Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
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The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
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The abstract of the disclosure is objected to because it exceeds 150 words. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Interpretation
Claim 22 cites a computer readable storage medium which the examiner interprets to not be a transitory signal. This is supported by the instant specification wherein it cites in para. [0239] “Moreover, while a computer storage medium is not a propagated signal,…”. This means that computer-readable storage is not a transitory signal.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-17, 19 and 22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Markram et al. (“Reconstruction and Simulation of Neocortical Microcircuitry” – hereinafter Markram).
In regards to claim 1, Markham disclose a method for constructing connections between nodes of an artificial recurrent neural network that mimics a target brain tissue, the method comprising: (Markham abstract and introduction on pages 456-457 cites “We present a first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat. The reconstruction uses cellular and synaptic organizing principles to algorithmically reconstruct detailed anatomy and physiology from sparse experimental data.”)
setting a total number of connections between the nodes in the artificial recurrent neural network; (Markham page 456 “Summary” cites “When digitally reconstructed neurons are positioned in the volume and synapse formation is restricted to biological bouton densities and numbers of synapses per connection, their overlapping arbors form ~8 million connections with ~37 million synapses.”)
setting a number of sub-connections in the artificial recurrent neural network, wherein a collection of sub-connections forms a single connection between different types of nodes; ((Markham page 456 “Summary” cites “When digitally reconstructed neurons are positioned in the volume and synapse formation is restricted to biological bouton densities and numbers of synapses per connection, their overlapping arbors form ~8 million connections with ~37 million synapses.” This teaches 37 million individual synaptic contact points (sub-connections) organized (formed) into 8 million neuron-to-neuron (node to nodes) connections. Also figure 6b shows multiple red circles (individual synapses = sub-connections) linking a presynaptic neuron (yellow) to a postsynaptic neuron (black). The red circles form the single connection between different types of nodes.)
setting a level of connectivity between the nodes in the artificial recurrent neural network; (Fig. 7 and the text teaches fig. 7b is a connection probability matrix and average connection probability with 100µm. The matrix quantifies the “level of connectivity” as it is the probability that a connection exists between each presynaptic and postsynaptic neuron type, thus a level of connectivity. Also see page 464 section “Digital Reconstruction of Connectivity” that cites “We developed an algorithmic approach to reconstruct synaptic connectivity between neurons in a companion … The approach is based on five rules of connectivity described in the Experimental Procedures and validated … We implemented these rules in four stages that yield plausible multi-synapse connections, consistent with the rules and constrained by experimental bouton densities (Figure 6A). The algorithm predicts the characteristics of multi-synapse connections between pairs of neurons that belong to specific m-types (Figure 6B).”. This algorithm determines which neuron pairs connect (level of connectivity) through the five connectivity rules and the four stage process.)
setting a direction of information transmission between the nodes in the artificial recurrent neural network; (Fig. 6b teaches pre- and post-synaptic m-type forming connections, and page 485 cites “Presynaptic neurons were stimulated with a set of previously described protocols (Gupta et al., 2000; Markram et al., 1998; Tsodyks and Markram, 1997; Wang et al., 2002, 2006). The synaptic parameters required to model the synapses were obtained by fitting the responses against the Tsodyks-Markram model for dynamic synaptic transmission (Fuhrmann et al., 2002; Tsodyks and Markram, 1997).”, which teaches pre-synaptic firing and getting a response from post-synaptic, thus presynaptic sends and post receives, thus defining the transmission direction.)
setting weights of the connections between the nodes in the artificial recurrent neural network; (Page 470 cites “Using the same method, we generated a first prediction of mean synaptic conductance for all 1,941 m-type-to-m-type connections (Figure 10D). Unique quantal synaptic conductance for individual synapses were drawn from truncated normal distributions around these means (Figure 10E; see Experimental Procedures).”, this teaches setting connection weights which are synaptic conductance. Also see figure 10D that has predicted conductance for connection types.)
setting response waveforms in the connections between the nodes, wherein the responses are induced by a single spike in a sending node; (Fig 10A and its text teaches waveform of response from spikes, wherein the gray trace is 30 separate PSP waveforms recorded, the blue trace is the average waveform, and each traces shows the rise, peak and decay, thus a visual waveform. Also, the waveform shape is set by the receptor kinetics AMPA and NMDA as the rise and decay time constants mathematically define the waveform shape, see page 14 “Parameterizing synaptic kinetics” which states “Excitatory synaptic transmission was modeled with both AMPA and NMDA receptor kinetics. For AMPA receptor (AMPAR) kinetics, the rise (τrisₑAMPA) and decay (τdₑcₐyAMPA) time constants were 0.2 ms and 1.74± 0.18 ms, respectively …”. This sets the waveform shape as it AMPA produces a fast sharp PSP waveform with a sharp peak and quick return to baseline, while the NMDA produces a slow prolonged waveform.)
setting transmission dynamics in the connections between the nodes, wherein the transmission dynamics characterize changing response amplitudes of an individual connections during a sequence of spikes from a sending node; (Page 467 “Reconstruction Synaptic Physiology” cites “To predict the physiology of the _36 million synapses in the reconstruction, we integrated published paired-recording data and reported synaptic properties (conductance, postsynaptic potentials [EPSPs/IPSPs], latencies, rise and decay times, failures, release probabilities, etc.; see Experimental Procedures and NMC portal). Neocortical synapses display known forms of short-term dynamics, which we used to classify synaptic connections as facilitating (E1 and I1), depressing (E2 and I2), or pseudo-linear (E3 and I3) s-types (Figures 9A and 9B)”. This teaches short-term plasticity and discloses 6 synapse types and different short-term plasticity properties: E1 and I1 are facilitating, wherein synaptic strength increases with repeated stimulation; E2 and I2 are depressing, thus synaptic strength decrease with repeated stimulation; and E3 and I3 are pseudo-linear which is relatively constant strength. This is also show in figure 9B.)
setting transmission probabilities in the connections between the nodes, wherein the transmission probabilities characterize a likelihood that a response is generated by the sub-connections that form a given connection given a spike in a sending neuron; and (Page 14 cites “Pathway specific values for the parameter “utilization of synaptic efficacy” (U, analogous to the probability of neurotransmitter release) were unified from various experimental studies of synaptic transmission…”, this teaches the U value is the transmission probability between connections to give a spike. Also see 9B that give U values for synapse types and see table S6 on page 33 that also give U values for synapse types.)
setting spontaneous transmission probabilities in the connections between the nodes. (Page 486 cites “Spontaneous miniature PSCs were modeled by implementing an independent Poisson process for each individual synapse that triggered release at rates (λspont) determined by the experimental data…” and page 15 “Spontaneous Synaptic Release” cites “Spontaneous miniature PSCs were modeled by implementing an independent Poisson process (of rate λspont) at each individual synapse to trigger release at low rates. The rates of spontaneous release for inhibitory and excitatory synapses were chosen to match experimental estimates(Ling and Benardo, 1999; Simkus and Stricker, 2002). The excitatory spontaneous rate was scaled up on a per layer basis to correct for missing extrinsic excitatory synapses. The resulting spontaneous release rates for unitary synapses were low enough (0.01Hz-0.6Hz) so as not to significantly depress individual synapse.”, both of with teaches setting spontaneous transmission probability.)
In regards to claim 2, Markham discloses the method of claim 1, wherein the total number of connections in the artificial recurrent neural network mimics a total number of synapses of a comparably sized portion of the target brain tissue. (Markham Page 464 cites “The individual reconstructions (Bio1-5) yield an average of 638 ± 74 million appositions and 36.7 ± 4.2 million synapses (27.0 ± 2.9 million excitatory and 9.7 ± 1.5 million inhibitory).” And validate against electron microscope study that cites “In a parallel electron microscopy study in which we determined average synapse density (0.63 ± 0.1/mm3; mean ± SD; n = 25) and calculated the number of synapses in a comparable volume of the neocortex, we obtained 182 ± 6 million synapses.”
In regards to claim 3, Markham discloses the method of claim 1, wherein the number of sub-connections mimics the number of synapses used to form single connections between different types of neurons in the target brain tissue. (Markham Fig. 7a cites “(A) Synapses per connection. A matrix of the average synapses per connection for multi-synapse connections formed between the 55 m-types (1,941 biologically viable connection types).”)
In regards to claim 4 Markham discloses the method of claim 1, wherein level of connectivity between the nodes in the artificial recurrent neural network mimics specific synaptic connectivity between the neurons of the target brain tissue. (Markham page 467 “Reconstruction Synaptic Physiology” section discloses the rules for specifying connectivity of which neurons connect to others. Also see figures 7B-C and text about connection probability matrix for all connection types.)
In regards to claim 5, Markham discloses the method of claim 1, wherein the direction of information transmission between the nodes in the artificial recurrent neural network mimics the directionality of synaptic transmission by synaptic connections of the target brain tissue. (Markham Fig. 6b teaches pre- and post-synaptic m-type forming connections, and page 485 cites “Presynaptic neurons were stimulated with a set of previously described protocols (Gupta et al., 2000; Markram et al., 1998; Tsodyks and Markram, 1997; Wang et al., 2002, 2006). The synaptic parameters required to model the synapses were obtained by fitting the responses against the Tsodyks-Markram model for dynamic synaptic transmission (Fuhrmann et al., 2002; Tsodyks and Markram, 1997).”, which teaches pre-synaptic firing and getting a response from post-synaptic, thus presynaptic sends and post receives, thus defining the transmission direction. Also figure 7C teaches inner ring segments, outputs (axons) and outer ring segments, inputs (dendrites), thus mimics the brain. )
In regards to claim 6, Markham discloses the method of claim 1, wherein a distribution of the weights of the connections between the nodes mimics weight distributions of synaptic connections between nodes in the target brain tissue. (Markham See Fig. 10 and Table S2 wherein it teaches excitatory conductance: 0.43-1.5ns; inhibitory conductance: 0.11-0.84ns and “synaptic conductance were adjusted until the silico PSPs matched experimental levels.”)
In regards to claim 7, Markham discloses the method of claim 1, wherein the method further comprises changing the weight of a selected of the connections between selected of the nodes. (Markham Page 467 “Reconstruction Synaptic Physiology” cites “To predict the physiology of the _36 million synapses in the reconstruction, we integrated published paired-recording data and reported synaptic properties (conductance, postsynaptic potentials [EPSPs/IPSPs], latencies, rise and decay times, failures, release probabilities, etc.; see Experimental Procedures and NMC portal). Neocortical synapses display known forms of short-term dynamics, which we used to classify synaptic connections as facilitating (E1 and I1), depressing (E2 and I2), or pseudo-linear (E3 and I3) s-types (Figures 9A and 9B)”. This teaches short-term plasticity and discloses 6 synapse types and different short-term plasticity properties: E1 and I1 are facilitating, wherein synaptic strength increases with repeated stimulation; E2 and I2 are depressing, thus synaptic strength decrease with repeated stimulation; and E3 and I3 are pseudo-linear which is relatively constant strength. This is also show in figure 9B. Thus weights (synaptic conductance) is changed.)
In regards to claim 8, Markham discloses the method of claim 1, wherein the method further comprises transiently shifting or changing the overall distribution of the weights of the connections between the nodes. (Markham Page 467 “Reconstruction Synaptic Physiology” cites “To predict the physiology of the _36 million synapses in the reconstruction, we integrated published paired-recording data and reported synaptic properties (conductance, postsynaptic potentials [EPSPs/IPSPs], latencies, rise and decay times, failures, release probabilities, etc.; see Experimental Procedures and NMC portal). Neocortical synapses display known forms of short-term dynamics, which we used to classify synaptic connections as facilitating (E1 and I1), depressing (E2 and I2), or pseudo-linear (E3 and I3) s-types (Figures 9A and 9B)”. This teaches short-term plasticity and discloses 6 synapse types and different short-term plasticity properties: E1 and I1 are facilitating, wherein synaptic strength increases with repeated stimulation; E2 and I2 are depressing, thus synaptic strength decrease with repeated stimulation; and E3 and I3 are pseudo-linear which is relatively constant strength. This is also show in figure 9B. Thus weights (synaptic conductance) is changed. Also, page 484 cites “the simulations further suggest that any mechanism that differentially changes the synaptic dynamics of different types of synapses (e.g., through neuromodulation; for reviews, see Lee and Dan, 2012; Zagha and McCormick, 2014) could alter the boundaries between activity regimes in complex ways.”, thus creating a distribution shift.)
In regards to claim 9, Markham discloses the method of claim 1, wherein the response waveforms mimics location-dependent shapes of synaptic responses generated in a corresponding type of neuron of the target brain tissue. (Markham Fig. 10A shows the waveform and Page 467 cites “The generalization power of these models has been demonstrated previously (Druckmann et al., 2011). As a further test, we compared dendritic attenuation of synaptic potentials in the models against past experiments (Berger et al., 2001; Nevian et al., 2007). While attenuation along basal dendrites (Figure S8; space constant, 40.0 ± 0.1 mm) was consistent with these results (Nevian et al., 2007), the reconstruction displayed stronger attenuation along apical dendrites (Figure S8; 174.3 ± 0.4 mm) than previously reported (273 mm; Berger et al., 2001).”, thus mimics the location dependent shape of synaptic responses.)
In regards to claim 10, Markham discloses the method of claim 1, wherein the method further comprises changing the response waveforms in a selected of the connections between selected of the nodes. (Markham Page 467 “Reconstruction Synaptic Physiology” cites “To predict the physiology of the _36 million synapses in the reconstruction, we integrated published paired-recording data and reported synaptic properties (conductance, postsynaptic potentials [EPSPs/IPSPs], latencies, rise and decay times, failures, release probabilities, etc.; see Experimental Procedures and NMC portal). Neocortical synapses display known forms of short-term dynamics, which we used to classify synaptic connections as facilitating (E1 and I1), depressing (E2 and I2), or pseudo-linear (E3 and I3) s-types (Figures 9A and 9B)”. This teaches short-term plasticity and discloses 6 synapse types and different short-term plasticity properties: E1 and I1 are facilitating, wherein synaptic strength increases with repeated stimulation; E2 and I2 are depressing, thus synaptic strength decrease with repeated stimulation; and E3 and I3 are pseudo-linear which is relatively constant strength. This is also show in figure 9B. Thus weights (synaptic conductance) is changed. As the conductance changes so would the waveforms.)
In regards to claim 11, Markham discloses the method of claim 1, wherein the method further comprises transiently changing a distribution of the response waveforms in the connections between the nodes. (Markham Page 467 “Reconstruction Synaptic Physiology” cites “To predict the physiology of the _36 million synapses in the reconstruction, we integrated published paired-recording data and reported synaptic properties (conductance, postsynaptic potentials [EPSPs/IPSPs], latencies, rise and decay times, failures, release probabilities, etc.; see Experimental Procedures and NMC portal). Neocortical synapses display known forms of short-term dynamics, which we used to classify synaptic connections as facilitating (E1 and I1), depressing (E2 and I2), or pseudo-linear (E3 and I3) s-types (Figures 9A and 9B)”. This teaches short-term plasticity and discloses 6 synapse types and different short-term plasticity properties: E1 and I1 are facilitating, wherein synaptic strength increases with repeated stimulation; E2 and I2 are depressing, thus synaptic strength decrease with repeated stimulation; and E3 and I3 are pseudo-linear which is relatively constant strength. This is also show in figure 9B. Thus weights (synaptic conductance) is changed. As the conductance changes so would the waveforms.)
In regards to claim 12, Markham discloses the method of claim 1, wherein the method further comprises changing the parameters of a function that determines the transmission dynamics in a selected of the connections between selected of the nodes. (Markham See Figure 9B and table S6 wherein the U (release probability): 0.016 to 0.5; D (depression time): 17-706ms and F (facilitation time): 17-803ms, and the Tsodyks-Markram model implement these changeable parameters.)
In regards to claim 13, Markham discloses the method of claim 1, wherein the method further comprises transiently changing a distribution of the parameters of functions that determine the transmission dynamics in the connections between the nodes. (Markham See page 467 and “reconstructing synaptic physiology” that teaches short-term plasticity that enables transiently changing distribution of parameters between the nodes)
In regards to claim 14, Markham discloses the method of claim 1, wherein the method further comprises changing a selected of the transmission probabilities in a selected of the connections between nodes. (Markham See page 467 and “reconstructing synaptic physiology” that teaches short-term plasticity that enables changing transmission probabilities by changing conductance)
In regards to claim 15, Markham discloses the method of claim 1, wherein the method further comprises transiently changing the transmission probabilities in the connections between nodes. (See Markham page 467 and “reconstructing synaptic physiology” that teaches short-term plasticity that enables transiently changing probabilism of transmission with E1/I1 and E2/I2 which are increased and decreased during spike trains respectively.)
In regards to claim 16, Markham discloses the method of claim 1, wherein the method further comprises changing a selected of the spontaneous transmission probabilities in a selected of the connections between nodes. (Markham Page 486 cites “Spontaneous miniature PSCs were modeled by implementing an independent Poisson process for each individual synapse that triggered release at rates (λspont) determined by the experimental data…” and page 15 “Spontaneous Synaptic Release” cites “Spontaneous miniature PSCs were modeled by implementing an independent Poisson process (of rate λspont) at each individual synapse to trigger release at low rates. The rates of spontaneous release for inhibitory and excitatory synapses were chosen to match experimental estimates(Ling and Benardo, 1999; Simkus and Stricker, 2002). The excitatory spontaneous rate was scaled up on a per layer basis to correct for missing extrinsic excitatory synapses. The resulting spontaneous release rates for unitary synapses were low enough (0.01Hz-0.6Hz) so as not to significantly depress individual synapse.”, both of with teaches setting spontaneous transmission probability.)
In regards to claim 17, Markham discloses the method of claim 1, wherein the method further comprises transiently changing the spontaneous transmission probabilities in the connections between nodes. (Markham Page 486 cites “Spontaneous miniature PSCs were modeled by implementing an independent Poisson process for each individual synapse that triggered release at rates (λspont) determined by the experimental data…” and page 15 “Spontaneous Synaptic Release” cites “Spontaneous miniature PSCs were modeled by implementing an independent Poisson process (of rate λspont) at each individual synapse to trigger release at low rates. The rates of spontaneous release for inhibitory and excitatory synapses were chosen to match experimental estimates(Ling and Benardo, 1999; Simkus and Stricker, 2002). The excitatory spontaneous rate was scaled up on a per layer basis to correct for missing extrinsic excitatory synapses. The resulting spontaneous release rates for unitary synapses were low enough (0.01Hz-0.6Hz) so as not to significantly depress individual synapse.”, both of with teaches setting spontaneous transmission probability. And page 467 section “Reconstructing Synaptic Physiology” teaches transiently changing transmission probabilities.)
In regards to claim 19, Markham discloses the method of improving a response of an artificial recurrent neural network, the method comprising:
training the artificial recurrent neural network to increase responses of the artificial recurrent neural network that comport with topological patterns of activity. (Markham page 480 cites “A similar analysis of the evoked response to thalamic stimulation of L5 neurons in the digital reconstruction found the same repeating triplet structures as observed in vivo (Figure 18A).”, these spike triplets are topological patterns because they represent structure between neurons, define temporal sequences of activity, and are patterns that emerge as result of the network architecture. Page 480 cites “A recent study showed that some neurons in a network display spiking activity that is tightly correlated with the average activity of the population of neurons in the network (choristers), while others display a diversity of spiking patterns whose correlation with that of the population is smaller than expected by chance (soloists), suggesting that they actively avoid correlating with the rest of the population”, this discloses a topological pattern also. Page 484-485 teaches “Modulation of cellular or synaptic physiology may therefore serve as mechanisms to dynamically reconfigure the network to satisfy different computational requirements.” And “A recent study has experimentally characterized the plasticity mechanisms for maintaining the network close to this transition.” Teaches modifying the network thought plasticity which is training.)
Claim 22 is the computer-readable storage medium embodiment of claim 1 with similar limitation and thus rejection using the same reasoning found in claim 1.
Conclusion
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/PAULINHO E SMITH/Primary Examiner, Art Unit 2127