) learning rule in the same episodic memory task. Unlike BCPNN, STDP does not explain the decontextualization process. We further examine how selective plasticity modulation of isolated salient events may enhance preferential retention and resistance to semantization. Our model reproduces important features of episodicity on behavioral timescales under various biological constraints while also offering a novel neural and synaptic explanation for semantization, thereby casting new light on the interplay between episodic and semantic memory processes.

The hippocampus

Effect of spike-timing-dependent plasticity on neural assembly computing

Enigmas of the Dentate Gyrus

Spike timing dependent plasticity (STDP) learning rule is one of hot topics in neurobiology since it's been widely believed that synaptic plasticity mainly contribute to learning and memory in brain. Up to now, STDP has been observed in a wide variety of areas of brain, hippocampus, cortex and so on. Competition among synapses is an important behavior for this learning rule. In present study, we propose a single layer spiking neural network model using STDP learning rule in inhibitory synapses to investigate the competitive behavior. The experiments show that the synapses among neurons are both strengthened on the whole training process. Thus neurons inhibit the activities of one another, eventually the neuron with the highest input spike rate win the competition. We have found that the behavior is efficient when the differences of firing rates of input neurons without STDP are great than 5Hz, otherwise the winner neuron is random. In order to use the principle to artificial intelligent system, we use a mechanism of dynamic learning rate to let the neuron with the highest input to be selected by the competitive behavior as the winner. Therefore, a robust competitive spiking neural network is obtained.

The CA3 region of the hippocampus: how is it? What is it for? How does it do it?

Phosphofructokinase, a glycolytic regulatory enzyme has a crucial role for maintenance of synaptic activity in guinea pig hippocampal slices

Convolutional Neural Networks(CNNs) have become the work horse for image classification tasks. This success has driven the exploration of Spike Time Dependent Plasticity (STDP) learning rule applied to the convolutional architecture for complex datasets as opposed to the fully connected architecture. Inhibitory neurons and adaptive threshold are widely adopted methods of inducing homeostasis in fully connected spiking networks to aid the unsupervised learning process. These methods ensure that all neurons have approximately equal firing activity across time and that their receptive fields are different, generally referred to as homeostatic behavior. While the adaptive threshold is straightforward to implement in spiking CNNs, adding in-hibitory neurons is not suitable to the convolutional architecture due to its shared weight nature. In this work, we first show that adaptive threshold in isolation is weak in obtaining approximate equal firing activity across activation maps in a spiking CNN. Next, we develop weight and offset decay mechanisms that enable the desired behavior to complement the STDP learning rule and adaptive threshold. We empirically show that these decay mechanisms improve feature learning as compared to baseline STDP in terms of accuracy (up to 1.4%) as well as enhanced homeostatic behavior among activation maps (more than halving the standard deviation). We discuss the complementary behavior of the decay mechanisms as compared to the adaptive threshold in terms of the variance in the activity induced. Finally, we show that when the convolutional features are trained on a subset of classes using STDP with decay mechanisms, the features learned are transferable to the subset of classes that are unseen to the convolutional layers. Thus, the decay mechanisms not only encourage the network to learn better features corresponding to the task being trained for but learn common structure prevalent among the classes while encouraging contribution from all activation maps. We perform experiments and present our findings on the Extended MNIST (EMNIST) dataset.

We present a digital implementation of the Spike Timing Dependent Plasticity (STDP) learning rule. The proposed digital implementation consists of an exponential decay generator array and a STDP adaptor array. On the arrival of a pre- and post-synaptic spike, the STDP adaptor will send a digital spike to the decay generator. The decay generator will then generate an exponential decay, which will be used by the STDP adaptor to perform the weight adaption. The exponential decay, which is computational expensive, is efficiently implemented by using a novel stochastic approach, which we analyse and characterise here. We use a time multiplexing approach to achieve 8192 (8k) virtual STDP adaptors and decay generators with only one physical implementation of each. We have validated our stochastic STDP approach with measurement results of a balanced excitation/inhibition experiment. Our stochastic approach is ideal for implementing the STDP learning rule in large-scale spiking neural networks running in real time.

Spike timing dependent plasticity (STDP) is found in various areas of the brain, visual cortex, hippocampus and hindbrain of electric fish, etc. The synaptic modification by STDP depends on time difference between presynaptic spike arrival and postsynaptic firing. If presynaptic neuron fires earlier than postsynaptic neuron dose, synaptic weight is strengthened. If postsynaptic neuron fires earlier than presynaptic neuron dose, synaptic weight is weakened. This learning rule is one example of various rules (hippocampal type). The learning rule of electric fish type is reversed. Changes of synaptic efficiency precisely depend on timing of pre- and postsynaptic spikes under STDP. Because of this precise dependence, STDP is thought to play the important role in temporal processing of stimuli. Temporal processing by STDP is well known. However, the role of STDP in spatial processing is not enough understood. To investigate spatial processing by STDP, We studied the role in spatial processing on interconnected network with STDP, using computer simulation. In this study, we found two type spatial filter by STDP on interconnected network. One is low pass filter using the learning rule of electric fish type. Another is edge-enhancing filter using the learning rule of hippocampus type. In this study, we calculated the response of interconnected neural network with STDP learning. The structure of the network is one-dimensional array. The neuron of the network has connection to neighbour neurons. In the case of learning rule of electric fish type, the network provided the function of low pass filter. The network was stimulated by direct current rectangularly distributed with noise. We examined two cases. In one case, synaptic modification did not occur (Fig ​(Fig1a).1a). In another case, synaptic modification occurred (Fig ​(Fig1b).1b). In the first case, Noise presented in response of the network and covered the rectangular form of stimulus. In the second case, noise was eliminated and the rectangular form of stimulus was represented. In the case of learning rule of hippocampal type, the network provided the function of edge-enhancing filter. The network was stimulated by direct current rectangularly distributed. The firing rate of the neuron that represents the edge of stimulus was larger than one of the others. This result shows that the network with hipocampal type STDP plays the role of edge-enhancing filter. These results show that interconnected network with STDP provides the function of spatial filter, and suggest that STDP would plays the important role in spatial processing. Figure 1 Response of network. Vertical and horizontal line indicates response of the network and neuronal number, respectively. (a) and (b) show the response of the network without and with STDP learning, respectively.

Genetically Increased Cell-Intrinsic Excitability Enhances Neuronal Integration into Adult Brain Circuits

A new pre-conditioned STDP rule and its hardware implementation in neuromorphic crossbar array

The hippocampus is thought to be involved in episodic memory in humans. Place cells of the rat hippocampus offer a potentially important model system to understand episodic memory. However, the difficulties in determining whether rats have episodic memory are profound. Progress can be made by considering the hippocampus as a computational device that presumably performs similar transformations on its inputs in both rats and in humans. Understanding the input/output transformations of rat place cells can thus inform research on the computational basis of human episodic memory. Two examples of different transformations in the CA3 and CA1 regions are presented. In one example, CA3 place fields are shown to maintain a greater degree of population coherence than CA1 place fields after a rearrangement of the salient landmarks in an environment, in agreement with computational models of CA3 as an autoassociative network. In the second example, CA3 place field appears to store information about the spatiotemporal sequences of place fields, starting with the first exposure to a cue-altered environment, whereas CA1 place fields store this information only on a temporary basis. Finally, recordings of hippocampal afferents from the lateral and medial entorhinal cortex (EC) suggest that these two regions convey fundamentally different representations to the hippocampus, with spatial information conveyed by the medial EC and nonspatial information conveyed by the lateral EC. The dentate gyrus and CA3 regions may create configural object+place (or item+context) representations that provide the spatiotemporal context of an episodic memory.

1. The effects of potassium channel blocking compounds on synaptic transmission in the CA1 and dentate gyrus regions of the rat hippocampus were examined by means of simultaneous field potential recording techniques in brain slices. 2. 4-Aminopyridine (4-AP) enhanced the excitatory postsynaptic potential (e.p.s.p.) and induced multiple population spike responses in both regions. EC50 values were 6.7 microM in the CAI (n = 5) and 161.7 microM (n = 5) in the dentate gyrus. 3. Tetraethylammonium (TEA) increased the amplitude and induced broadening of the population spike in both regions. In the dentate gyrus (n = 5) a single slow spike response was introduced (EC50 12.8 mM) and in the CA1 region (n = 5) the response was transformed into two wide spikes (EC50 2.6 mM). 4. In the CA1 region all of the dendrotoxins (toxin I, toxin K, alpha-Dtx and delta-Dtx) induced multiple population spikes and enlarged e.p.s.p. responses. Potentials recorded simultaneously in the dentate gyrus exhibited comparatively minor enhancements. The EC50 value for toxin 1 in the CA1 was calculated to be 237 nM (n = 4). Estimated EC50 values were obtained for alpha-Dtx (1.1 microM, n = 3), toxin K (411 nM, n = 4) and delta-Dtx (176 nM, n = 3). 5. In the presence of toxin 1, DL-2-amino-5-phosphonovaleric acid (APV) induced slight reduction of the late e.p.s.p. phase (n = 3). 6-Cyano-7-nitroquinoxaline-2,3-dione (CNQX) abolished all population spikes leaving a late slow positive waveform (n = 3). Co-application of APV and CNQX abolished all postsynaptic responses. 6. Charybdotoxin (CbTx) was significantly less potent than the dendrotoxins and had mixed actions in the CA1 region (n = 3). Again the dentate gyrus exhibited reduced sensitivity (n = 3). 7. In the presence of mast cell degranulating peptide (MCDP), enhancement of the CA1 field potential response (n = 5) was greater than that observed in the dentate gyrus (n = 5). 8. The results show that some potassium channel modulators can profoundly enhance CA1 region synaptic responses in the absence of notable changes in dentate gyrus excitability. Selective enhancement of defined synaptic pathways by potassium channel modulators may prove to have considerable therapeutic potential.

The plasticity of inhibitory interneurons (INs) plays an important role in the organization and maintenance of cortical microcircuits. Given the many different IN types, there is an even greater diversity in synapse-type-specific plasticity learning rules at excitatory to excitatory (E→I), I→E, and I→I synapses. I→I synapses play a key disinhibitory role in cortical circuits. Because they typically target other INs, vasoactive intestinal peptide (VIP) INs are often featured in I→I→E disinhibition, which upregulates activity in nearby excitatory neurons. VIP IN dysregulation may thus lead to neuropathologies such as epilepsy. In spite of the important activity regulatory role of VIP INs, their long-term plasticity has not been described. Therefore, we characterized the phenomenology of spike-timing-dependent plasticity (STDP) at inputs and outputs of genetically defined VIP INs. Using a combination of whole-cell recording, 2-photon microscopy, and optogenetics, we explored I→I STDP at layer 2/3 (L2/3) VIP IN outputs onto L5 Martinotti cells (MCs) and basket cells (BCs). We found that VIP IN→MC synapses underwent causal long-term depression (LTD) that was presynaptically expressed. VIP IN→BC connections, however, did not undergo any detectable plasticity. Conversely, using extracellular stimulation, we explored E→I STDP at inputs to VIP INs which revealed long-term potentiation (LTP) for both causal and acausal timings. Taken together, our results demonstrate that VIP INs possess synapse-type-specific learning rules at their inputs and outputs. This suggests the possibility of harnessing VIP IN long-term plasticity to control activity-related neuropathologies such as epilepsy.